A Cross-Sectional Validation of Horos and CoreSlicer Software Programs for Body Composition Analysis in Abdominal Computed Tomography Scans in Colorectal Cancer Patients

Citation: Jiménez-Sánchez, A.; Soriano-Redondo, M.E.; Pereira-Cunill, J.L.; Martínez-Ortega, A.J.; Rodríguez-Mowbray, J.R.; Ramallo-Solís, I.M.; García-Luna, P.P. A Cross-Sectional Validation of Horos and CoreSlicer Software Programs for Body Composition Analysis in Abdominal Computed Tomography Scans in Colorectal Cancer Patients. Diagnostics 2024,14, 1696. https://doi.org/10.3390/ diagnostics14151696 Academic Editors: Takuji Tanaka, Mandeep Garg, Uma Debi, Nidhi Prabhakar and Amit Kumar Janu Received: 30 June 2024 Revised: 21 July 2024 Accepted: 3 August 2024 Published: 5 August 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article A Cross-Sectional Validation of Horos and CoreSlicer Software Programs for Body Composition Analysis in Abdominal Computed Tomography Scans in Colorectal Cancer Patients Andrés Jiménez-Sánchez 1,*, María Elisa Soriano-Redondo 2, JoséLuis Pereira-Cunill 1,* , Antonio Jesús Martínez-Ortega 1, JoséRamón Rodríguez-Mowbray 3, Irene María Ramallo-Solís4 and Pedro Pablo García-Luna 1 1 Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Biomedicina de Sevilla, IBiS/Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Avda. Manuel Siurot s/n, 41013 Seville, Spain 2Unidad de Gestión Clínica de Radiodiagnóstico, Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n, 41013 Seville, Spain 3Unidad de Gestión Clínica de Oncología Médica, Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n, 41013 Seville, Spain 4Unidad de Gestión Clínica de Cirugía General y del Aparato Digestivo, Hospital Universitario Virgen del Rocío, Avda. Manuel Siurot s/n, 41013 Seville, Spain *Correspondence: [email protected] (A.J.-S.); [email protected] (J.L.P.-C.) Abstract: Background: Body composition assessment using computed tomography (CT) scans may be hampered by software costs. To facilitate its implementation in resource-limited settings, two opensource segmentation programs (Horos and CoreSlicer) were transversally validated in colorectal cancer patients. Methods: Contrast-enhanced abdominal CT scans were analyzed following the Alberta protocol. The Cross-Sectional Area (CSA) and intensities of skeletal muscle tissue (MT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intramuscular adipose tissue (IMAT) were measured. The Skeletal Muscle Index (SMI) was calculated. Cutoff points were applied to the SMI, MT intensity, and VAT CSA to define muscle atrophy, myosteatosis, and abdominal obesity. The inter-software agreement was evaluated using different statistical tools. Results: A total of 68 participants were measured. The MT CSA and SMI displayed no differences. The MT CSA agreement was excellent, and both programs provided equal muscle atrophy prevalences. CoreSlicer underestimated the MT intensity, with a non-significant myosteatosis prevalence increase (+5.88% and +8.82%) using two different operative definitions. CoreSlicer overestimated the CSA and intensity in both VAT and SAT, with a non-significant increase (+2.94%) in the abdominal obesity prevalence. Conclusions: Both software programs were feasible tools in the study group. The MT CSA showed great inter-software agreement and no muscle atrophy misdiagnosis. Segmentation differences in the MT intensity and VAT CSA caused limited diagnostic misclassification in the study sample. Keywords: Alberta protocol; Horos; CoreSlicer; computed tomography; body composition; muscle mass; colorectal cancer; sarcopenia 1. Introduction Computed tomography (CT) is considered the reference technique for body composition analysis in oncology [ 1 ], as it is an indirect technique with high spatial resolution, accuracy, and reproducibility [ 2 ]. Like magnetic resonance imaging (MRI), these imaging techniques can determine fatty infiltration in the muscle (myosteatosis) and measure visceral adipose tissue (VAT). Logistically, CT scans allow for opportunistic or retrospective measurements in routine studies requested in medical or surgical services for diagnostic– therapeutic purposes. As drawbacks, it is a high-cost and ionizing technique, although this could change with the introduction of low-radiation protocols for body composition Diagnostics 2024,14, 1696. https://doi.org/10.3390/diagnostics14151696 https://www.mdpi.com/journal/diagnostics Diagnostics 2024,14, 1696 2 of 17 analysis. Measurements are regional, yet whole-body estimation models are available [ 3 , 