Ci a ion: Jiménez-Sánchez, A.;
So iano-Redondo, M.E.;
Pe ei a-Cunill, J.L.; Ma ínez-O ega,
A.J.; Rod íguez-Mowb ay, J.R.;
Ramallo-Solís, I.M.; Ga cía-Luna, P.P.
A C oss-Sec ional Valida ion o Ho os
and Co eSlice So wa e P og ams o
Body Composi ion Analysis in
Abdominal Compu ed Tomog aphy
Scans in Colo ec al Cance Pa ien s.
Diagnos ics 2024,14, 1696.
h ps://doi.o g/10.3390/
diagnos ics14151696
Academic Edi o s: Takuji Tanaka,
Mandeep Ga g, Uma Debi, Nidhi
P abhaka and Ami Kuma Janu
Recei ed: 30 June 2024
Re ised: 21 July 2024
Accep ed: 3 Augus 2024
Published: 5 Augus 2024
Copy igh : © 2024 by he au ho s.
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons
A ibu ion (CC BY) license (h ps://
c ea i ecommons.o g/licenses/by/
4.0/).
diagnos ics
A icle
A C oss-Sec ional Valida ion o Ho os and Co eSlice So wa e
P og ams o Body Composi ion Analysis in Abdominal
Compu ed Tomog aphy Scans in Colo ec al Cance Pa ien s
And és Jiménez-Sánchez 1,*, Ma ía Elisa So iano-Redondo 2, JoséLuis Pe ei a-Cunill 1,* ,
An onio Jesús Ma ínez-O ega 1, JoséRamón Rod íguez-Mowb ay 3, I ene Ma ía Ramallo-Solís4
and Ped o Pablo Ga cía-Luna 1
1
Unidad de Ges ión Clínica de Endoc inología y Nu ición, Ins i u o de Biomedicina de Se illa, IBiS/Hospi al
Uni e si a io Vi gen del Rocío/CSIC/Uni e sidad de Se illa, A da. Manuel Siu o s/n, 41013 Se ille, Spain
2Unidad de Ges ión Clínica de Radiodiagnós ico, Hospi al Uni e si a io Vi gen del Rocío, A da. Manuel
Siu o s/n, 41013 Se ille, Spain
3Unidad de Ges ión Clínica de Oncología Médica, Hospi al Uni e si a io Vi gen del Rocío, A da. Manuel
Siu o s/n, 41013 Se ille, Spain
4Unidad de Ges ión Clínica de Ci ugía Gene al y del Apa a o Diges i o, Hospi al Uni e si a io Vi gen del
Rocío, A da. Manuel Siu o s/n, 41013 Se ille, Spain
*Co espondence: [email p o ec ed] (A.J.-S.); [email p o ec ed] (J.L.P.-C.)
Abs ac : Backg ound: Body composi ion assessmen using compu ed omog aphy (CT) scans may
be hampe ed by so wa e cos s. To acili a e i s implemen a ion in esou ce-limi ed se ings, wo open-
sou ce segmen a ion p og ams (Ho os and Co eSlice ) we e ans e sally alida ed in colo ec al
cance pa ien s. Me hods: Con as -enhanced abdominal CT scans we e analyzed ollowing he
Albe a p o ocol. The C oss-Sec ional A ea (CSA) and in ensi ies o skele al muscle issue (MT),
subcu aneous adipose issue (SAT), isce al adipose issue (VAT), and in amuscula adipose issue
(IMAT) we e measu ed. The Skele al Muscle Index (SMI) was calcula ed. Cu o poin s we e applied
o he SMI, MT in ensi y, and VAT CSA o de ine muscle a ophy, myos ea osis, and abdominal
obesi y. The in e -so wa e ag eemen was e alua ed using di e en s a is ical ools. Resul s: A o al
o 68 pa icipan s we e measu ed. The MT CSA and SMI displayed no di e ences. The MT CSA
ag eemen was excellen , and bo h p og ams p o ided equal muscle a ophy p e alences. Co eSlice
unde es ima ed he MT in ensi y, wi h a non-signi ican myos ea osis p e alence inc ease (+5.88%
and +8.82%) using wo di e en ope a i e de ini ions. Co eSlice o e es ima ed he CSA and in ensi y
in bo h VAT and SAT, wi h a non-signi ican inc ease (+2.94%) in he abdominal obesi y p e alence.
Conclusions: Bo h so wa e p og ams we e easible ools in he s udy g oup. The MT CSA showed
g ea in e -so wa e ag eemen and no muscle a ophy misdiagnosis. Segmen a ion di e ences in he
MT in ensi y and VAT CSA caused limi ed diagnos ic misclassi ica ion in he s udy sample.
Keywo ds: Albe a p o ocol; Ho os; Co eSlice ; compu ed omog aphy; body composi ion; muscle
mass; colo ec al cance ; sa copenia
1. In oduc ion
Compu ed omog aphy (CT) is conside ed he e e ence echnique o body compo-
si ion analysis in oncology [
1
], as i is an indi ec echnique wi h high spa ial esolu ion,
accu acy, and ep oducibili y [
2
]. Like magne ic esonance imaging (MRI), hese imaging
echniques can de e mine a y in il a ion in he muscle (myos ea osis) and measu e is-
ce al adipose issue (VAT). Logis ically, CT scans allow o oppo unis ic o e ospec i e
measu emen s in ou ine s udies eques ed in medical o su gical se ices o diagnos ic–
he apeu ic pu poses. As d awbacks, i is a high-cos and ionizing echnique, al hough
his could change wi h he in oduc ion o low- adia ion p o ocols o body composi ion
Diagnos ics 2024,14, 1696. h ps://doi.o g/10.3390/diagnos ics14151696 h ps://www.mdpi.com/jou nal/diagnos ics
Diagnos ics 2024,14, 1696 2 o 17
analysis. Measu emen s a e egional, ye whole-body es ima ion models a e a ailable [
3
,
4
].
Rega ding echnical e o , he p esence o in a enous con as [
5
], olume o e load [
6
],
slice hickness, and ube cu en [7,8] should be aken in o accoun .
