The University Of British Columbia Irving K. Barber Faculty of Science
Okanagan B.Sc. Computer Science Honours April 2022 Computer Science

# Towards Parallel Learned Sorting

Ivan Carvalho

(2022)

###### Abstract

We introduce a new sorting algorithm that is the combination of ML-enhanced
sorting with the In-place Super Scalar Sample Sort ($\text{IPS}^{4}\text{o}$).
The main contribution of our work is to achieve parallel ML-enhanced sorting,
as previous algorithms were limited to sequential implementations. We
introduce the In-Place Parallel Learned Sort (IPLS) algorithm and compare it
extensively against other sorting approaches. IPLS combines the
$\text{IPS}^{4}\text{o}$ framework with linear models trained using the
Fastest Minimum Conflict Degree algorithm to partition data. The experimental
results do not crown IPLS as the fastest algorithm. However, they do show that
IPLS is competitive among its peers and that using the
$\text{IPS}^{4}\text{o}$ framework is a promising approach towards parallel
learned sorting.

Contents

toc

List of Figures

lof

##  Chapter 1 Acknowledgements

I would like to thank my supervisor, Dr. Ramon Lawrence, for all the support
while completing this work. Thanks for motivating me to do research in the
field of databases and Learned Indexes.

I would also like to thank my friends Matthew Currie, Sean Roarty, and Kathryn
Lecha. Thanks for listening to incomplete parts of this thesis, often when the
details were unclear and things went wrong.


2024/02/02 2024/04/04

Galaxy: center — ISM: clouds — ISM: molecules — ISM: radio lines — ISM:
general

# The CO-to-H2 Conversion Factor in the Central Molecular Zone of the Milky
Way using CO isotopologues

Mikito Kohno11affiliation: Astronomy Section, Nagoya City Science Museum,
2-17-1 Sakae, Naka-ku, Nagoya, Aichi 460-0008, Japan 22affiliation: Department
of Physics, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-
ku, Nagoya, Aichi 464-8602, Japan ∗ Yoshiaki Sofue33affiliation: Institute of
Astronomy, The University of Tokyo, 2-21-1 Osawa, Mitaka, Tokyo 181-0015,
Japan<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

We performed correlation analyses between the 12CO and 13CO $J=$1-0 line
intensities in order to derive the variability of the CO-to-H2 conversion
factor ($X_{\rm CO,{iso}}$) in the central molecular zone (CMZ) of our Galaxy.
New high-resolution $X_{\rm CO,{iso}}$ maps at a resolution of
$\sim\timeform{30"}$ and the longitude-velocity diagram (LVD) at resolution
$\sim\timeform{30"}\times 2$ km s-1 are presented using the 12CO and 13CO
archival survey data obtained by the Nobeyama 45 m telescope. We revealed the
variation of $X_{\rm CO,{iso}}$ in the CMZ within the range of $X_{\rm
CO,{iso}}\sim(0.2-1.3)\times 10^{20}\ {\rm cm^{-2}\ (K\ km\ s^{-1})^{-1}}$, if
we assume the normalization value of $0.59\times 10^{20}\ {\rm cm^{-2}\ (K\
km\ s^{-1})^{-1}}$. The mean value is obtained as $X_{\rm CO,{iso}}=(0.48\pm
0.15)\times 10^{20}\ {\rm cm^{-2}\ (K\ km\ s^{-1})^{-1}}$ in the CMZ of our
Galaxy.

##  1 Introduction

The Central Molecular Zone (hereafter CMZ) is the concentration of molecular
gas within $\sim 300$ pc in the Galactic Center (GC: [Morris & Serabyn (1996),
Henshaw et al. (2023)]). The total molecular mass in the CMZ has been derived
to be $\sim 2.3\times 10^{7}\ M_{\odot}$ assuming the CO-to-H2 conversion
factor of $X_{\rm CO}=0.51\times 10^{20}$ cm-2 (K km s-1)-1 (Sofue, 2022), but
it directly depends on the assumed value of $X_{\rm CO}$, which is still
uncertain in the GC (Dahmen et al., 1998). Hence, the mass, the most
fundamental parameter of the GC, is still uncertain by a factor of $\sim 2$ to
4. The conversion factor is defined by

$X_{\rm CO}={N({\rm H_{2}})_{\rm 12X}\over I_{\rm{}^{12}CO}}\ {\rm[cm^{-2}\
(K\ km\ s^{-1})^{-1}]},$ (1)

where $N({\rm H_{2}})_{\rm 12X}$ is the H2 column density and
$I_{\rm{}^{12}CO}$ is the integrated intensity of the 12CO $J=$1-0 line. The
standard value of the $X_{\rm CO}$ factor is $2\times 10^{20}$ ${\rm
cm^{-2}(K\ km\ s^{-1})^{-1}}$ with the uncertainty of $\pm 30\%$ in the
Galactic disk (Bolatto et al., 2013).

In the GC, $X_{\rm CO}$ is suggested to be significantly smaller than the
local value due to the higher metallicity (Arimoto et al., 1996), decreasing
by a factor of $\sim 3$–10 (Bolatto et al., 2013). Lower $X_{\rm CO}$ factors
have been also pointed out comparison of the CO luminosity with the $\gamma$
ray brightness (Blitz et al., 1985; Strong et al., 2004), analysis of the
infrared dust emission (Sodroski et al., 1995; Schultheis et al., 2014), and
Virial mass (Oka et al., 1998a).

Recently, Sofue & Kohno (2020, hereafter: Paper I) and Kohno & Sofue (2024,
hereafter: Paper II) revealed the variability of the $X_{\rm CO}$ factor in
the Galactic giant molecular clouds (GMC). These papers present the high-
resolution ($\sim 15\arcsec$) $X_{\rm CO}$ maps using CO isotopologues
obtained by the FUGIN CO survey data (Umemoto et al., 2017; Torii et al.,
2019).

In this paper, we aim first at deriving the $X_{\rm CO}$ variability in the GC
at higher accuracy and at presenting detailed distributions of $X_{\rm CO}$ in
longitude-latitude $(l,b)$ and longitude-velocity $(l,V_{\rm LSR})$ planes at
high angular and velocity resolutions using the CO-line data from the Nobeyama
45-m radio telescope.

##  2 Data

We used the high-resolution CO $J=$1-0 archival data of the Galactic center
obtained by the Nobeyama 45 m telescope (Tokuyama et al.,
2019)111https://www.nro.nao.ac.jp/~nro45mrt/html/results/data.html. The 12CO
and 13CO $J=$ 1-0 data were obtained by the multi-beam receiver of BEARS
(25-BEam Array Receiver System: Sunada et al. (2000); Yamaguchi et al. (2000))
and FOREST (FOur beam REceiver System on the 45-m Telescope Minamidani et al.
(2016); Nakajima et al. (2019)) installed in the Nobeyama 45 m telescope. The
full width half-maximum beam size is approximately 15” at 115 GHz and 110 GHz.
The intensity is calibrated to the radiation temperature ($T_{R}^{*}$: Kutner
& Ulich (1981)) comparing with the previous CO survey data observed by the
Nobeyama 45 m telescope (Oka et al., 1998b). Here, $T_{R}^{*}$ corresponds to
the brightness temperature $T_{\rm B}$ of a spatially resolved (extended)
source. The final data used in this paper has a grid spacing with
$(l,b,v)=(\timeform{7.5"},\timeform{7.5"},2\ {\rm km\ s^{-1}})$. We smoothed
the archival data to $\sim\timeform{30"}$ using the kernel Gaussian function
to improve the signal-to-noise ratio. The root-mean-square (r.m.s) noise
levels of the final cube data are $\sim 0.4$ K and $\sim 0.1$ K for 12CO and
13CO $J=$1-0, respectively.

##  3 Methods

In this study, we calculated the column densities of molecular hydrogen using
the integrated intensity of 12CO and 13CO $J=$1-0. The H2 column density is
taken from the 12CO integrated intensity by assuming the $X_{\rm CO}$ factor
from the equation (1). We used the conversion factor of ${X_{\rm
CO}}=0.59\times 10^{20}\ {\rm cm^{-2}\ (K\ km\ s^{-1})^{-1}}$ (Arimoto et al.,
1996) currently obtained for the Galactic Center region as the normalization
value in this article. This value has been obtained by extrapolating the
$X_{\rm CO}$ values determined in the disc to the GC by assuming a scale
radius of the Galactic disc, $r_{e}=6.2$ kpc, and the distance of the GC from
the Sun, $R_{0}=8.0$ kpc (Reid et al., 2019; VERA Collaboration et al., 2020).

Independent of the conversion factor, we derived the H2 column density in the
CMZ by the local thermal equilibrium (LTE) method described in Paper I, II,
and Pineda et al. (2008). The brightness temperature ($T_{\rm B}$) of the CO
line intensity with the excitation temperature ($T_{\rm ex}$) and the optical
depth ($\tau$) is given by

$T_{\rm B}=T_{0}\left(\frac{1}{e^{T_{0}/T_{\rm
ex}}-1}-\frac{1}{e^{T_{0}/T_{\rm bg}}-1}\right)\left(1-e^{-\tau}\right)\ [{\rm
K}],$ (2)

where $T_{\rm bg}=2.73$ K is the temperature of the cosmic-microwave
background radiation. $T_{0}=h\nu/k$ is the Planck temperature with $h$,
$\nu$, and $k$ being the Planck constant, rest frequency, and Boltzman
constant, respectively.