4 ]. Regarding technical error, the presence of intravenous contrast [ 5 ], volume overload [ 6 ], slice thickness, and tube current [7,8] should be taken into account. Image segmentation allows for the quantitative measurement of the Cross-Sectional Area (CSA, usually expressed in cm 2 ) of tissues in a region of interest (ROI). This process is based on the unique radiation absorption of each tissue, expressed as the attenuation intensity in Hounsfield Units (HU), and an adequate location of anatomical landmarks. An archetypical image for this task is an axial slice located in the third lumbar vertebra (L3), since it is the abdominal location with the maximum individual representativeness and interindividual variability [ 9 ]. The following tissues can be segmented for body composition analysis at this location: muscle tissue (MT), subcutaneous adipose tissue (SAT), VAT, and intramuscular adipose tissue (IMAT). Manual or semi-automatic image segmentation can be a labor-intensive and operatordependent procedure. To overcome this barrier, artificial intelligence (AI)-based software programs for body composition analysis allow for fully automated tissue segmentation [ 10 ], dramatically speeding up this process [ 11 , 12 ]. This opens the possibility of performing a 3D analysis of the body composition, analyzing all the images in the study in a fast and feasible way. These AI-based programs usually have restricted access and also have technical limitations: although their performance can be excellent, cases of erroneous segmentation can still occur [11]. For the time being, 2D and human-guided analyses still have a place. In colorectal cancer patients, the estimation of muscle atrophy using CT scans has been shown to be an independent predictor of events of interest such as survival [1,13–17]; physical, cognitive and social functionality [ 18 , 19 ]; quality of life [ 20 ]; postoperative complications [ 21 ]; length of hospital stay [ 22 , 23 ]; and the need for in-hospital rehabilitation or discharge to a nursing home [ 24 ]. Due to methodological heterogeneity, previous studies in colorectal cancer calculated a prevalence of muscle atrophy ranging from 15 to 60% and of myosteatosis from 19 to 78% [ 25 ]. Myosteatosis in CT scans has also been associated with a reduced survival time in digestive malignancies [ 15 – 17 , 26 – 28 ], increased risk of post-surgical complications [ 29 ], and reduced physical function [ 19 ]. Regarding adiposity and colorectal cancer, higher levels of SAT at baseline have been linked to a better diseasefree survival [ 30 ]. Chemotherapy responders have also shown an increase in their levels of adiponectin after treatment in comparison with non-responders [ 31 ]. The role of VAT may be more complex and time dependent: some studies have linked a VAT excess at baseline to lower survival [ 32 ] or more surgical complications [ 33 ], while others have found no impact [ 30 ], and some evidence points to a positive effect of VAT increase after surgery [ 34 ]. Despite its prognostic value, body composition image analysis in abdominal CT scans is not routinely conducted in many centers. To facilitate the implementation of a semiautomatic segmentation analysis in L3 axial images of CT scans in resource-limited settings, this study has compared the performances of two open-source and user-friendly software programs for body composition analysis: a picture archiving and communication system (PACS) viewer with semi-automatic HU-based threshold segmentation (Horos) and a web browser-based image analyzer with automatic threshold segmentation (CoreSlicer). Its target population is colorectal cancer due to the high prevalence of the disease and the previously described importance of body composition analysis in this group. 2. Materials and Methods 2.1. Study Design The study design comprised an analytical observational study that was carried out in a single center (Hospital Universitario Virgen del Rocío, Seville, Spain). The measurement period was from July 2022 to June 2024. Regarding the study sample, inclusion, exclusion, and withdrawal criteria were applied in colorectal cancer outpatients as previously published [ 35 ]. Consecutive sampling was used. Regarding the sample size, Lu et al.’s [ 36 ] methodology was applied using the statistical package blandPower (https://rdrr.io/github/nwisn/blandPower/f/README.md, Diagnostics 2024,14, 1696 3 of 17 accessed on 22 June 2024) [ 37 ] in Rstudio software (version 2023.06.1+524) [ 38 ]. An a priori risk of type I error = 0.05 and a risk of type II error = 0.20 were set to compare the muscle mass measurements provided by the two software programs of interest (Horos and CoreSlicer) in a Bland–Altman analysis. We predetermined a maximal clinically acceptable difference between the software programs: ( δ ) = 5%. A preliminary study with a subset of the study sample produced the following results: the mean of differences between Horos and CoreSlicer ( µ ) = 0.4 and the standard deviation of differences between Horos and CoreSlicer ( σ ) = 1.8. These parameters provided an estimated sample size of n= 68 pairs of measurements. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee “CEI de los Hospitales Universitarios Virgen Macarena y Virgen del Rocío” (protocol code: 1006-N-22; date of approval: 23 May 2022). 2.2. Data Collection 2.2.1. Image Analysis Abdominal CT scans were requested by the Oncology Department of our center due to diagnostic–therapeutic reasons. Both the General Electric Revolution EVO (GE HealthCare Technologies Inc., Chicago, IL, USA) and Toshiba Aquilion (Toshiba, Minato, Japan) scanners were used. Portovenous phase scans with a slice thickness of either 1.00 or 1.25 mm were obtained after intravenous administration of a contrast medium following a standardized acquisition protocol. The images were retrospectively downloaded in Digital Imaging and Communication in Medicine (DICOM) format using our local PACS server with Philips Vue PACS (Philips, Amsterdam, The Netherlands). DICOM files were then anonymized using DICOM Anonymizer v2.4.2 (https://www.dicomanonymizer.com/ index.html, accessed on 22 June 2024). Two software programs were used in this study, both capable of tissue segmentation based on intensity thresholds. Horos is an open-source code software (FOSS) program that is distributed free of charge under the LGPL license at Horosproject.org and sponsored by Nimble Co LLC d/b/a Purview (Annapolis, MD, USA). Horos is a 64-bit medical image viewer for Mac OS X based upon OsiriX TM and other open-source medical imaging libraries. Its 4.0.0RC4 version was used for this study (https://github.com/horosproject/ horos/releases, accessed on 22 June 2024). CoreSlicer [ 39 ] version 1.0 is a free-of-charge, web-based, CT scan segmentator and is distributed under an MIT license. Its 1.0 version was accessed for this study (https://old.coreslicer.com, accessed on 22 June 2024) with the available source code (https://github.com/louismullie/web-ct-segmentation, accessed on 22 June 2024). CoreSlicer has demonstrated prognostic value in rectal cancer [ 33 ] and has undergone a thorough validation process [ 39 ]. Nevertheless, Horos was selected as the gold-standard software in this study, as it has demonstrated the following requirements: prognostic capacity in the target population [ 40 ], reliability and accuracy in the measurement of myosteatosis [ 41 ], reliability and accuracy in the measurements of body composition volume [ 42 ], and excellent intraand inter-observer agreement with itself [ 43 ] and with respect to reference software [ 42 ]. Additionally, Horos has a graphical user interface (GUI) similar to PACS viewers commonly used in Radiology as well as a 3D volumetric rendering feature that facilitates the identification of structures in patients with anatomical alterations [44]. All measurements were simultaneously performed by a single operator on a Mac Mini M1 with 16 GB of RAM (Apple Inc., Cupertino, CA, USA) and an LG 32UN500P-W 31.5-inch screen with 4K resolution (LG Electronics, Seoul, Republic of Korea). The identification of the L3 vertebra and tissue segmentation in a selected axial slice were performed in all cases following the Alberta protocol (TomoVision, Magog, QC, Canada, https://tomovision.com/ Sarcopenia_Help/index.htm, accessed on 22 June 2024). The following HU thresholds were used to segment VAT ( − 150 to − 50 HU), SAT and IMAT ( − 190 to − 30 HU), and MT ( − 29 to +150 HU). We provide an example of tissue segmentation using both software programs Diagnostics 2024,14, 1696 4 of 17 in Figure 1. The Horos “3D Volume Rendering” function was used to three-dimensionally visualize the axial skeleton if needed. Diagnostics 2024, 14, x FOR PEER REVIEW 4 of 17 All measurements were simultaneously performed by a single operator on a Mac Mini M1 with 16 GB of RAM (Apple Inc., Cupertino, CA, USA) and an LG 32UN500P-W 31.5-inch screen with 4K resolution (LG Electronics, Seoul, Republic of Korea). The identification of the L3 vertebra and tissue segmentation in a selected axial slice