Image segmen a ion allows o he quan i a i e measu emen o he C oss-Sec ional
A ea (CSA, usually exp essed in cm
2
) o issues in a egion o in e es (ROI). This p ocess
is based on he unique adia ion abso p ion o each issue, exp essed as he a enua ion
in ensi y in Houns ield Uni s (HU), and an adequa e loca ion o ana omical landma ks.
An a che ypical image o his ask is an axial slice loca ed in he hi d lumba e eb a
(L3), since i is he abdominal loca ion wi h he maximum indi idual ep esen a i eness
and in e indi idual a iabili y [
9
]. The ollowing issues can be segmen ed o body
composi ion analysis a his loca ion: muscle issue (MT), subcu aneous adipose issue
(SAT), VAT, and in amuscula adipose issue (IMAT).
Manual o semi-au oma ic image segmen a ion can be a labo -in ensi e and ope a o -
dependen p ocedu e. To o e come his ba ie , a i icial in elligence (AI)-based so wa e
p og ams o body composi ion analysis allow o ully au oma ed issue segmen a ion [
10
],
d ama ically speeding up his p ocess [
11
,
12
]. This opens he possibili y o pe o ming a 3D
analysis o he body composi ion, analyzing all he images in he s udy in a as and easible
way. These AI-based p og ams usually ha e es ic ed access and also ha e echnical
limi a ions: al hough hei pe o mance can be excellen , cases o e oneous segmen a ion
can s ill occu [11]. Fo he ime being, 2D and human-guided analyses s ill ha e a place.
In colo ec al cance pa ien s, he es ima ion o muscle a ophy using CT scans has
been shown o be an independen p edic o o e en s o in e es such as
su i al [1,13–17];
physical, cogni i e and social unc ionali y [
18
,
19
]; quali y o li e [
20
]; pos ope a i e com-
plica ions [
21
]; leng h o hospi al s ay [
22
,
23
]; and he need o in-hospi al ehabili a ion o
discha ge o a nu sing home [
24
]. Due o me hodological he e ogenei y, p e ious s udies in
colo ec al cance calcula ed a p e alence o muscle a ophy anging om 15 o 60% and
o myos ea osis om 19 o 78% [
25
]. Myos ea osis in CT scans has also been associa ed
wi h a educed su i al ime in diges i e malignancies [
15
–
17
,
26
–
28
], inc eased isk o
pos -su gical complica ions [
29
], and educed physical unc ion [
19
]. Rega ding adiposi y
and colo ec al cance , highe le els o SAT a baseline ha e been linked o a be e disease-
ee su i al [
30
]. Chemo he apy esponde s ha e also shown an inc ease in hei le els o
adiponec in a e ea men in compa ison wi h non- esponde s [
31
]. The ole o VAT may
be mo e complex and ime dependen : some s udies ha e linked a VAT excess a baseline
o lowe su i al [
32
] o mo e su gical complica ions [
33
], while o he s ha e ound no
impac [
30
], and some e idence poin s o a posi i e e ec o VAT inc ease a e su ge y [
34
].
Despi e i s p ognos ic alue, body composi ion image analysis in abdominal CT scans
is no ou inely conduc ed in many cen e s. To acili a e he implemen a ion o a semi-
au oma ic segmen a ion analysis in L3 axial images o CT scans in esou ce-limi ed se ings,
his s udy has compa ed he pe o mances o wo open-sou ce and use - iendly so wa e
p og ams o body composi ion analysis: a pic u e a chi ing and communica ion sys em
(PACS) iewe wi h semi-au oma ic HU-based h eshold segmen a ion (Ho os) and a web
b owse -based image analyze wi h au oma ic h eshold segmen a ion (Co eSlice ). I s
a ge popula ion is colo ec al cance due o he high p e alence o he disease and he
p e iously desc ibed impo ance o body composi ion analysis in his g oup.
2. Ma e ials and Me hods
2.1. S udy Design
The s udy design comp ised an analy ical obse a ional s udy ha was ca ied ou in
a single cen e (Hospi al Uni e si a io Vi gen del Rocío, Se ille, Spain). The measu emen
pe iod was om July 2022 o June 2024.
Rega ding he s udy sample, inclusion, exclusion, and wi hd awal c i e ia we e ap-
plied in colo ec al cance ou pa ien s as p e iously published [
35
]. Consecu i e sampling
was used. Rega ding he sample size, Lu e al.’s [
36
] me hodology was applied using he s a-
is ical package blandPowe (h ps:// d .io/gi hub/nwisn/blandPowe / /README.md,
Diagnos ics 2024,14, 1696 3 o 17
accessed on 22 June 2024) [
37
] in Rs udio so wa e ( e sion 2023.06.1+524) [
38
]. An a
p io i isk o ype I e o = 0.05 and a isk o ype II e o = 0.20 we e se o compa e he
muscle mass measu emen s p o ided by he wo so wa e p og ams o in e es (Ho os and
Co eSlice ) in a Bland–Al man analysis. We p ede e mined a maximal clinically accep able
di e ence be ween he so wa e p og ams: (
δ
) = 5%. A p elimina y s udy wi h a subse o
he s udy sample p oduced he ollowing esul s: he mean o di e ences be ween Ho os
and Co eSlice (
µ
) = 0.4 and he s anda d de ia ion o di e ences be ween Ho os and
Co eSlice (
σ
) = 1.8. These pa ame e s p o ided an es ima ed sample size o n= 68 pai s o
measu emen s.
This s udy was conduc ed in acco dance wi h he Decla a ion o Helsinki and ap-
p o ed by he E hics Commi ee “CEI de los Hospi ales Uni e si a ios Vi gen Maca ena y
Vi gen del Rocío” (p o ocol code: 1006-N-22; da e o app o al: 23 May 2022).