If we assume that the 12CO line is optically thick, the excitation temperature
is given by

$T_{\rm ex}=T_{0}^{115}\bigg{/}{\rm ln}\left(1+\frac{T_{0}^{115}}{T_{\rm
B}({\rm{}^{12}CO})_{\rm max}+{0.836}}\right)\ [{\rm K}],$ (3)

where $T_{\rm B}({\rm{}^{12}CO})_{\rm max}$ and $T_{0}^{115}={5.53}$ K
correspond to the 12CO peak brightness temperature and the Planck temperature
at the rest frequency of 12CO $J=$1-0, respectively. In the longitude-latitude
($l-b$) and longitude-velocity ($l-v$) space, $T_{\rm ex}$ at each pixel is
calculated using the peak value along the velocity-axis and latitude-axis,
respectively. We assume that $T_{\rm ex}$ is equal in the 12CO and 13CO line
emissions, and express the optical depth as

$\tau(^{13}{\rm CO})=-{\rm ln}\left(1-\frac{T_{\rm B}({\rm{}^{13}CO})_{\rm
max}/T_{0}^{110}}{(e^{T_{0}^{110}/T_{\rm ex}}-1)^{-1}-{0.168}}\right),$ (4)

where $T_{\rm B}({\rm{}^{13}CO})_{\rm max}$ and $T_{0}^{110}={5.29}$ K
represent the 13CO peak brightness temperature and the Planck temperature at
the rest frequency of 13CO $J=$1-0, respectively. The 13CO column density is
given by

$N_{\rm{}^{13}CO}=3.0\times
10^{14}~{}\frac{\tau}{1-e^{-\tau}}\frac{I_{\rm{}^{13}CO}}{1-e^{-T_{0}^{110}/T_{\rm
ex}}}\ [{\rm cm^{-2}}],$ (5)

where $I_{\rm{}^{13}CO}$ is the 13CO integrated intensity. Then, we convert
$N_{\rm{}^{13}CO}$ to the H2 column density using the abundance ratio of H2 to
13CO molecules given by

$N({\rm H_{2}})_{\rm 13L}=Y_{\rm GC^{13}CO}N_{\rm{}^{13}CO}\ [{\rm cm^{-2}}].$
(6)

Here, $Y_{\rm GC^{13}CO}$ is adopted as $\sim 1\times 10^{6}$ in Sagittarius
B2 (Lis & Goldsmith, 1989).

##  4 Results

Figure 1: (a) The integrated intensity map of 12CO $J=$1-0 obtained by the
Nobeyama 45 m telescope (Tokuyama et al., 2019). The integrated velocity range
is from $-219$ km s-1to 221 km s-1. (b) The longitude-velocity diagram of 12CO
at $b=0^{\circ}$. Arm I and Arm II are pointed in red dashed lines, that is
spiral arms around Sgr A (Sofue, 1995a). The lowest white contour level and
intervals of the panel are 2.0 and 4.0 K. The shadow area from $-60$ to $+20$
km s-1is contaminated by the foreground spiral arms (e.g., Oka et al. (2001);
Sofue (2006); Reid et al. (2016)). The red points show the positions of Sgr B2
and Sgr C. Figure 2: (a) Scatter plots between $N({\rm H_{2}})_{\rm 13L}$ and
$N({\rm H_{2}})_{\rm 12X}$ of the CMZ. Red points show the averaged values of
each bin, and the error bars are standard deviations of $N({\rm H_{2}})_{\rm
12X}$. Blue curves indicate the fitting results of scatter plots. The black
dashed lines indicate the linear relation of $N({\rm H_{2}})_{\rm 12X}=N({\rm
H_{2}})_{\rm 13L}$. (b) Same as (a), but for contaminated velocity range at
$-60\leq V_{\rm LSR}<+20$ km s-1are removed. The color bars show the number of
data points in a bin ($N_{\rm data}/{\rm bin}$).

###  4.1 CO spatial and velocity distributions in the CMZ

Figure 1 (a) shows the integrated intensity map of 12CO $J=$1-0. The
integrated velocity range is from $-219$ km s-1to $+221$ km s-1. 12CO has a
peak at the GMC complex of Sagittarius B2 (Sgr B2), Sagittarius C (Sgr C), and
$l=1^{\circ}.3$ complex. Molecular gas shows $\sim 50$ pc distributions of
each GMC complex. Figure 1(b) shows the longitude-velocity diagram (LVD) of
12CO $J=$ 1-0 cutting at $b=\timeform{0D}$. The red dashed lines show Arm I
and Arm II of the spiral arms around Sgr A (Sofue, 1995a). The CMZ is
distributed from $-200$ km s-1to $+200$ km s-1on the velocity space. We can
also find the Expanding Molecular Ring (EMR) with the non-circular gas motion
around the GC (Kaifu et al., 1972, 1974; Scoville, 1972; Sofue, 1995b). The
shadowed area at the velocity range from $-60$ km s-1to $+20$ km s-1is
contaminated by the GC’s foreground spiral arms (Oka et al., 1998b; Sofue,
2006).

Figure 3: (a) Spatial distributions of $X_{\rm CO,iso}$ in the CMZ. The map is
presented of $I_{\rm{}^{12}CO}>500$ K km s-1. (b) Same, but contaminated
velocity range at $-60\leq V_{\rm LSR}<+20$ km s-1are removed.

Fitting results and $<X_{\rm CO,{iso}}>$ taken from the correlation between
$N({\rm H_{2}})_{\rm 13L}$ and $N({\rm H_{2}})_{\rm 12X}$.  Name $l$ $b$
$V_{\rm LSR}$ $N_{\rm a}$ $N_{\rm b}$ $<X_{\rm CO,iso}>$ C.C [deg] [deg] [km
s-1] [$10^{23}$ cm-2] [$10^{23}$ cm-2] [$10^{20}$ ${\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$] (1) (2) (3) (4) (5) (6) (7) (8) CMZ (all) — — $-220$ – $+220$
2.2 5.7 $0.54\pm 0.16$ 0.89 CMZ[1] — — $-220$ – $-60$ 1.8 4.2 $0.48\pm 0.15$
0.87 $+20$ – $+220$ Sgr B2 $0.62$ $-0.05$ $+20$ – $+150$ 1.9 4.7 $0.54\pm
0.14$ 0.92 Sgr C $-0.55$ $-0.10$ $-90$ – $-30$ 0.46 1.1 $0.56\pm 0.16$ 0.87
$l=$1.3D 1.3 $-0.1$ $+20$ – $+150$ 1.5 2.3 $0.59\pm 0.11$ 0.92 [1] The velociy
components from $-60$ km s-1to $20$ km s-1were removed. Columns: (1) Region
names, (2) Galactic longitude, (3) Galactic latitude, (4) Integrated velocity
range, (5),(6) The fitting parameters of the non-linear relation. (7) The mean
value of $X_{\rm CO,{iso}}$ in the each region. (8) Correlation coefficient.
The errors of mean value are adopted as the standard deviation in all pixels.
$<X_{\rm CO,iso}>$ of the individual regions are derived by the data points of
$I_{\rm{}^{12}CO}>200$ K km s-1.

###  4.2 $X_{\rm CO,{iso}}$ maps

Figure 2 shows the scatter plots between $N({\rm H_{2}})_{\rm 13L}$ and
$N({\rm H_{2}})_{\rm 12X}$ in the CMZ. Black dashed line indicates the linear
relation of $N({\rm H_{2}})_{\rm 12X}=N({\rm H_{2}})_{\rm 13L}$. $N({\rm
H_{2}})_{\rm 12X}$ shows saturation at high $N({\rm H_{2}})_{\rm 13L}$, and
the nonlinear relation. Then, we carried out the empirical curve fitting using
the relation of

$N({\rm H_{2}})_{\rm 12X}={N_{\rm a}N({\rm H_{2}})_{\rm 13L}\over N({\rm
H_{2}})_{\rm 13L}+N_{\rm b}}\ [{\rm cm}^{-2}],$ (7)

where $N_{\rm a}$ and $N_{\rm b}$ [both in H2 cm-2 ] are fitting parameters of
the nonlinear relation having the same dimension as the column density. From
the equation 1 and 7, we calculated the $X_{\rm CO}$ factor (hereafter $X_{\rm
CO,iso}$) of each pixel using the following relation of

$X_{\rm CO,{iso}}=\left({N_{\rm a}N({\rm H_{2}})_{\rm 13L}\over N({\rm
H_{2}})_{\rm 13L}+N_{\rm b}}\right)\bigg{/}I_{\rm{}^{12}CO}\ {\rm[cm^{-2}(K\
km\ s^{-1})^{-1}]}.$ (8)

Figure 3(a) and 3(b) show the $X_{\rm CO,iso}$ maps of the CMZ with the
integrated velocity range from $-220$ to $+220$ km s-1and removed velocity
range from $-60$ to $+20$ km s-1, respectively. $X_{\rm CO,{iso}}$ in the GMZ
shows the variation of $(0.2-1.0)\times 10^{20}$ cm-2 (K km s-1)-1. We
obtained the mean value as $X_{\rm CO,{iso}}=(0.54\pm 0.16)\times 10^{20}$ and
$X_{\rm CO,{iso}}=(0.48\pm 0.15)\times 10^{20}$ ${\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$ in Figure 3(a) and 3(b), respectively. The mean $X_{\rm
CO,iso}$ removed from the contaminated components is lower than all integrated
velocity ranges. We also performed the curve fitting of the correlation
between $N_{\rm a}$ and $N_{\rm b}$ of the individual regions in the CMZ. As a
result, the mean $X_{\rm CO,iso}$ is obtained in Sgr B2, Sgr C, and the
$L=\timeform{1.3D}$ complex. Table 4.1 presents the fitting parameters and
$X_{\rm CO,iso}$ of our analysis.

###  4.3 The $X_{\rm CO,{iso}}$ longitude-velocity diagram

Figure 4: Scatter plots between $N({\rm H_{2}})_{\rm 13L}$ and $N({\rm
H_{2}})_{\rm 12X}$ on the longitude-velocity space at $b=\timeform{0.0D}$.
Black points show the $-220$ km s-1$<V_{\rm LSR}<-60$ km s-1and $+20$ km
s-1$<V_{\rm LSR}<+220$ km s-1component. Green points present the $-60$ km
s-1$<V_{\rm LSR}<+20$ km s-1component. Red points show the averaged values of
each bin, and the error bars are standard deviations of $N({\rm H_{2}})_{\rm
12X}$. The blue curve indicates the fitting result of the Galactic center
components. The black dashed lines indicate the linear relation of $N({\rm
H_{2}})_{\rm 12X}=N({\rm H_{2}})_{\rm 13L}$.