2.2. Da a Collec ion
2.2.1. Image Analysis
Abdominal CT scans we e eques ed by he Oncology Depa men o ou cen e
due o diagnos ic– he apeu ic easons. Bo h he Gene al Elec ic Re olu ion EVO (GE
Heal hCa e Technologies Inc., Chicago, IL, USA) and Toshiba Aquilion (Toshiba, Mina o,
Japan) scanne s we e used. Po o enous phase scans wi h a slice hickness o ei he 1.00 o
1.25 mm we e ob ained a e in a enous adminis a ion o a con as medium ollowing a
s anda dized acquisi ion p o ocol. The images we e e ospec i ely downloaded in Digi al
Imaging and Communica ion in Medicine (DICOM) o ma using ou local PACS se e
wi h Philips Vue PACS (Philips, Ams e dam, The Ne he lands). DICOM iles we e hen
anonymized using DICOM Anonymize 2.4.2 (h ps://www.dicomanonymize .com/
index.h ml, accessed on 22 June 2024).
Two so wa e p og ams we e used in his s udy, bo h capable o issue segmen a ion
based on in ensi y h esholds. Ho os is an open-sou ce code so wa e (FOSS) p og am ha
is dis ibu ed ee o cha ge unde he LGPL license a Ho osp ojec .o g and sponso ed
by Nimble Co LLC d/b/a Pu iew (Annapolis, MD, USA). Ho os is a 64-bi medical
image iewe o Mac OS X based upon Osi iX
TM
and o he open-sou ce medical imaging
lib a ies. I s 4.0.0RC4 e sion was used o his s udy (h ps://gi hub.com/ho osp ojec /
ho os/ eleases, accessed on 22 June 2024). Co eSlice [
39
] e sion 1.0 is a ee-o -cha ge,
web-based, CT scan segmen a o and is dis ibu ed unde an MIT license. I s 1.0 e sion
was accessed o his s udy (h ps://old.co eslice .com, accessed on 22 June 2024) wi h he
a ailable sou ce code (h ps://gi hub.com/louismullie/web-c -segmen a ion, accessed on
22 June 2024).
Co eSlice has demons a ed p ognos ic alue in ec al cance [
33
] and has unde gone
a ho ough alida ion p ocess [
39
]. Ne e heless, Ho os was selec ed as he gold-s anda d
so wa e in his s udy, as i has demons a ed he ollowing equi emen s: p ognos ic
capaci y in he a ge popula ion [
40
], eliabili y and accu acy in he measu emen o
myos ea osis [
41
], eliabili y and accu acy in he measu emen s o body composi ion
olume [
42
], and excellen in a- and in e -obse e ag eemen wi h i sel [
43
] and wi h
espec o e e ence so wa e [
42
]. Addi ionally, Ho os has a g aphical use in e ace
(GUI) simila o PACS iewe s commonly used in Radiology as well as a 3D olume ic
ende ing ea u e ha acili a es he iden i ica ion o s uc u es in pa ien s wi h ana omical
al e a ions [44].
All measu emen s we e simul aneously pe o med by a single ope a o on a Mac Mini
M1 wi h 16 GB o RAM (Apple Inc., Cupe ino, CA, USA) and an LG 32UN500P-W 31.5-inch
sc een wi h 4K esolu ion (LG Elec onics, Seoul, Republic o Ko ea). The iden i ica ion o
he L3 e eb a and issue segmen a ion in a selec ed axial slice we e pe o med in all cases
ollowing he Albe a p o ocol (TomoVision, Magog, QC, Canada, h ps:// omo ision.com/
Sa copenia_Help/index.h m, accessed on 22 June 2024). The ollowing HU h esholds we e
used o segmen VAT (
−
150 o
−
50 HU), SAT and IMAT (
−
190 o
−
30 HU), and MT (
−
29
o +150 HU). We p o ide an example o issue segmen a ion using bo h so wa e p og ams
Diagnos ics 2024,14, 1696 4 o 17
in Figu e 1. The Ho os “3D Volume Rende ing” unc ion was used o h ee-dimensionally
isualize he axial skele on i needed.
Diagnos ics 2024, 14, x FOR PEER REVIEW 4 o 17
All measu emen s we e simul aneously pe o med by a single ope a o on a Mac
Mini M1 wi h 16 GB o RAM (Apple Inc., Cupe ino, CA, USA) and an LG 32UN500P-W
31.5-inch sc een wi h 4K esolu ion (LG Elec onics, Seoul, Republic o Ko ea). The iden-
i ica ion o he L3 e eb a and issue segmen a ion in a selec ed axial slice we e pe -
o med in all cases ollowing he Albe a p o ocol (TomoVision, Magog, QC, Canada,
h ps:// omo ision.com/Sa copenia_Help/index.h m, accessed on 22 June 2024). The ol-
lowing HU h esholds we e used o segmen VAT (−150 o −50 HU), SAT and IMAT (−190
o −30 HU), and MT (−29 o +150 HU). We p o ide an example o issue segmen a ion
using bo h so wa e p og ams in Figu e 1. The Ho os “3D Volume Rende ing” unc ion
was used o h ee-dimensionally isualize he axial skele on i needed.
Figu e 1. Examples o issue segmen a ion using Ho os (le ) and Co eSlice ( igh ) in he same slice
a he hi d lumba e eb a in an abdominal CT scan. The ollowing issues we e segmen ed: mus-
cle issue (MT, ep esen ed in ed), subcu aneous adipose issue (SAT, ep esen ed in blue), isce al
adipose issue (VAT, ep esen ed in yellow), and in amuscula adipose issue (IMAT, ep esen ed
in pu ple).
All images we e isualized wi h an “Abdominal CT Scan” window in bo h so wa e
p og ams. In he case o Co eSlice , Ch ome e sion 126.0.6478.114 (Google LLC, Menlo
Pa k, CA, USA) was used. To inc ease he image size, Ch ome zoom was se a 125%, and
Co eSlice zoom was se a he maximum allowable alue. Analogous o an Vug e al.
[42], segmen a ion in Ho os was ini ially ca ied ou using he “G ow Region (2D/3D Seg-
men a ion)” unc ion o selec pixels acco ding o he in ensi y h esholds o he Albe a
p o ocol. In he case o Co eSlice , he ini ial segmen a ion was ca ied ou wi h he so -
wa e’s “Analyze Slice” unc ion, which includes he ollowing in ensi y h esholds: −190
o −30 HU o adipose issues (VAT and SAT) and −29 o 150 HU o MT. Co eSlice e sion
1.0’s buil -in algo i hm includes a median il e o denoising, a h eshold il e o edge
de ec ion, and a pe cen ile il e o edge smoo hing. We e e o Addi ional ile 1 o Mullie
e al. o u he in o ma ion [39].
The ini ial semi-au oma ic segmen a ion esul s in bo h p og ams we e la e manu-
ally edi ed by A.J.S. o ensu e he ana omical accu acy o he issues o in e es . S uc u es
e oneously included wi hin a issue o in e es (VAT, SAT, IMAT, o MT) due o HU sim-
ila i y we e deselec ed, and pixels o he issue o in e es ha would no ha e been in-
cluded in he ini ial analysis we e included. Fo his pu pose, he “B ush” ool was used
in Ho os, while he zoom was modi ied as needed wi h he “Magni y” ool. The “B ush”
ool in Ho os d ew o e ased pixels in he ROI independen ly o in ensi y. The “UCLA”
pale e wi hin he “Colo Look Up Table” in Ho os and mul iplana econs uc ion (MPR)
we e used on an ad hoc basis a he disc e ion o he esea che (A.J.S.) o imp o e he
iden i ica ion o s uc u es. In Co eSlice , he b ush ool (wi h buil -in in ensi y h esholds
Figu e 1. Examples o issue segmen a ion using Ho os (le ) and Co eSlice ( igh ) in he same slice
a he hi d lumba e eb a in an abdominal CT scan. The ollowing issues we e segmen ed: muscle
issue (MT, ep esen ed in ed), subcu aneous adipose issue (SAT, ep esen ed in blue), isce al
adipose issue (VAT, ep esen ed in yellow), and in amuscula adipose issue (IMAT, ep esen ed
in pu ple).
All images we e isualized wi h an “Abdominal CT Scan” window in bo h so wa e
p og ams. In he case o Co eSlice , Ch ome e sion 126.0.6478.114 (Google LLC, Menlo
Pa k, CA, USA) was used. To inc ease he image size, Ch ome zoom was se a 125%,
and Co eSlice zoom was se a he maximum allowable alue. Analogous o an Vug
e al. [
42
], segmen a ion in Ho os was ini ially ca ied ou using he “G ow Region (2D/3D
Segmen a ion)” unc ion o selec pixels acco ding o he in ensi y h esholds o he Albe a
p o ocol. In he case o Co eSlice , he ini ial segmen a ion was ca ied ou wi h he
so wa e’s “Analyze Slice” unc ion, which includes he ollowing in ensi y h esholds:
−
190 o
−
30 HU o adipose issues (VAT and SAT) and
−
29 o 150 HU o MT. Co eSlice
e sion 1.0’s buil -in algo i hm includes a median il e o denoising, a h eshold il e o
edge de ec ion, and a pe cen ile il e o edge smoo hing. We e e o Addi ional ile 1 o
Mullie e al. o u he in o ma ion [39].
The ini ial semi-au oma ic segmen a ion esul s in bo h p og ams we e la e manually
edi ed by A.J.S. o ensu e he ana omical accu acy o he issues o in e es . S uc u es
e oneously included wi hin a issue o in e es (VAT, SAT, IMAT, o MT) due o HU
simila i y we e deselec ed, and pixels o he issue o in e es ha would no ha e been
included in he ini ial analysis we e included. Fo his pu pose, he “B ush” ool was used
in Ho os, while he zoom was modi ied as needed wi h he “Magni y” ool. The “B ush”
ool in Ho os d ew o e ased pixels in he ROI independen ly o in ensi y. The “UCLA”
pale e wi hin he “Colo Look Up Table” in Ho os and mul iplana econs uc ion (MPR)
we e used on an ad hoc basis a he disc e ion o he esea che (A.J.S.) o imp o e he
iden i ica ion o s uc u es. In Co eSlice , he b ush ool (wi h buil -in in ensi y h esholds
depending on he issue o in e es ) was used o his ask. In his p og am, a new egion
called “IMAT” was c ea ed in he buil -in oolbox in he igh side o he sc een by selec ing
“THRESHOLD TYPE: Fa ” and hen clicking on he plus icon. Segmen a ions we e ca ied
ou simul aneously in bo h so wa e p og ams o maximize in a-ope a o epea abili y so
ha measu ed di e ences could be mainly a ibu able o in e -so wa e di e ences.
Segmen a ion colo s we e kep he same in bo h so wa e p og ams o acili a e a la e
e iew o high- esolu ion sc eensho s by a ce i ied adiologis (E.S.R.). I human-made
Diagnos ics 2024,14, 1696 5 o 17
e o s o inconsis encies we e de ec ed, he segmen a ion p ocess was epea ed, applying
he necessa y co ec ions. A e image analysis was comple ed, he nume ical alues o
he CSAs (cm
2
) and in ensi ies (HU) o MT, SAT, VAT, and IMAT we e la e egis e ed
o bo h p og ams on an Excel sp eadshee (Mic oso Co po a ion, Redmond, WA, USA).
The e o e, all issue segmen a ion p ocedu es we e blinded o hese nume ical alues. This
in o ma ion was displayed using “ROI In o” o he . oi ile o each segmen ed issue in
Ho os and he “measu emen s.cs ” ile esul ing om each Co eSlice segmen a ion.