We made the $X_{\rm CO,iso}$ LVD of the CMZ at the Galactic plane using the
correlation of spectral column densities (SCD) obtained by the 12CO and 13CO
intensity described as Paper I and Paper II. The ${\rm SCD}_{\rm 12X}$ and
${\rm SCD}_{\rm 13L}$ is given by

$\displaystyle{\rm SCD}_{\rm 12X}$ $\displaystyle=$
$\displaystyle\frac{dN({\rm H_{2}})_{\rm 12X}}{dv}$ (9) $\displaystyle=$
$\displaystyle X_{\rm CO}T_{\rm B}({\rm{}^{12}CO})\ {\rm[cm^{-2}(km\
s^{-1})^{-1}]}$

and

$\displaystyle{\rm SCD}_{\rm 13L}$ $\displaystyle=$
$\displaystyle\frac{dN({\rm H_{2}})_{\rm 13L}}{dv}\ {\rm[cm^{-2}(km\
s^{-1})^{-1}]}$ (10) $\displaystyle=$ $\displaystyle 3.0\times
10^{14}~{}\frac{\tau}{1-e^{-\tau}}\frac{Y_{\rm{}^{13}CO}T_{\rm
B}({\rm{}^{13}CO})}{1-e^{-T_{0}^{110}/T_{\rm ex}}},$

respectively. ${\rm SCD}_{\rm 12X}$ and ${\rm SCD}_{\rm 13L}$ are the H2
column densities at assuming a normalization value of ${X_{\rm CO}}=0.59\times
10^{20}$ cm-2 (K km s-1)-1 (Arimoto et al., 1996) and $Y_{\rm
GC^{13}CO}=1\times 10^{6}$ (Lis & Goldsmith, 1989), respectively. Figure 4
shows scatter plots between ${\rm SCD}_{\rm 12X}$ and ${\rm SCD}_{\rm 13L}$ in
the longitude-velocity space at the Galactic plane ($b=\timeform{0.0D}$).
Scatter plots on the longitude-velocity space at the different latitudes are
presented in Figures 9a-14a of the Appendix. Green data points indicate from
$-60$ km s-1to $+20$ km s-1contaminated by the foreground disk components.
Black data points show the CMZ components with the removed from $-60$ km s-1to
$+20$ km s-1components. The SCD12X saturation level of the GC is higher than
contaminated components. We suggest that this result corresponds to the
difference of the 12CO and 13CO ratio between the CMZ and the Galactic disk
components reported in previous works (e.g., Figure 10 in Enokiya et al.
(2023)). Here, we performed the curve fitting to the GC components using the
function of

${\rm SCD}_{\rm 12X}={{\rm SCD}_{\rm a}{\rm SCD}_{\rm 13L}\over{\rm SCD}_{\rm
13L}+{\rm SCD}_{\rm b}}\ {\rm[cm^{-2}(km\ s^{-1})^{-1}]},$ (11)

where ${\rm SCD}_{\rm a}$ and ${\rm SCD}_{\rm b}$ correspond to the fitting
parameters. The result of fitting parameters are obtained as ${\rm SCD}_{\rm
a}=1.8\times 10^{21}\ {\rm cm^{-2}(km\ s^{-1})^{-1}}$ and ${\rm SCD}_{\rm
b}=2.5\times 10^{21}\ {\rm cm^{-2}(km\ s^{-1})^{-1}}$. Then, $X_{\rm
CO,{iso}}$ in the LVD is given by

$\displaystyle X_{\rm CO,{iso}}=\left({{\rm SCD}_{\rm a}{\rm SCD}_{\rm
13L}\over{\rm SCD}_{\rm 13L}+{\rm SCD}_{\rm b}}\right)\bigg{/}T_{\rm
B}({\rm{}^{12}CO})$ $\displaystyle{\rm[cm^{-2}(K\ km\ s^{-1})^{-1}]}$ . (12)

$X_{\rm CO,iso}$ on the LVD.  Name $V_{\rm LSR}$ $X_{\rm CO,iso}$ [km s-1]
[$10^{20}$ ${\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$] Arm I — $\sim 0.5$ – 1.0 Arm
II — $\sim 0.4$ – 0.8 EMR $\sim+150$ – $+200$ $\sim 0.2$ – 1.3 $\sim-200$ –
$-100$

Figure 5: Longitude-velocity distributions of $X_{\rm CO,{iso}}$ in the CMZ at
$b=\timeform{0D}$. The map is presented for $T_{\rm B}({\rm{}^{12}CO)}>1$ K.
The masked area shows the contaminated velocity range by the foreground disk
components.

Figure 5 shows the $X_{\rm CO,iso}$ map on the LVD at $b=\timeform{0.0D}$.
$X_{\rm CO,iso}$ maps on the LVDs at the different latitudes are presented in
Figures 9b-14b of the Appendix. The masked area presents the contaminated
velocity range from the foreground disk components. This velocity range exists
the CMZ gas and foreground disk gas in the same pixel of the data (see Figures
7 and 8 in the Appendix). In the optically thick 12CO line, the emission from
the CMZ is completely obscured and we only see the foreground gas. On the
other hand, in the optically thin 13CO line, we practically see the sum of the
CMZ emission and foreground emission. In such a situation, the 12CO and 13CO
lines trace totally different components. In this paper, we therefore focus on
the variation of $X_{\rm CO,iso}$ in the CMZ components, which are removed the
contaminated velocity range on the LVD. $X_{\rm CO,iso}$ has the variability
of $X_{\rm CO,iso}\sim(0.2-1.3)\times 10^{20}{\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$ in the CMZ component on the LVD. We summarize the $X_{\rm
CO,iso}$ values on the LVD of Arm I, Arm II, and EMR in Table 4.3.

##  5 Discussion

We investigated the radial variation of $X_{\rm CO,iso}$ based on the results
of Paper II. Figure 6 shows the plot of $X_{\rm CO,iso}$ as a function of the
Galactocentric distance. $X_{\rm CO,iso}$ of the GMCs in the Galactic disk
were obtained by Paper II assuming the normalization value of $X_{\rm
CO}=2\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$(Bolatto et al., 2013).
In this paper, we assumed the normalization in the GC of $X_{\rm
CO}=0.59\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$(Arimoto et al.,
1996). Since the obtained value is proportional to this normalization factor
if adopting a value of $2.0\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$
of local molecular clouds, the whole results in our analysis must be
multiplied by a factor of 3.39. The pink and yellow triangle in Figure 6 shows
$X_{\rm CO,iso}$ in the GC assuming the normalization $X_{\rm CO}$ of
$2.0\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$ and $0.59\times
10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$, respectively.

Figure 6: Radial gradient of $X_{\rm CO,iso}$ from the distance of the GC. The
pink and yellow triangle shows $X_{\rm CO,iso}$ in the GC assuming the
normalization $X_{\rm CO}$ of $2.0\times 10^{20}\ {\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$ and $0.59\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$,
respectively. The vertical error bars of the GC indicate the standard
deviation in the $X_{\rm CO,iso}$ map. Other symbols present the individual
GMCs of W51A, W33, N35-N36 complex, W49A, M17SW, G012.02-00.03, W43, and M16
obtained by Paper II. The blue line shows the fitting result to individual
GMCs and GC assuming the normalization $X_{\rm CO}=2\times 10^{20}\ {\rm
cm^{-2}(K\ km\ s^{-1})^{-1}}$ by an exponential function. The green dashed
line indicates the relation from Arimoto et al. (1996).

Comparison with the previous studies of the $X_{\rm CO}$ factor in the
Galactic Center region.  Method $X_{\rm CO}$ References $[10^{20}$ ${\rm
cm^{-2}(K\ km\ s^{-1})^{-1}}]$ Isotopologues 0.2–1.3† This study 0.7–4.4∗ This
study Virial 0.24 [1] Gamma-rays 0.3–0.6 [2] Extinction 0.3–0.6 [3] Dust
0.2–0.7 [4] Dust and HI $0.7\pm 0.1$ [5] We assumed the normalization $X_{\rm
CO}$ value of ${}^{\dagger}0.59\times 10^{20}$ ${\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$ and ${}^{*}2.0\times 10^{20}$ ${\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$. References [1] Oka et al. (1998a),[2]Ackermann et al. (2012)
[3] Schultheis et al. (2014),[4] Sodroski et al. (1995), [5] Torii et al.
(2010)

Finally, we compared $X_{\rm CO,iso}$ in the CMZ with previous studies
obtained by other methods to validate our assumption of the normalization
value. The $X_{\rm CO}$ in the GC were taken from comparing the CO luminosity
with the virial mass, gamma-ray brightness, extinction, infrared dust
emission, and HI emission. Table 5 summarizes the Galactic Center $X_{\rm CO}$
values. Previous studies of $X_{\rm CO}$ in the GC are in the range of
$(0.2-0.7)\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$, which is lower
than the standard value of $2\times 10^{20}\ {\rm cm^{-2}(K\ km\
s^{-1})^{-1}}$ in the Milky Way disk (Bolatto et al., 2013). Our results using
CO isotopologues assuming the normalization value of $X_{\rm CO}=0.59\times
10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$ shows the variation of $X_{\rm
CO,iso}\sim(0.2-1.3)\times 10^{20}\ {\rm cm^{-2}(K\ km\ s^{-1})^{-1}}$ in the
GC, and its value is consistent with these previous works. These points show
our assumption of the normalization $X_{\rm CO}$ of $0.59\times 10^{20}\ {\rm
cm^{-2}(K\ km\ s^{-1})^{-1}}$ based on Arimoto et al. (1996) is valid in this
paper, whereas the fitting result shown in the blue line is consistent with
the pink triangle obtained by the normaliation value of $2\times 10^{20}\ {\rm
cm^{-2}(K\ km\ s^{-1})^{-1}}$. This inconsistency might be explained by the
gradient between $X_{\rm CO,{iso}}$ in the CMZ and the Galactic disk component
is a discontinuous function of the Galactocentric distance. The absolute value
of $X_{\rm CO,{iso}}$ is influenced by the initially-assumed $X_{\rm CO}$,
while the relative variability of $X_{\rm CO,{iso}}$ is independet of the
normalization value. We point out that our assumption does not change the
difference of $X_{\rm CO,{iso}}$ on the longitude-latitude and longitude-
velocity space.