2.2.2. Ope a i e De ini ions o Dynapenia, Muscle A ophy, Sa copenia,
and Visce al Obesi y
The diagnosis o muscle a ophy was based on he Skele al Muscle Index (SMI; cm2/m2),
which was ob ained om he measu ed MT CSA (cm2) using he o mula
SMI =(MT −CSA)/heigh 2
The diagnosis o myos ea osis was based on he measu ed MT in ensi y. The selec ed
cu o poin s o he diagnosis o bo h condi ions we e he p5 e e ence alues o a heal hy
popula ion published by Van Vug e al. [
45
], as well as he p ognos ic h esholds p o ided
by Dolan e al. in colo ec al cance [
46
]. To diagnose dynapenia, maximal handg ip s eng h
was de e mined as p e iously desc ibed [
35
] on he same day o he CT scan. In his s udy,
we used he maximal handg ip s eng h no ma i e alues de eloped by Dodds e al. [
47
],
and dynapenia was de ined wi h a pe cen ile-based app oach (maximal s eng h below
he co esponding 10 h pe cen ile based on age and sex) [
48
]. Sa copenia was de ined
as he conjunc ion o muscle a ophy and dynapenia using he EWGSOP-II c i e ia [
48
].
The diagnosis o isce al obesi y was based on he VAT CSA (cm2) using he cu o poin s
published by Doyle e al. [49].
2.2.3. Basic An h opome y P o ocol
The heigh and weigh we e measu ed on he same day o he CT scan ollowing
ESPEN guidance [
50
]. A desc ip ion o he ins umen s used o his ask is a ailable
elsewhe e [35].
2.2.4. Clinical Va iables and Cance S aging
Clinical a iables we e de ined and ob ained om digi ized heal h eco ds (“DIRAYA
Clinical S a ion”) as p e iously published [
35
], including in o ma ion abou su gical ea men .
2.2.5. Da a Quali y
All measu emen s we e ca ied ou by a single esea che wi h expe ience in body
composi ion analysis (A.J.S.). Image analysis was supe ised by a ce i ied adiologis
(E.S.R.) wi h ex ensi e expe ience in abdomen imaging. Cance s agings, ea men s, and
pe o mance sco es we e egis e ed in he da abase as eco ded by oncologis s (J.R.R.-M.) in
heal h eco ds. The ype o su ge y was egis e ed in he da abase as eco ded by su geons
(I.R.-S.) in heal h eco ds.
2.3. Da a Analysis
Fo he s a is ical analysis, he packages idy e se [
51
], cowplo [
52
], DescTools [
53
],
ggpub [
54
], and Rcmd [
55
] we e used in RS udio so wa e ( e sion 2023.06.1+524) [
38
].
No mali y was analyzed wi h he Shapi o–Wilk es . No mally dis ibu ed a iables
we e depic ed as he mean and s anda d de ia ion (SD), and non-no mally dis ibu ed
a iables we e desc ibed as he median and in e qua ile ange (IQR). Cen al endency
measu emen s o he CSA and in ensi y we e compa ed in all issues o in e es (MT, SAT,
VAT, and IMAT) depending on he so wa e p og am (Ho os s. Co eSlice ), bo h as aw

Diagnos ics 2024,14, 1696 6 o 17
measu emen s and ela i e di e ences (
∆
) o Co eSlice o Ho os, wi h he la e compu ed
as ollows:
∆=(Pa ame e (Co eSlice )−Pa ame e (Ho os))/Pa ame e (Ho os)
A - es (in he p esence o no mali y and homoscedas ici y) o a Wilcoxon signed- ank
es we e used o he wise. Simple co ela ion was calcula ed wi h he Pea son co ela ion
coe icien ( ). Accu acy and p ecision ega ding he CSA and in ensi y o each issue o
in e es (MT, SAT, VAT, and IMAT) in Ho os and Co eSlice we e de e mined using he
Bland–Al man analysis [
56
] and Lin’s Conco dance Co ela ion Coe icien (
ρ
), conside ing
alues > 0.99 as “nea pe ec ”, 0.95 o 0.99 as “subs an ial”, 0.90 o 0.98 as “mode a e”,
and < 0.90 as “poo ” [
57
]. Di e ences in he p e alence o sa copenia, myos ea osis, and
excess VAT we e compa ed using an X
2
es . Ca ego ical ag eemen in his ega d was
s udied using Cohen’s kappa [
58
]. Ou lie s we e no censo ed, and all measu emen s we e
included o s a is ical analysis. S a is ical signi icance was de e mined in all wo- ailed
es s as a p- alue < 0.05.
3. Resul s
3.1. Clinical and Demog aphical Desc ip ions o he S udy Sample
A o al o n= 68 pa icipan s we e measu ed and included o he analysis. Thei
clinical da a a e egis e ed in Table 1. The s udy pa icipan s we e mos ly a ec ed by
igh colon (n= 13) and sigmoid (n= 13) neoplasms. The modal TNM s age a diagnosis
was IIIB (n= 22). A as majo i y o pa icipan s had unde gone su ge y (n= 58). The
mos equen ype o su ge y was low an e io esec ion (n= 16), ollowed by igh hemi-
colec omy
(n= 15).
Only n= 17 pa icipan s we e unde ac i e chemo he apy a he ime o
measu emen . The sample modal ECOG sco e was 1. The modal BMI was no mal weigh ,
ollowed by o e weigh . No pa icipan displayed clinically e iden signs o olume
o e load.
Table 1. Clinical and demog aphic cha ac e is ics o he s udy sample.
Pa ame e Resul s
Sample size (ni)n= 68
Age (yea s) Me = 64.72
IQR = 12.67
Olde han 65 (ni)n= 32 (47.05%)
Female (ni)n= 31 (45.58%)
Neoplasm loca ion (ni)
Righ colon, n= 13
T ans e se colon, n= 4
Le colon, n= 6
Rec osigmoid, n= 5
Sigma, n= 13
Rec um, n= 27
S age (TNM) a diagnosis
IIA (n= 10); IIB (n= 2); IIC (n= 2)
IIIA (n= 4); IIIB (n= 22); IIIC (n= 9)
IVA (n= 9); IVB (n= 10); IVC (n= 0)
P e ious su ge y Yes, n= 58
No, n= 10
Fi s su ge y
Abdomino-pe ineal esec ion, n= 3
Colos omy, n= 2
Hepa ec omy, n= 3
Le hemi-colec omy, n= 8
Low an e io esec ion, n= 16
Diagnos ics 2024,14, 1696 7 o 17
Table 1. Con .