##  6 Summary

The conclusions of this paper are summarized as follows:

  1. 1.

We investigated the correlation between the 12CO and 13CO $J=$1-0 integrated
intensities and the brightness temperatures in the central molecular zone of
our Galaxy using the archival CO survey data obtained by the Nobeyama 45 m
telescope. The non-linear relation is revealed between them.

  2. 2.

We performed the curve fitting of the non-linear relation between the 12CO and
13CO $J=$1-0 intensities to derive the variability of the CO-to-H2 conversion
factor ($X_{\rm CO,{iso}}$) in the CMZ.

  3. 3.

The high-resolution $X_{\rm CO,{iso}}$ maps ($\sim\timeform{30"}$) and the
longitude-velocity diagram are presented. The $X_{\rm CO,{iso}}$ in the
Galactic center region shows the variability of $X_{\rm
CO,{iso}}\sim(0.2-1.3)\times 10^{20}\ {\rm cm^{-2}\ (K\ km\ s^{-1})^{-1}}$ if
we assume the normalization value of $X_{\rm CO}=0.59\times 10^{20}\ {\rm
cm^{-2}(K\ km\ s^{-1})^{-1}}$.

  4. 4.

We obtained $X_{\rm CO,{iso}}=(0.48\pm 0.15)\times 10^{20}$ ${\rm cm^{-2}}$
${\rm(K\ km\ s^{-1})^{-1}}$ as the mean value in the CMZ of our Galaxy.

## Acknowledgements

We are grateful to the anonymous referee for his/her thoughtful comments. The
Nobeyama 45-m radio telescope is operated by the Nobeyama Radio Observatory.
We utilized the Python software package for astronomy (Astropy Collaboration
et al., 2013). The authors are grateful to Prof. Tomoharu Oka and the radio
astronomy group of Keio University for the archival GC survey data using the
Nobeyama 45-m telescope.

## References

  * Ackermann et al. (2012) Ackermann, M., Ajello, M., Atwood, W. B., et al. 2012, ApJ, 750, 3
  * Arimoto et al. (1996) Arimoto, N., Sofue, Y., & Tsujimoto, T. 1996, PASJ, 48, 275
  * Astropy Collaboration et al. (2013) Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33
  * Blitz et al. (1985) Blitz, L., Bloemen, J. B. G. M., Hermsen, W., et al. 1985, A&A, 143, 267
  * Bolatto et al. (2013) Bolatto, A. D., Wolfire, M., & Leroy, A. K. 2013, ARA&A, 51, 207
  * Dahmen et al. (1998) Dahmen, G., Huttemeister, S., Wilson, T. L., et al. 1998, A&A, 331, 959
  * Enokiya et al. (2023) Enokiya, R., Sano, H., Filipović, M. D., et al. 2023, PASJ, 75, 970
  * Henshaw et al. (2023) Henshaw, J. D., Barnes, A. T., Battersby, C., et al. 2023, Protostars and Planets VII, 534, 83
  * Kaifu et al. (1972) Kaifu, N., Kato, T., & Iguchi, T. 1972, Nature Physical Science, 238, 105
  * Kaifu et al. (1974) Kaifu, N., Iguchi, T., & Kato, T. 1974, PASJ, 26, 117
  * Kohno & Sofue (2024) Kohno, M. & Sofue, Y. 2024, MNRAS, 527, 9290 (Paper II)
  * Kutner & Ulich (1981) Kutner, M. L. & Ulich, B. L. 1981, ApJ, 250, 341
  * Lis & Goldsmith (1989) Lis, D. C. & Goldsmith, P. F. 1989, ApJ, 337, 704
  * Minamidani et al. (2016) Minamidani, T., Nishimura, A., Miyamoto, Y., et al. 2016, Proc. SPIE, 99141Z
  * Morris & Serabyn (1996) Morris, M. & Serabyn, E. 1996, ARA&A, 34, 645
  * Nakajima et al. (2019) Nakajima, T., Inoue, H., Fujii, Y., et al. 2019, PASJ, 71, S17
  * Oka et al. (1998a) Oka, T., Hasegawa, T., Hayashi, M., et al. 1998a, ApJ, 493, 730
  * Oka et al. (1998b) Oka, T., Hasegawa, T., Sato, F., et al. 1998b, ApJS, 118, 455
  * Oka et al. (2001) Oka, T., Hasegawa, T., Sato, F., et al. 2001, ApJ, 562, 348
  * Pineda et al. (2008) Pineda, J. E., Caselli, P., & Goodman, A. A. 2008, ApJ, 679, 481
  * Reid et al. (2016) Reid, M. J., Dame, T. M., Menten, K. M., et al. 2016, ApJ, 823, 77
  * Reid et al. (2019) Reid, M. J., Menten, K. M., Brunthaler, A., et al. 2019, ApJ, 885, 131
  * Schultheis et al. (2014) Schultheis, M., Chen, B. Q., Jiang, B. W., et al. 2014, A&A, 566, A120
  * Scoville (1972) Scoville, N. Z. 1972, ApJ, 175, L127
  * Sodroski et al. (1995) Sodroski, T. J., Odegard, N., Dwek, E., et al. 1995, ApJ, 452, 262
  * Sofue & Kohno (2020) Sofue, Y. & Kohno, M. 2020, MNRAS, 497, 1851 (Paper I)
  * Sofue (1995a) Sofue, Y. 1995a, PASJ, 47, 527
  * Sofue (1995b) Sofue, Y. 1995b, PASJ, 47, 551
  * Sofue (2006) Sofue, Y. 2006, PASJ, 58, 335
  * Sofue (2022) Sofue, Y. 2022, MNRAS, 516, 907
  * Strong et al. (2004) Strong, A. W., Moskalenko, I. V., Reimer, O., et al. 2004, A&A, 422, L47
  * Sunada et al. (2000) Sunada, K., Yamaguchi, C., Nakai, N., et al. 2000, Proc. SPIE, 4015, 237
  * Tokuyama et al. (2019) Tokuyama, S., Oka, T., Takekawa, S., et al. 2019, PASJ, 71, S19
  * Torii et al. (2010) Torii, K., Kudo, N., Fujishita, M., et al. 2010, PASJ, 62, 1307
  * Torii et al. (2019) Torii, K., Fujita, S., Nishimura, A., et al. 2019, PASJ, 71, S2
  * Umemoto et al. (2017) Umemoto, T., Minamidani, T., Kuno, N., et al. 2017, PASJ, 69, 78
  * VERA Collaboration et al. (2020) VERA Collaboration, Hirota, T., Nagayama, T., et al. 2020, PASJ, 72, 50
  * Yamaguchi et al. (2000) Yamaguchi, C., Sunada, K., Iizuka, Y., et al. 2000, Proc. SPIE, 4015, 614

## Velocity channel maps of the contaminated velocity range

We present the 12CO and 13CO velocity channel maps contaminated by foreground
disk components in Figures 7 and 8, respectively. In this velocity range, the
CMZ and foreground disk gas components coexist in the same data pixel.

Figure 7: The 12CO $J=$1-0 channel map of the contaminated velocity range with
the CMZ and foreground disk components. Figure 8: Same as Figure 7, but for
13CO $J=$1-0.

## Scatter plots and $X_{\rm CO,iso}$ maps on the LVDs.

We show scatter plots of spectral column densities and $X_{\rm CO,iso}$ maps
on the LVDs from $b=\timeform{+0.30D}$ to $b=\timeform{-0.30D}$ in Figures 9
\- 14.

Figure 9: (a) Same as Figure 4, but for $b=\timeform{+0.30D}$ (b) Same as
Figure 5, but for $b=\timeform{+0.30D}$ Figure 10: (a) Same as Figure 4, but
for $b=\timeform{+0.20D}$ (b) Same as Figure 5, but for $b=\timeform{+0.20D}$
Figure 11: (a) Same as Figure 4, but for $b=\timeform{+0.10D}$ (b) Same as
Figure 5, but for $b=\timeform{+0.10D}$ Figure 12: (a) Same as Figure 4, but
for $b=\timeform{-0.10D}$ (b) Same as Figure 5, but for $b=\timeform{-0.10D}$
Figure 13: (a) Same as Figure 4, but for $b=\timeform{-0.20D}$ (b) Same as
Figure 5, but for $b=\timeform{-0.20D}$ Figure 14: (a) Same as Figure 4, but
for $b=\timeform{-0.30D}$ (b) Same as Figure 5, but for $b=\timeform{-0.30D}$


# EXACT-Net: EHR-guided lung tumor auto-segmentation for non-small cell lung
cancer radiotherapy

Hamed Hooshangnejad
Johns Hokins University
Baltimore, MD
<EMAIL_ADDRESS>Xue Feng
Carina Medical
Lexington, KY
<EMAIL_ADDRESS>Gaofeng Huang
Carina Medical
Lexington, KY
<EMAIL_ADDRESS>Rui Zhang
University of Minnesota
Minneapolis, MN
<EMAIL_ADDRESS>Quan Chen
Mayo Clinic
Phoenix, AZ
<EMAIL_ADDRESS>Kai Ding
Johns Hopkins University
Baltimore, MD
<EMAIL_ADDRESS>
###### Abstract

Lung cancer is a devastating disease with the highest mortality rate among
cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, which
accounts for 87% of diagnoses, require radiation therapy. Rapid treatment
initiation significantly increases the patient’s survival rate and reduces the
mortality rate. Accurate tumor segmentation is a critical step in the
diagnosis and treatment of NSCLC. Manual segmentation is time and labor-
consuming and causes delays in treatment initiation. Although many lung nodule
detection methods, including deep learning-based models, have been proposed,
there is still a long-standing problem of high false positives (FPs) with most
of these methods. Here, we developed an electronic health record (EHR) guided
lung tumor auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in
Tumor segmentation), where the extracted information from EHRs using a pre-
trained large language model (LLM), was used to remove the FPs and keep the TP
nodules only. The auto-segmentation model was trained on NSCLC patients’
computed tomography (CT), and the pre-trained LLM was used with the zero-shot
learning approach. Our approach resulted in a 250% boost in successful nodule
detection using the data from ten NSCLC patients treated in our institution.

##  1 Introduction

Lung cancer is the deadliest cancer type, with more than 238,000 new diagnoses
each year in the US [41]. Radiation therapy (RT) is the common and preferred
treatment modality for medically inoperable non-small cell lung cancer (NSCLC)
[6], accounting for 87% of lung cancer cases [43]. Over 60% of NSCLC
diagnoses, more than 142,000 patients, require RT at least once over the
course of their disease [15, 45]. However current RT workflow is time-
consuming and comprises numerous steps, resulting in a considerably long tie
to treatment initiation (TTI).

Previous studies have reported that lung cancer mortality rate increases with
prolonged TTI [14, 37, 21, 7, 16, 17] , not only increasing the chance of new
lymph node involvement, site of disease, and but also re-staging[32],
demonstrating that reducing the TTI is critical for patient survival and
treatment outcome.

Figure 1: The current radiotherapy workflow, consisting of multiple steps
including the target and organs at risk segmenation.

Tumor segmentation is a vital step in the diagnosis and treatment workflow.
Figure 1 shows the current radiotherapy workflow, which starts with patient
registration and assessment. The patient is then sent for radiology diagnostic
image acquisition and pathology tests. The radiologist and pathologist provide
their initial diagnosis based on the test as a report for the physician’s
evaluation. Based on the physician’s clinical evaluation and radiology and
pathology reports, a clinical assessment plan report is generated that not
only includes the diagnostic reports but also the concluding diagnosis. To
design the treatment plan a new patient’s CT scan is acquired and then the
pivotal step of target and organs at risk segmentation is carried out, which
is critical for radiotherapy plan optimization and effectiveness of treatment.
Treatment is delivered with image guidance and the treatment response is
assessed for potential amendment. If no further treatment is required, the
treatment will be concluded.

The main imaging modality used for diagnosis and RT treatment is Computed
Tomography (CT). Since these CT scans are very high quality with thin slices,
they are large volumetric scans with millions of voxels, making the diagnosis
and tumor/target segmentation challenging and time-consuming even for
radiation oncologists [25]. Given that manual segmentation is labor-intensive
and considerably time-consuming, an automated lung cancer nodule segmentation
method is extremely desirable.

Although there have been numerous efforts for automatic lung nodule
segmentation, a major and long-lasting issue with the automatic methods is
their high false positive (FP) rate. As a result, most algorithms are
comprised of nodule detection and FP reduction system[25, 13, 22, 51], or are
dealt with by manual cropping prior to nodule segmentation [51]. Pulmonary
blood vessels, lung borders, and noise from CT scanners may result in high
false-positive rates of detected nodules [49]. Different methods such as
classic machine learning, feature extraction [39], and deep learning (DL)[26]
algorithms have been used for FP reduction but there are still challenges with
these methods.

Figure 2: Overview of EHR-guided automated target segmentation system. The
auto-contouring platform contour the initial structures on the diagnostic CT
scan. Due to advantages of PET scan for improved target segmentation, it will
be used as the second primary imaging modality for target segmentation
platform. The NLP based algorithm will extract critical information regarding
the location and shape of tumor.

Meanwhile, information pinpointing the location of the lung tumor is already
available in the patient’s electronic health record (EHR) such as clinical
decisions outside the traditional simulation CT (such as reports from
diagnostic radiology scans and pathology reports). Still, little attention has
been paid to parsing and incorporating this data into target segmentation.
These reports have been meticulously written and contain valuable information
regarding the location, shape, and size of the tumor.

Recently, Large language models (LLMs) have shown remarkable performance in a
variety of natural language processing (NLP) tasks, such as summarization,
question, and answering[9]. Using neural networks with several billion
parameters, and self-surprised learning on a large corpus of unlabeled text
data, LLMs can very efficiently parse information and generate human-like
responses to prompts. OpenAI GPT3 and 4, Google Bard[40], and Meta LLAMA are
only a few examples of state-of-the-art LLMs.

ChatGPT is developed from InstructGPT[34], a fine-tuned version of GPT3, by
fine-tuning for dialog interface. While InstructGPT is designed for processing
prompts to provide responses to pre-defined tasks and instructions, ChatGPT is
designed to engage in conversion and create more naturalistic responses[34].
ChatGPT is trained on more than 300 billion words and has more than 100
trillion parameters, showing remarkable capability in generating and
collaborating with users for creative and technical downstream tasks. Recent
findings have shown that with in-context zero-shot and few-shot leanings, LLMs
can adapt to many novel downstream tasks [1, 23].

“Prompt Engineering” is an important aspect of zero-shot and few-shot
learning, which is best defined as providing effective contextual cues to help
the LLMs with the given task. More recently, LLMs have also been used to
produce clinical reports[38, 5] or, more focused, generate domain-specific
text, such as radiation oncology treatment regimens[28].

In this work, we demonstrate the feasibility of an EHR-guided lung tumor auto-
segmentation method (Figure 2). To deal with the critical issue of FPs in lung
nodule segmentation, LLM-parsed patient-specific EHR is used to aid the tumor
auto-segmentation method eliminate the FP nodules from the result. This is
because the main tumor to be treated can be one of the detected nodules and
not all lung nodules are the cancerous tumor. Thus, without relying on direct
user input, FPs were significantly reduced, and automatic target segmentation
was improved. Specifically, the LLM-extracted tumor location served as an
input bounding area for the CT images, to guide the segmentation network.

##  2 Related Work

Lung cancer is a devastating disease, and tumor segmentation is a critical
part of radiotherapy treatment practice, as initiating the treatment depends
on the tumor and organs at risk segmentation. Thus, the lung tumor
segmentation has been of much interest to many researchers over the years. The
first attempts for lung tumor segmentation were based on image characteristics
such as shape, intensity, and texture. These methods were computationally
expensive and hard to generalize to difficult cases and were mainly based on
traditional image processing methods such as intensity and adaptive
thresholding, image registration, and region growing [20, 46].

CT scan, amongst all the other imaging modalities, is the standard of care for
cancer therapy such as radiation therapy[8], the diagnostic CT scan, which is
acquired for tumor delineation, has the field of view that is bound to the
lungs to make lung parenchyma easily distinguishable. However, even with this
field of view tumor only accounts for a small number of voxels compared to the
entire CT, making lung tumor segmentation very challenging [30, 47].

Convolutional neural networks (CNNs) have been used in numerous medical image
processing fields, such as segmentation, classification, detection, and
reconstruction, and have achieved great success. CNNs have been applied to
lung CT scans for lung pathology segmentation [52], lung volume segmentation
[42], and lung region classification [52, 4]. In the case of entire lung
volume segmentation, there are two common approaches: (i) 2D segmentation, in
which each CT slice is segmented independently. In this case, the CNN
architectures such as UNet or VGG-16, are used to segment each slice of CT in
a 2D manner[4, 35, 36]. Generative adversarial networks (GAN) have also been
used for segmentation, where the GAN model is used to encode features of CT
slice and then the encoder-decoder is used to segment lung volume[2]; (ii) 3D
segmentation, in which the CT scan is treated as 3D volume, and is segmented
for lung in 3D. Previously, V-net and more improved networks like RU-Net, and
R2U-Net have been used for volumetric segmentation[31, 33, 48].

In the case of lung tumor segmentation, the common practice is a 2-step
framework in which a delineation model first segments all suspicious tumors
and an FP reduction model reduces the FPs. For instance, Xie H. et al.[48]
used a 2D Faster R-CNN model with deconvolutional layers to magnify the
feature maps to detect all the candidate nodules from slices of CT scan. Then,
they trained a classifier to reduce FPs. Another work used a different
architecture, ResNet[50], to segment tumors for NSCLC cases. For 3D tumor
segmentation, Kopelowits E., et al. [24] used MaskRCNN to detect 3D nodules on
CT scans, and Kamal U., et al.[19] used a Recurrent 3D-DenseUNet for lung
tumor segmentation. More recently, Le V. and Saut O. [25] used a variant of
the UNet model, RRc-UNet 3D, which is augmented with residual recurrent block,
for 3D segmentation of NSCLC tumors.

##  3 Methods and Materials

Here we have developed a 2-step framework for an EHR-guided tumor auto-
segmentation, where an LLM model extracts tumor location, tumor size, and
lymph node involvement from clinical reports (Figure 2) , and the parsed data
from LLM such as tumor location and size will be used to refine the result and
a robust target delineation.

###  3.1 LLM with Prompts for Tumor Phenotype Extraction

To demonstrate the feasibility of EHR-guided tumor segmentation, we used the
GPT 3.5 Turbo LLM through OpenAI API. We used an in-house Python interface to
the API to read the EHR text files and feed them to the model. To access the
API, an API key is required that can be obtained from OpenAI official website.

An important step is setting up the appropriate parameters for the LLM before
submitting the prompts. Especially, “Temperature” is an important parameter
determining the certainty vs creativity of the model. In our case, maximum
certainty is desirable, to which we used zero for the temperature.

To demonstrate the feasibility, the pathology lab and radiology image reports
and CT images of 10 cases of lung cancer patients treated with RT at Johns
Hopkins Hospital were used for this study. As, at the moment, we did not have
the resources to deploy the local model, we manually de-identified all the
patient data, removed the names and locations, and altered the dates of the
procedures and visits.