Pa ame e Resul s
Fi s su ge y
Righ hemi-colec omy, n= 15
Sigmoidec omy, n= 10
Sub- o al colec omy, n= 1
Ac i e chemo he apy (ni)Yes, n= 17
No, n= 51
ECOG (ni)0, n= 46
1, n= 22
Weigh (kg) Mean = 74.17
SD = 14.61
Heigh (m) Mean = 1.644
SD = 0.092
BMI (kg/m2)Me = 27.0
IQR = 4.6
BMI by g oup (ni)
Unde weigh , n= 4
No mal weigh , n= 20
O e weigh , n= 30
G ade 1 obesi y, n= 7
G ade 2 obesi y, n= 7
Clinical cha ac e is ics o he sample. Nume ical alues a e exp essed as absolu e equencies (n
i
), pe cen ages
(%), medians (Me), and in e qua ile anges (IQRs).
3.2. Image Analysis Cha ac e is ics
Rega ding imaging, n= 63 s udies we e unde aken using a GE Re olu ion EVO scan-
ne and n= 5 using a Toshiba Aquilion. All s udies used in a enous con as and had ei he
a 1.25 mm slice hickness (n= 59) o 1.00 mm slice hickness (n= 9). The image acquisi ion
pa ame e s we e as ollowing: ol age = 120 kV in all cases, and
ampe age = 5(3) mAs.
The
i s s udy was acqui ed on 11 July 2022 and he las on 25 Ap il 2023.
3.3. Compa isons o Tissue CSAs and Tissue In ensi ies be ween So wa e P og ams
When compa ing CSAs in he issues o in e es (MT, SAT, VAT, and IMAT) using bo h
so wa e p og ams (Co eSlice and Ho os), no signi ican di e ences we e ound in ei he
MT (130.682 s. 130.852 cm
2
) o IMAT (8.700 s. 8.317 cm
2
). Al hough he magni ude o
he di e ence in IMAT was small in absolu e e ms (0.187 cm
2
), i p o ed la ge in ela i e
e ms (+18.045%) due o i s small CSA. Rega ding he o he adipose issue compa men s,
s a is ically signi ican di e ences we e ound in bo h SAT (188.911 s. 187.352 cm
2
) and
VAT (182.990 s. 166.092 cm
2
). A de ailed desc ip ion o hese da a is in Table 2, wi h hei
g aphical desc ip ion in Figu e 2.
Table 2. Compa isons in he CSA (cm2) o he di e en issues using Ho os and Co eSlice .
Co eSlice Ho os Absolu e
Di e ences (cm2)∆(%) p-Value
MT 130.682
(48.583)
130.852
(48.260) −0.008 (2.334) −0.007 (1.882) 0.537
SAT 188.911
(128.498)
187.352
(131.454) 5.349 (6.844) +2.576 (4.702) 2.3 ×10−8
VAT 182.990
(152.620)
166.092
(147.960) 12.171 (8.815) +8.624 (5.591) 7.9 ×10−12
IMAT 8.700 (7.112) 8.317 (7.760) 0.187 (1.556) +18.045 (2.572) 0.08
CSA: C oss-Sec ional A ea;
∆
= (Co eSlice CSA
−
Ho os CSA)
÷
Ho os CSA; MT: muscle issue; SAT: subcu a-
neous adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose issue.
Diagnos ics 2024,14, 1696 8 o 17
Diagnos ics 2024, 14, x FOR PEER REVIEW 8 o 17
CSA: C oss-Sec ional A ea; ΔCSA = (Co eSlice CSA − Ho os CSA) ÷ Ho os CSA; MT: muscle issue;
SAT: subcu aneous adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose is-
sue.
Figu e 2. Compa isons o he CSA (C oss-Sec ional A ea) in cm2 o he diffe en issues o in e es
be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu aneous adipose
issue (SAT) (B); isce al adipose issue (VAT) (C); in amuscula adipose issue (IMAT) (D). The
signi icance o he pe o med Wilcoxon signed- ank es appea s as ei he “ns” (no signi ican ) o
wi h he ollowing symbols ep esen ing p- alues: * <0.05; ** <0.01; *** <0.001; **** <0.0001.
When compa ing he in ensi y in he issues o in e es (MT, SAT, VAT, and IMAT)
using bo h so wa e p og ams (Co eSlice and Ho os), all issues p esen ed signi ican di -
e ences: MT (33.237 s. 35.398 cm2), SAT (−103.945 s. −106.530 cm2), VAT (−87.868 s.
−92.723 cm2), and IMAT (−64.353 s. −65.272 cm2). A de ailed desc ip ion o hese da a is
in Table 3, wi h hei g aphical desc ip ion in Figu e 3.
Table 3. Compa isons o he in ensi y (HU) o he diffe en issues using Ho os and Co eSlice .
Co eSlice Ho os
Absolu e Diffe -
ences (HU) Δ (%) p-Value
MT 33.237 (12.038) 35.398 (11.421) −1.388 (2.016) −4.163 (5.226) 4.4 × 10−9
SAT −103.945 (9.417) −106.530 (8.177) 2.538 (2.695) −2.459 (2.688) 2.8 × 10−12
VAT −87.868 (11.788) −92.723 (10.624) 4.141 (3.824) −4.542 (4.255) 7.8 × 10−11
IMAT −64.353 (5.453) −65.272 (8.659) 0.368 (2.201) −0.537 (3.362) 0.026
HU: Houns ield Uni s; ΔHU = (Co eSlice HU − Ho os HU) ÷ Ho os HU; MT: muscle issue; SAT:
subcu aneous adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose issue.
ns
75
100
125
150
175
200
Co eSlice Ho os
Absolu e di e ences in MT CSA (cm²)
A
****
0
100
200
300
400
500
Co eSlice Ho os
Absolu e di e ences in SAT CSA (cm²)
B
****
0
100
200
300
400
500
Co eSlice Ho os
Absolu e di e ences in VAT CSA (cm²)
C
ns
0
10
20
30
40
Co eSlice Ho os
Absolu e di e ences in IMAT CSA (cm²)
D
****
20
30
40
50
60
Co eSlice Ho os
Absolu e di e ences in MT in ensi y (HU)
A
****
−100
−75
−50
Co eSlice Ho os
Absolu e di e ences in SAT in ensi y (HU)
B
****
−100
−80
−60
Co eSlice Ho os
Absolu e di e ences in VAT in ensi y (HU)
C
*
−80
−70
−60
−50
Co eSlice Ho os
Absolu e di e ences in IMAT in ensi y (HU)
D
Figu e 2. Compa isons o he CSA (C oss-Sec ional A ea) in cm
2
o he di e en issues o in e es
be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu aneous adipose
issue (SAT) (B); isce al adipose issue (VAT) (C); in amuscula adipose issue (IMAT) (D). The
signi icance o he pe o med Wilcoxon signed- ank es appea s as ei he “ns” (no signi ican ) o
wi h he ollowing symbols ep esen ing p- alues: **** < 0.0001.
When compa ing he in ensi y in he issues o in e es (MT, SAT, VAT, and IMAT)
using bo h so wa e p og ams (Co eSlice and Ho os), all issues p esen ed signi ican
di e ences: MT (33.237 s. 35.398 cm
2
), SAT (
−
103.945 s.
−
106.530 cm
2
), VAT (
−
87.868
s.
−
92.723 cm
2
), and IMAT (
−
64.353 s.
−
65.272 cm
2
). A de ailed desc ip ion o hese
da a is in Table 3, wi h hei g aphical desc ip ion in Figu e 3.
Diagnos ics 2024, 14, x FOR PEER REVIEW 8 o 17
CSA: C oss-Sec ional A ea; ΔCSA = (Co eSlice CSA − Ho os CSA) ÷ Ho os CSA; MT: muscle issue;
SAT: subcu aneous adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose is-
sue.
Figu e 2. Compa isons o he CSA (C oss-Sec ional A ea) in cm2 o he diffe en issues o in e es
be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu aneous adipose
issue (SAT) (B); isce al adipose issue (VAT) (C); in amuscula adipose issue (IMAT) (D). The
signi icance o he pe o med Wilcoxon signed- ank es appea s as ei he “ns” (no signi ican ) o
wi h he ollowing symbols ep esen ing p- alues: * <0.05; ** <0.01; *** <0.001; **** <0.0001.
When compa ing he in ensi y in he issues o in e es (MT, SAT, VAT, and IMAT)
using bo h so wa e p og ams (Co eSlice and Ho os), all issues p esen ed signi ican di -
e ences: MT (33.237 s. 35.398 cm2), SAT (−103.945 s. −106.530 cm2), VAT (−87.868 s.
−92.723 cm2), and IMAT (−64.353 s. −65.272 cm2). A de ailed desc ip ion o hese da a is
in Table 3, wi h hei g aphical desc ip ion in Figu e 3.
Table 3. Compa isons o he in ensi y (HU) o he diffe en issues using Ho os and Co eSlice .
Co eSlice Ho os
Absolu e Diffe -
ences (HU) Δ (%) p-Value
MT 33.237 (12.038) 35.398 (11.421) −1.388 (2.016) −4.163 (5.226) 4.4 × 10−9
SAT −103.945 (9.417) −106.530 (8.177) 2.538 (2.695) −2.459 (2.688) 2.8 × 10−12
VAT −87.868 (11.788) −92.723 (10.624) 4.141 (3.824) −4.542 (4.255) 7.8 × 10−11
IMAT −64.353 (5.453) −65.272 (8.659) 0.368 (2.201) −0.537 (3.362) 0.026
HU: Houns ield Uni s; ΔHU = (Co eSlice HU − Ho os HU) ÷ Ho os HU; MT: muscle issue; SAT:
subcu aneous adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose issue.
ns
75
100
125
150
175
200
Co eSlice Ho os
Absolu e di e ences in MT CSA (cm²)
A
****
0
100
200
300
400
500
Co eSlice Ho os
Absolu e di e ences in SAT CSA (cm²)
B
****
0
100
200
300
400
500
Co eSlice Ho os
Absolu e di e ences in VAT CSA (cm²)
C
ns
0
10
20
30
40
Co eSlice Ho os
Absolu e di e ences in IMAT CSA (cm²)
D
****
20
30
40
50
60
Co eSlice Ho os
Absolu e di e ences in MT in ensi y (HU)
A
****
−100
−75
−50
Co eSlice Ho os
Absolu e di e ences in SAT in ensi y (HU)
B
****
−100
−80
−60
Co eSlice Ho os
Absolu e di e ences in VAT in ensi y (HU)
C
*
−80
−70
−60
−50
Co eSlice Ho os
Absolu e di e ences in IMAT in ensi y (HU)
D
Figu e 3. Compa isons o in ensi y in HU (Houns ield Uni s) o he di e en issues o in e es
be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu aneous adipose
issue (SAT) (B); isce al adipose issue (VAT) (C); in amuscula adipose issue (IMAT) (D). The
signi icance o he pe o med Wilcoxon signed- ank es appea s as ei he “ns” (no signi ican ) o
wi h he ollowing symbols ep esen ing p- alues: * < 0.05; **** < 0.0001.
Diagnos ics 2024,14, 1696 9 o 17
Table 3. Compa isons o he in ensi y (HU) o he di e en issues using Ho os and Co eSlice .
Co eSlice Ho os Absolu e
Di e ences (HU) ∆(%) p-Value
MT 33.237 (12.038) 35.398 (11.421) −1.388 (2.016) −4.163 (5.226) 4.4 ×10−9
SAT −103.945
(9.417)
−106.530
(8.177) 2.538 (2.695) −2.459 (2.688) 2.8 ×10−12
VAT −87.868
(11.788)
−92.723
(10.624) 4.141 (3.824) −4.542 (4.255) 7.8 ×10−11
IMAT −
64.353 (5.453)
−
65.272 (8.659)
0.368 (2.201) −0.537 (3.362) 0.026
HU: Houns ield Uni s;
∆
= (Co eSlice HU
−
Ho os HU)
÷
Ho os HU; MT: muscle issue; SAT: subcu aneous
adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose issue.