We implemented our novel strategy using the zero-shot learning approach
meaning that we have not fine-tuned the model for cancer reports. Thus, an
important step is “prompt engineering”, which is the careful design of the
prompts that work with ChatGPT to help and guide the pre-trained GPT3.5 model
in extracting the relevant information. A prompt is an instruction set that
regulates the LLM’s capabilities and affects the generated outputs. Prompts
not only can filter information but also create new interaction paradigms,
such as directing the LLM to search for relevant information that has not been
seen before.

Here, we also used prompts as a tool for LLM self-adaptation to new tasks. For
instance, we can extract the tumor location by using the prompts such as ”find
the current lung lobe that the determinate tumor/carcinoma/malignancy is
involving in this report:” or ”based on the report, where is the tumor or
carcinoma located? followed by possible options of right upper lobe (RUL),
right middle lobe (RML), right lower lobe (RLL), left upper lobe (LUL), left
lower lobe (LLL). Currently, in the medical domain automatically learned
prompts are less common as they are often not human-readable[29]. As
interpretably is essential, we manually engineered prompts with trial and
error.

Figure 3: Detailed tumor auto-segmentation model architecture

###  3.2 Tumor Auto-segmentation Algorithm Design and Training

To automatically detect and segment the lung nodules, we developed an
automated deep-learning model.

####  3.2.1 Architecture

We chose to employ the UNet3D architecture for our 3D segmentation networks
(Figure 3). This selection was based on the architecture’s well-established
effectiveness in segmenting 3D medical images[11, 27]. 2D object detection in
computer vision has significantly advanced and matured. When it comes to 3D
object detection in medical images, recent efforts have drawn inspiration from
the progress in 2D single-stage detectors, particularly RetinaNet[27], and
leveraged the successful 3D segmentation models, UNet3D, to construct a 3D
detection framework termed as Retina-UNet3D.

RetinaNet stands out due to its feature pyramid network (FPN), which
efficiently handles object detection across various scales. This FPN-based
approach serves as the fundamental building block for our 3D object detection
framework, providing a strong foundation for our work in the medical image
domain. In the context of 3D segmentation, the UNet3D encoder-decoder approach
has proven highly effective. Leveraging this success, Retina-UNet3D adopts the
UNet3D encoders and establishes connections between the encoder outputs and
the detection decoder heads as the UNet architecture also shares a similar
pyramid network design.

This design choice aligns with the principles of both FPN and UNet, allowing
us to leverage the benefits of feature pyramid structures for our 3D detection
framework while building upon the successful foundation of UNet3D in
segmentation tasks.

####  3.2.2 Loss Functions

In our segmentation tasks, we used a dual loss strategy:

  1. 1.

Categorical Cross-Entropy Loss: This loss is employed for pixel-level
classification, ensuring precise object boundary delineation and class-
specific region identification.

$L\left(p,q\right)=-\sum_{i}{y_{i}}{\mathrm{log}\left(p_{i}\right)\ }$ (1)

  2. 2.

Dice Loss: Dice loss is used for image-level Intersection over Union (IoU)
measurement, assessing overall segmentation quality.

$L\left(y,p\right)=1-\frac{2\sum_{i}{y_{i}p_{i}}}{\sum_{i}{y^{2}_{i}}+\sum_{i}{p^{2}_{i}}}$
(2)

These two losses are in same order of magnitude, so we simply added them as
the final dual loss. This approach stroked a balance between pixel-wise
accuracy and image-level fidelity, enhancing segmentation performance across
both detailed object recognition and holistic quality assessment.

In our detection tasks, we used smooth L1 loss[3] for box regression heads and
focal loss [12] for box classification heads. Hereby, we used $\delta$= 1. MAE
is the mean absolute error between predicted bounding boxes and ground-truth
bounding boxes.

$smoothL1=\left\\{\begin{array}[]{cc}\frac{0.5\
{\left(MAE\right)}^{2}}{\delta}\ \ \ \ \ if\ \ MAE<\delta\\\ MAE-0.5\ \delta\
\ \ \ \ \ \ otherwise\end{array}\right.$ (3)

$FL\left(p,y\right)=\left\\{\begin{array}[]{c}-\alpha{\left(1-p\right)}^{\gamma}{\mathrm{log}\left(p\right)\
}\ \ \ \ \ \ \ \ if\ y=1\\\
-\left(1-\alpha\right)p^{\gamma}{\mathrm{log}\left(1-p\right)\ if\
y=0}\end{array}\right.$ (4)

####  3.2.3 Dataset and Data Preprocessing

We used the publicly available Lung Image Database Consortium imaging
collection (LIDC-IDRI) from TCIA [44] as it contains high-quality annotations
for all nodules $>=3mm$ with consensus from experienced radiologists. With the
same LIDC-IDRI dataset, [10] annotated 50 CT scans on lobe segmentation.

We standardized the voxel spacing of all CT datasets to 1mm × 1mm × 1mm. In
addition, we applied intensity clipping, constraining the Hounsfield Units
(HU) within the range of -1000 HU to 600 HU. This adjustment was made to
prevent the model from overly emphasizing irrelevant information.

Furthermore, in preparation for training our detection model, data containing
segmentation contours were transformed into bounding boxes. This conversion
facilitated the training process and enabled effective detection.

####  3.2.4 Data Augmentation

For our segmentation task, we employed a comprehensive data augmentation
approach, which included 3D random rotations within a range of -30 to 30
degrees along each axis, random scaling between 70% and 140%, random cropping,
random flipping along each axis, random Gaussian noise, random Gaussian blur,
and random variations in brightness, contrast, and gamma.

For our detection task, we used the same augmentation methods as segmentation
but removing rotation and flipping approaches. This combined augmentation
strategy was instrumental in enhancing the robustness and generalization of
our models.

Figure 4: Showing an example case of EHR-guided tumor auto-segmentation where
the extracted information regarding the confirmed tumor is used to remove FPs.
The nodule detection algorithm detected and classified seven nodules as
malignant for this sample case (Case ID 4). Out of the seven nodules, three
were found in right inferior lobe (RIL) and two were found in left inferior
lobe (LIL) and two in left upper lobe (LUL). According to the EHR extracted
information, however, the confirmed tumors were in LUL. Using this
information, the result is automatically refined, and the FPs are removed.

####  3.2.5 Training and Evaluation Methods

Our physical device was equipped with a Nvidia RTX 4080 with 16GB of memory.
To address the considerable memory demands associated with 3D medical images,
we adopted a sliding patch method, enabling us to effectively train models on
large-sized images[18, 10] .

For the lung nodule detection model, training was conducted over 500,000
iterations, employing patch sizes of 96 × 96 × 96 (equivalent to 96mm × 96mm ×
96mm) with a batch size of 1. In the case of the lung nodule segmentation
model, it underwent training for 300,000 iterations, with patch sizes set at
64×64×64 (64mm × 64mm × 64mm) and a batch size of 1.

Similarly, the lobe segmentation model was trained for 300,000 iterations,
with patch sizes configured as 96 × 192 × 192 (equivalent to 288mm × 192mm ×
192mm, with 3-times down-sampling along the axial axis) and a batch size of 1.
These training settings were tailored to accommodate the hardware limitations
while ensuring efficient and effective model training.

In the first stage, we employed the lung nodule detection model to generate a
list of bounding box candidates for potential nodules. Subsequently, we
applied the lobe segmentation model to obtain lobe masks and assigned lobe
location information to each bounding box. Any candidate bounding box lacking
contact with a lobe was discarded from further consideration.

In the second stage, we cropped 64 × 64 × 64 patches for each remaining
candidate. These patches were then processed using the lung nodule
segmentation model to delineate the contours of the nodules. This two-stage
approach allows us to first identify potential nodules and then precisely
segment their contours.

We assessed the quality of segmentation results using the Dice Coefficient
Score (DSC), which provides a measure of the overlap between predicted and
ground-truth segmentation.

$DSC=\frac{|x\cap y|}{\left|x\right|+|y|}$ (5)

For our detection results, we employed the mean Average Precision (mAP) metric
with two Intersection over Union (IoU) thresholds at 0.5 and 0.7 for 3D
objects. An IoU of 0.5 signifies a good match for 3D small objects and an IoU
of 0.7 represents an excellent match. These IoU thresholds allow us to
precisely assess the performance of our detection model across varying levels
of accuracy and provide a comprehensive evaluation of its object detection
capabilities.

$mAP=\frac{1}{N}\sum^{N}_{c=1}{\frac{TP\left(c\right)}{TP\left(c\right)+FP(c)}}$
(6)

As we only had one class of detection object, mAP is equivalent to AP.

###  3.3 Experiment Design

We verified our approach and demonstrated the effect of EHR-extracted
information on the tumor segmentation model performance by conducting a
comparison study. We used data from 10 test patients from the Johns Hopkins
Hospital database, unseen by both the LLM and tumor auto-segmentation model.
Once, the nodules were detected and classified using the entire chest CT scan
without any EHR guidance. First, the entire body was automatically segmented
using the anonymized patients’ diagnostic CT scan for all organs including
lung lobes and lymph nodes. Then, auto lung nodule detection and
classification software detected and classified all supposedly malignant
nodules.

Next, using the EHR extracted information, we only presented the auto-
segmentation model with the confirmed location of tumor according to the
clinical reports. For instance, if reports mention that tumor is located in
“left upper lobe (LUL)”, the LUL mask was used to mask all other structures
and only the LUL was presented to the segmentation model for nodule detection.

##  4 Results

###  4.1 Tumor auto-segmentation performance

Table 1: Dice score of auto-segmentation method Lung Region | DSC
---|---
RUL | 0.94
RML | 0.83
RLL | 0.96
LUL | 0.98
LLL | 0.97
Lungs Overall | 0.97
Lung Nodule | 0.67

The evaluation of segmentation results using the Dice Coefficient Score (DSC)
revealed that lobe segmentation closely approximated the manual annotations
made by radiologists. This high level of performance in lobe segmentation
significantly reinforced the viability of our experiments, which focused on
tumor identification based on lobe-specific information obtained from
radiology reports.

In comparison to lobes and lungs, lung nodules are notably smaller in size.
It’s important to note that smaller regions tend to yield lower DSC scores
(Table 1), given the inherent challenges in segmenting small structures
accurately. Therefore, achieving a DSC score of 0.67 for lung nodule
segmentation can be considered a favorable outcome (Table 2).

Table 2: lung nodule detection performance |<EMAIL_ADDRESS>|<EMAIL_ADDRESS>
---|---|---
Lung Nodule | 65.63 | 59.15

The results were measured in the validation subset of LIDC-IDRI with 201 CTs
and 399 solid nodules. Our lung nodule detection model has demonstrated strong
performance in identifying solid nodules. However, it’s important to note
that, in this process, some false positives may arise, and there may be
challenges in detecting ground glass nodules.

###  4.2 LLM prompt design

To do so, first, as an “assistant prompt”, we gave a list of lung lobes as
options to direct pre-trained LLM to find the lobe with the tumor. Secondly,
we used the following as the “user prompt”: ”find the current lung lobe that
the determinate tumor/carcinoma/malignancy is involving in this report:”.

The are three keywords in this prompt to effectively guide pretrained LLM, (i)
current, as the patient may have a history of several lung tumors treatment,
it is important to specify the currently under treatment tumor; (ii)
determinate, the patient reports may include some indeterminate nodules which
we are not interested in for current treatment; (iii)
tumor/carcinoma/malignancy, we used the common words used in report to refer
to tumors in reports so that we do not miss any information regarding tumor
location.

Furthermore, to find the malignant lymph nodes, we specifically used the
pathology report with the following “user prompt”: ”find out what lymph
station/node are malignant in this report:”.

With the above-mentioned prompt, we could find the right tumor location with
100% accuracy, and it is important to consider that this is based on zero-shot
learning which means no fine-tuning has been done on the model. Again, we
could find the involved lymph nodes with 100% accuracy from ten reports.

###  4.3 EHR-guided Tumor Auto-segmentation Experiment

Table 3 summarizes the results for the ten test patients. As seen, when the
tumor segmentation algorithm is used without any EHR information, in only 20%
of patients the result matches the ground truth, and similar to previous
models, there is a false positive issue with initial nodule detection. On the
other hand, when the EHR extracted information is used to guide the tumor
detection, for 70% of the patients the patient the result matches the ground
truth. Thus, in other words, using medical report information resulted in a
huge increase in the number of successful tumor auto-segmentation cases 250%
boost in performance, demonstrating the power of our approach in reducing the
FPs.

Table 3: The EHR-guided tumor segmentation experiment result Case ID |  Ground Truth |  Detected Nodules |  Removed Nodules |  Matching Ground Truth
---|---|---|---|---
1 |  1 |  2 (FP) |  1 |  Yes
2 |  1 |  1 |  0 |  Yes
3 |  1 |  1 |  0 |  Yes
4 |  2 |  7 (FP) |  5 |  Yes
5 |  2 |  4 (FP) |  2 |  Yes
6 |  1 |  0 (FN) |  0 |  No
7 |  2 |  5 (FP) |  3 |  Yes
8 |  1 |  4 (FP) |  2 |  No (FP)
9 |  1 |  3 (FP) |  2 |  Yes
10 |  1 |  1 (FP) |  1 |  No

Figure 4 shows a sample case (Case ID 4), where the nodule detection algorithm
detected and classified seven nodules as malignant. Out of the seven nodules,
three were found in the right inferior lobe (RIL) and two were found in left
inferior lobe (LIL) and two in left upper lobe (LUL). According to the EHR
extracted information, however, the confirmed tumors were located in LUL.
Using this information, the result is automatically refined, and the FPs are
removed.

Figure 5: Case 6 where the nodule is very close to chest wall making the
nodule detection very challenging. Left showing the coronal view, and right
axial view, with tumor marked with red circle. Figure 6: Case 10, our approach
averted a false positive in right upper lobe, however, due to highly advanced
stage 4 cancer, the nodule detection failed to detect the advanced tumor shown
in red shade (left coronal view, right axial view)

##  5 Discussion

In this study, we presented a novel EHR-guided tumor auto-segmentation method.
We showed that incorporating the EHR information into segmentation, as a prior
knowledge of confirmed tumor location, is very effective in reducing the FPs.
We evaluated the method using the data from ten test cases of actual patients
from our institution database.

We are aware that our study may have a few limitations. First, as seen in
Table 3, we see two cases (6 and 10) with false negatives (FN), which is
highly undesirable. Upon further analysis, we realized that for case 6 the
reason that the segmentation model did not detect the nodule is that, (i) as
seen in Figure 5, the nodule is very close to the lung wall making the nodule
detection very challenging, which has also been reported by previous
works[25]; (ii) currently, our model is designed as a standalone nodule
detector, thus it is restrictive to detect fewer FPs, which resulted in some
true positives (TP) to be filtered out. With the EHR extracted information and
lowering the classifier threshold, the issue is resolved.

As for case 10 (Figure 6), the nodule detection method found an FP nodule in
the right upper lobe. According to the EHR report the tumor was located in the
left upper lobe, thus the FP was averted. However, case 10, particularly, is
highly advanced stage 4 and thus very challenging. As seen in Figure 6, the
tumor spread significantly and involved a large area of lymph nodes. Because
currently our model is not trained on these challenging cases, it failed to
detect the tumor.

##  6 Conclusion

In this study, we presented a novel method for enhancing the nodule detection
performance by augmenting the model with EHR-extracted information. EXACT-Net
uses a pre-trained LLM model to extract the clinically confirmed tumor
location and use that to filter our FPs. We used a zero-shot learning approach
by taking advantage of prompt engineering to extract the relevant information.
Thus, with EXACT-Net approach we could avert FPs which is the long-standing
problem of most nodule detection algorithms.

##  7 Acknowledgments

Research reported in this publication was supported by the National Institutes
of Health (award numbers XXX). The content is solely the responsibility of the
authors and does not necessarily represent the official views of the National
Institutes of Health.