3.4. Co ela ions o Tissue CSAs and Tissue In ensi ies be ween So wa e P og ams
Pea son co ela ion coe icien s o bo h he CSA and in ensi y in he di e en issues
o in e es be ween Ho os and Co eSlice a e desc ibed in Table 4. The co ela ion was
e y s ong o all pa ame e s, he s onges being he MT CSA ( = 0.998; IC95%: 0.997 o
0.998), SAT CSA ( = 0.998; IC95%: 0.997 o 0.999), and VAT CSA ( = 0.998; IC95%: 0.996 o
0.998). The IMAT in ensi y had he weakes co ela ion ( = 0.843; IC95%: 0.756 o 0.900).
The g aphical ep esen a ion o hese da a is depic ed in Figu e 4.
Table 4. Pea son co ela ion coe icien s ( ) o he CSA and in ensi y in he di e en issues using
Ho os and Co eSlice .
Measu ed Tissue CSA (cm2, 95% CI) In ensi y (HU, 95% CI)
MT 0.998 (0.997 o 0.998) 0.982 (0.971 o 0.989)
SAT 0.998 (0.997 o 0.999) 0.984 (0.975 o 0.990)
VAT 0.998 (0.996 o 0.998) 0.946 (0.914 o 0.966)
IMAT 0.985 (0.976 o 0.990) 0.843 (0.756 o 0.900)
95% CI: 95% con idence in e al; CSA: C oss-Sec ional A ea; MT: muscle issue; SAT: subcu aneous adipose issue;
VAT: isce al adipose issue; IMAT: in amuscula adipose issue. The 95% con idence in e als a e ep esen ed
in pa en hesis.
Diagnos ics 2024, 14, x FOR PEER REVIEW 9 o 17
Figu e 3. Compa isons o in ensi y in HU (Houns ield Uni s) o he diffe en issues o in e es
be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu aneous adipose
issue (SAT) (B); isce al adipose issue (VAT) (C); in amuscula adipose issue (IMAT) (D). The
signi icance o he pe o med Wilcoxon signed- ank es appea s as ei he “ns” (no signi ican ) o
wi h he ollowing symbols ep esen ing p- alues: * < 0.05; ** <0.01; *** <0.001; **** <0.0001.
3.4. Co ela ions o Tissue CSAs and Tissue In ensi ies be ween So wa e P og ams
Pea son co ela ion coefficien s o bo h he CSA and in ensi y in he diffe en issues
o in e es be ween Ho os and Co eSlice a e desc ibed in Table 4. The co ela ion was
e y s ong o all pa ame e s, he s onges being he MT CSA ( = 0.998; IC95%: 0.997 o
0.998), SAT CSA ( = 0.998; IC95%: 0.997 o 0.999), and VAT CSA ( = 0.998; IC95%: 0.996 o
0.998). The IMAT in ensi y had he weakes co ela ion ( = 0.843; IC95%: 0.756 o 0.900).
The g aphical ep esen a ion o hese da a is depic ed in Figu e 4.
Table 4. Pea son co ela ion coefficien s ( ) o he CSA and in ensi y in he diffe en issues using
Ho os and Co eSlice .
Measu ed Tissue CSA (cm2, 95% CI) In ensi y (HU, 95% CI)
MT 0.998 (0.997 o 0.998) 0.982 (0.971 o 0.989)
SAT 0.998 (0.997 o 0.999) 0.984 (0.975 o 0.990)
VAT 0.998 (0.996 o 0.998) 0.946 (0.914 o 0.966)
IMAT 0.985 (0.976 o 0.990) 0.843 (0.756 o 0.900)
95% CI: 95% con idence in e al; CSA: C oss-Sec ional A ea; MT: muscle issue; SAT: subcu aneous
adipose issue; VAT: isce al adipose issue; IMAT: in amuscula adipose issue. The 95% con i-
dence in e als a e ep esen ed in pa en hesis.
Figu e 4. Simple linea eg ession o he CSA (C oss-Sec ional A ea) in cm2 o he diffe en issues
o in e es be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu ane-
ous adipose issue (SAT) (B); isce al adipose issue (VAT) (C); in amuscula adipose issue (IMAT)
(D). Simple linea eg ession o he in ensi y in HU (Houns ield Uni s) o he diffe en issues o
in e es be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (E); subcu aneous
adipose issue (SAT) (F); isce al adipose issue (VAT) (G); in amuscula adipose issue (IMAT) (H).
In all cases, he pe ec bisec o o in e -so wa e eg ession is shown as a solid ed line, and he
75
100
125
150
175
100 125 150 175
MT CSA (cm²) using Co eSlice
MT CSA (cm²) using Ho os
A
0
100
200
300
400
500
0 100 200 300 400 500
SAT CSA (cm²) using Co eSlice
SAT CSA (cm²) using Ho os
B
0
100
200
300
400
0 100 200 300 400 500
VAT CSA (cm²) using Co eSlice
VAT CSA (cm²) using Ho os
C
0
10
20
30
40
0 10203040
IMAT CSA (cm²) using Co eSlice
IMAT CSA (cm²) using Ho os
D
20
30
40
50
20 30 40 50
MT in ensi y (HU) using Co eSlice
MT in ensi y (HU) using Ho os
E
−100
−80
−60
−100 −80 −60 −40
SAT in ensi y (HU) using Co eSlice
SAT in ensi y (HU) using Ho os
F
−100
−90
−80
−70
−100 −90 −80 −70 −60 −50
VAT in ensi y (HU) using Co eSlice
VAT in ensi y (HU) using Ho os
G
−80
−70
−60
−50
−70 −60
IMAT in ensi y (HU) using Co eSlice
IMAT in ensi y (HU) using Ho os
H
Figu e 4. Simple linea eg ession o he CSA (C oss-Sec ional A ea) in cm
2
o he di e en issues o
in e es be ween so wa e p og ams (Ho os and Co eSlice ): muscle issue (MT) (A); subcu aneous adipose
Diagnos ics 2024,14, 1696 16 o 17
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