## References

  * Agrawal et al. [2022] Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag.  Large language models are zero-shot clinical information extractors.  _arXiv preprint arXiv:2205.12689_ , 2022.
  * Alom et al. [2019] Md Zahangir Alom, Chris Yakopcic, Mahmudul Hasan, Tarek M Taha, and Vijayan K Asari.  Recurrent residual U-Net for medical image segmentation.  _Journal of Medical Imaging_ , 6(1):14006, 2019\.
  * Armato III et al. [2011] Samuel G Armato III, Geoffrey McLennan, Luc Bidaut, Michael F McNitt‐Gray, Charles R Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, and Eric A Hoffman.  The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans.  _Medical physics_ , 38(2):915–931, 2011.
  * Asuntha and Srinivasan [2020] A Asuntha and Andy Srinivasan.  Deep learning for lung Cancer detection and classification.  _Multimedia Tools and Applications_ , 79:7731–7762, 2020\.
  * Biswas [2023] Som Biswas.  ChatGPT and the future of medical writing, 2023.
  * Cao et al. [2019] Christopher Cao, Daniel Wang, Caroline Chung, David Tian, Andreas Rimner, James Huang, and David R Jones.  A systematic review and meta-analysis of stereotactic body radiation therapy versus surgery for patients with non–small cell lung cancer.  _The Journal of Thoracic and Cardiovascular Surgery_ , 157(1):362–373.e8, 2019.
  * Cushman et al. [2021] Taylor R Cushman, Bernard Jones, David Akhavan, Chad G Rusthoven, Vivek Verma, Ravi Salgia, Mina Sedrak, Erminia Massarelli, James W Welsh, and Arya Amini.  The Effects of Time to Treatment Initiation for Patients With Non–small-cell Lung Cancer in the United States.  _Clinical lung cancer_ , 22(1):e84–e97, 2021\.
  * Delbeke et al. [2006] Dominique Delbeke, R Edward Coleman, Milton J Guiberteau, Manuel L Brown, Henry D Royal, Barry A Siegel, David W Townsend, Lincoln L Berland, J Anthony Parker, and Karl Hubner.  Procedure guideline for tumor imaging with 18F-FDG PET/CT 1.0.  _Journal of nuclear Medicine_ , 47(5):885–895, 2006.
  * Fan et al. [2023] Lizhou Fan, Lingyao Li, Zihui Ma, Sanggyu Lee, Huizi Yu, and Libby Hemphill.  A bibliometric review of large language models research from 2017 to 2023\.  _arXiv preprint arXiv:2304.02020_ , 2023.
  * Feng et al. [2019] Xue Feng, Kun Qing, Nicholas J Tustison, Craig H Meyer, and Quan Chen.  Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.  _Medical Physics_ , 46(5):2169–2180, 2019.
  * Feng et al. [2020] Xue Feng, Nicholas J. Tustison, Sohil H. Patel, and Craig H. Meyer.  Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features.  _Frontiers in Computational Neuroscience_ , 14, 2020.
  * Girshick [2015] Ross Girshick.  Fast r-cnn.  In _Proceedings of the IEEE international conference on computer vision_ , pages 1440–1448, 2015.
  * Gruetzemacher et al. [2018] Ross Gruetzemacher, Ashish Gupta, and David Paradice.  3D deep learning for detecting pulmonary nodules in CT scans.  _Journal of the American Medical Informatics Association_ , 25(10):1301–1310, 2018.
  * Hanna et al. [2020] Timothy P Hanna, Will D King, Stephane Thibodeau, Matthew Jalink, Gregory A Paulin, Elizabeth Harvey-Jones, Dylan E O’Sullivan, Christopher M Booth, Richard Sullivan, and Ajay Aggarwal.  Mortality due to cancer treatment delay: systematic review and meta-analysis.  _BMJ_ , 371:m4087, 2020.
  * Herbst et al. [2018] Roy S Herbst, Daniel Morgensztern, and Chris Boshoff.  The biology and management of non-small cell lung cancer.  _Nature_ , 553(7689):446–454, 2018.
  * Hooshangnejad et al. [2023a] Hamed Hooshangnejad, Quan Chen, Xue Feng, Rui Zhang, and Kai Ding.  deepPERFECT: Novel Deep Learning CT Synthesis Method for Expeditious Pancreatic Cancer Radiotherapy.  _Cancers_ , 15(11):3061, 2023a.
  * Hooshangnejad et al. [2023b] Hamed Hooshangnejad, Quan Chen, Xue Feng, Rui Zhang, Reza Farjam, Khinh Ranh Voong, Russell K Hales, Yong Du, Xun Jia, and Kai Ding.  DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer.  _Frontiers in Oncology_ , 13:1201679, 2023b.
  * [18] F Isensee, PF Jaeger, SAA Kohl, J Petersen Nature …, and undefined 2021.  nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.  _nature.comF Isensee, PF Jaeger, SAA Kohl, J Petersen, KH Maier-HeinNature methods, 2021•nature.com_.
  * Kamal et al. [2020] Uday Kamal, Abdul Muntakim Rafi, Rakibul Hoque, Jonathan Wu, and Md Kamrul Hasan.  Lung cancer tumor region segmentation using recurrent 3d-denseunet.  In _Thoracic Image Analysis: Second International Workshop, TIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 2_ , pages 36–47. Springer, 2020.
  * Kenneth [1996] R C Kenneth.  _Digital image processing_.  Prentice Hall., 1996.
  * Khorana et al. [2019] Alok A Khorana, Katherine Tullio, Paul Elson, Nathan A Pennell, Stephen R Grobmyer, Matthew F Kalady, Daniel Raymond, Jame Abraham, Eric A Klein, R Matthew Walsh, Emily E Monteleone, Wei Wei, Brian Hobbs, and Brian J Bolwell.  Time to initial cancer treatment in the United States and association with survival over time: An observational study.  _PLOS ONE_ , 14(3):e0213209, 2019.
  * Khosravan and Bagci [2018] Naji Khosravan and Ulas Bagci.  Semi-supervised multi-task learning for lung cancer diagnosis.  In _2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC)_ , pages 710–713. IEEE, 2018\.
  * Kojima et al. [2022] Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa.  Large language models are zero-shot reasoners.  _Advances in neural information processing systems_ , 35:22199–22213, 2022.
  * Kopelowitz and Engelhard [2019] Evi Kopelowitz and Guy Engelhard.  Lung nodules detection and segmentation using 3D mask-RCNN.  _arXiv preprint arXiv:1907.07676_ , 2019.
  * Le and Saut [2023] Van-Linh Le and Olivier Saut.  RRc-UNet 3D for Lung Tumor Segmentation from CT Scans of Non-Small Cell Lung Cancer Patients.  In _Proceedings of the IEEE/CVF International Conference on Computer Vision_ , pages 2316–2325, 2023.
  * Li et al. [2022] Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, and Bingding Huang.  Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review, 2022.
  * Lin et al. [2017] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár.  Focal loss for dense object detection.  In _Proceedings of the IEEE international conference on computer vision_ , pages 2980–2988, 2017.
  * Liu et al. [2023a] Zhengliang Liu, Peilong Wang, Yiwei Li, Jason Holmes, Peng Shu, Lian Zhang, Chenbin Liu, Ninghao Liu, Dajiang Zhu, and Xiang Li.  RadOnc-GPT: A Large Language Model for Radiation Oncology.  _arXiv preprint arXiv:2309.10160_ , 2023a.
  * Liu et al. [2023b] Zhengliang Liu, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Wei Liu, Dinggang Shen, and Quanzheng Li.  Deid-gpt: Zero-shot medical text de-identification by gpt-4.  _arXiv preprint arXiv:2303.11032_ , 2023b.
  * Mercieca et al. [2021] Susan Mercieca, José S A Belderbos, and Marcel van Herk.  Challenges in the target volume definition of lung cancer radiotherapy.  _Translational lung cancer research_ , 10(4):1983, 2021.
  * Milletari et al. [2016] Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi.  V-net: Fully convolutional neural networks for volumetric medical image segmentation.  In _2016 fourth international conference on 3D vision (3DV)_ , pages 565–571. Ieee, 2016.
  * Mohammed et al. [2011] Nasiruddin Mohammed, Larry Llyn Kestin, Inga Siiner Grills, Madhu Battu, Dwight Lamar Fitch, Ching-yee Oliver Wong, Jeffrey Harold Margolis, Gary William Chmielewski, and Robert James Welsh.  Rapid Disease Progression With Delay in Treatment of Non–Small-Cell Lung Cancer.  _International Journal of Radiation Oncology*Biology*Physics_ , 79(2):466–472, 2011.
  * Negahdar et al. [2018] Mohammadreza Negahdar, David Beymer, and Tanveer Syeda-Mahmood.  Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network.  In _Medical Imaging 2018: Computer-Aided Diagnosis_ , pages 356–361. SPIE, 2018.
  * Ouyang et al. [2022] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, and Alex Ray.  Training language models to follow instructions with human feedback.  _Advances in Neural Information Processing Systems_ , 35:27730–27744, 2022.
  * Patil et al. [2020] Ravindra Patil, Leonard Wee, and Andre Dekker.  Auto Segmentation of Lung in Non-small Cell Lung Cancer Using Deep Convolution Neural Network.  In _Advances in Computing and Data Sciences: 4th International Conference, ICACDS 2020, Valletta, Malta, April 24–25, 2020, Revised Selected Papers 4_ , pages 340–351. Springer, 2020.
  * Pawar and Talbar [2021] Swati P Pawar and Sanjay N Talbar.  LungSeg-Net: Lung field segmentation using generative adversarial network.  _Biomedical Signal Processing and Control_ , 64:102296, 2021\.
  * Samson et al. [2015] Pamela Samson, Aalok Patel, Tasha Garrett, Traves Crabtree, Daniel Kreisel, A Sasha Krupnick, G Alexander Patterson, Stephen Broderick, Bryan F Meyers, and Varun Puri.  Effects of delayed surgical resection on short-term and long-term outcomes in clinical stage I non-small cell lung cancer.  _The Annals of thoracic surgery_ , 99(6):1906–1913, 2015.
  * Shachar [2022] Carmel Shachar.  HIPAA, privacy, and reproductive rights in a post-Roe era.  _JAMA_ , 328(5):417–418, 2022.
  * Shamas et al. [2023] Shazia Shamas, S N Panda, and Ishu Sharma.  Review on Lung Nodule Segmentation-Based Lung Cancer Classification Using Machine Learning Approaches BT - Artificial Intelligence on Medical Data.  pages 277–286, Singapore, 2023. Springer Nature Singapore.
  * Siad [2023] S M Siad.  The Promise and Perils of Google’s Bard for Scientific Research.  2023\.
  * Siegel et al. [2023] Rebecca L Siegel, Kimberly D Miller, Nikita Sandeep Wagle, and Ahmedin Jemal.  Cancer statistics, 2023.  _CA: A Cancer Journal for Clinicians_ , 73(1):17–48, 2023.
  * Skourt et al. [2018] Brahim Ait Skourt, Abdelhamid El Hassani, and Aicha Majda.  Lung CT image segmentation using deep neural networks.  _Procedia Computer Science_ , 127:109–113, 2018.
  * Tandberg et al. [2018] Daniel J Tandberg, Betty C Tong, Bradley G Ackerson, and Chris R Kelsey.  Surgery versus stereotactic body radiation therapy for stage I non–small cell lung cancer: A comprehensive review.  _Cancer_ , 124(4):667–678, 2018.
  * Tang et al. [2019] Hao Tang, Chupeng Zhang, and Xiaohui Xie.  Automatic pulmonary lobe segmentation using deep learning.  In _2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019)_ , pages 1225–1228. IEEE, 2019.
  * Tyldesley et al. [2001] Scott Tyldesley, Chris Boyd, Karleen Schulze, Hugh Walker, and William J Mackillop.  Estimating the need for radiotherapy for lung cancer: an evidence-based, epidemiologic approach.  _International Journal of Radiation Oncology* Biology* Physics_ , 49(4):973–985, 2001.
  * Uzelaltinbulat and Ugur [2017] Selin Uzelaltinbulat and Buse Ugur.  Lung tumor segmentation algorithm.  _Procedia computer science_ , 120:140–147, 2017.
  * Van Meerbeeck et al. [2011] Jan P Van Meerbeeck, Dean A Fennell, and Dirk K M De Ruysscher.  Small-cell lung cancer.  _The Lancet_ , 378(9804):1741–1755, 2011.
  * Xie et al. [2019] Hongtao Xie, Dongbao Yang, Nannan Sun, Zhineng Chen, and Yongdong Zhang.  Automated pulmonary nodule detection in CT images using deep convolutional neural networks.  _Pattern Recognition_ , 85:109–119, 2019.
  * Yu et al. [2023] Xuyao Yu, Lian He, Yuwen Wang, Yang Dong, Yongchun Song, Zhiyong Yuan, Ziye Yan, and Wei Wang.  A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT.  _Frontiers in Oncology_ , 13, 2023.
  * Zhang et al. [2020] Fuli Zhang, Qiusheng Wang, and Haipeng Li.  Automatic segmentation of the gross target volume in non-small cell lung cancer using a modified version of ResNet.  _Technology in Cancer Research & Treatment_, 19:1533033820947484, 2020.
  * Zhao et al. [2022] Dandan Zhao, Yang Liu, Hongpeng Yin, and Zhiqiang Wang.  A novel multi-scale CNNs for false positive reduction in pulmonary nodule detection.  _Expert Systems with Applications_ , 207:117652, 2022.
  * Zotin et al. [2019] Aleksandr Zotin, Yousif Hamad, Konstantin Simonov, and Mikhail Kurako.  Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks.  _Procedia Computer Science_ , 159:1439–1448, 2019.