were performed in all cases following the Alberta protocol (TomoVision, Magog, QC, Canada, https://tomovision.com/Sarcopenia_Help/index.htm, accessed on 22 June 2024). The following HU thresholds were used to segment VAT (−150 to −50 HU), SAT and IMAT (−190 to −30 HU), and MT (−29 to +150 HU). We provide an example of tissue segmentation using both software programs in Figure 1. The Horos “3D Volume Rendering” function was used to three-dimensionally visualize the axial skeleton if needed. Figure 1. Examples of tissue segmentation using Horos (left) and CoreSlicer (right) in the same slice at the third lumbar vertebra in an abdominal CT scan. The following tissues were segmented: muscle tissue (MT, represented in red), subcutaneous adipose tissue (SAT, represented in blue), visceral adipose tissue (VAT, represented in yellow), and intramuscular adipose tissue (IMAT, represented in purple). All images were visualized with an “Abdominal CT Scan” window in both software programs. In the case of CoreSlicer, Chrome version 126.0.6478.114 (Google LLC, Menlo Park, CA, USA) was used. To increase the image size, Chrome zoom was set at 125%, and CoreSlicer zoom was set at the maximum allowable value. Analogous to van Vugt et al. [42], segmentation in Horos was initially carried out using the “Grow Region (2D/3D Segmentation)” function to select pixels according to the intensity thresholds of the Alberta protocol. In the case of CoreSlicer, the initial segmentation was carried out with the software’s “Analyze Slice” function, which includes the following intensity thresholds: −190 to −30 HU for adipose tissues (VAT and SAT) and −29 to 150 HU for MT. CoreSlicer version 1.0’s built-in algorithm includes a median filter for denoising, a threshold filter for edge detection, and a percentile filter for edge smoothing. We refer to Additional file 1 of Mullie et al. for further information [39]. The initial semi-automatic segmentation results in both programs were later manually edited by A.J.S. to ensure the anatomical accuracy of the tissues of interest. Structures erroneously included within a tissue of interest (VAT, SAT, IMAT, or MT) due to HU similarity were deselected, and pixels of the tissue of interest that would not have been included in the initial analysis were included. For this purpose, the “Brush” tool was used in Horos, while the zoom was modified as needed with the “Magnify” tool. The “Brush” tool in Horos drew or erased pixels in the ROI independently of intensity. The “UCLA” palette within the “Color Look Up Table” in Horos and multiplanar reconstruction (MPR) were used on an ad hoc basis at the discretion of the researcher (A.J.S.) to improve the identification of structures. In CoreSlicer, the brush tool (with built-in intensity thresholds Figure 1. Examples of tissue segmentation using Horos (left) and CoreSlicer (right) in the same slice at the third lumbar vertebra in an abdominal CT scan. The following tissues were segmented: muscle tissue (MT, represented in red), subcutaneous adipose tissue (SAT, represented in blue), visceral adipose tissue (VAT, represented in yellow), and intramuscular adipose tissue (IMAT, represented in purple). All images were visualized with an “Abdominal CT Scan” window in both software programs. In the case of CoreSlicer, Chrome version 126.0.6478.114 (Google LLC, Menlo Park, CA, USA) was used. To increase the image size, Chrome zoom was set at 125%, and CoreSlicer zoom was set at the maximum allowable value. Analogous to van Vugt et al. [ 42 ], segmentation in Horos was initially carried out using the “Grow Region (2D/3D Segmentation)” function to select pixels according to the intensity thresholds of the Alberta protocol. In the case of CoreSlicer, the initial segmentation was carried out with the software’s “Analyze Slice” function, which includes the following intensity thresholds: − 190 to − 30 HU for adipose tissues (VAT and SAT) and − 29 to 150 HU for MT. CoreSlicer version 1.0’s built-in algorithm includes a median filter for denoising, a threshold filter for edge detection, and a percentile filter for edge smoothing. We refer to Additional file 1 of Mullie et al. for further information [39]. The initial semi-automatic segmentation results in both programs were later manually edited by A.J.S. to ensure the anatomical accuracy of the tissues of interest. Structures erroneously included within a tissue of interest (VAT, SAT, IMAT, or MT) due to HU similarity were deselected, and pixels of the tissue of interest that would not have been included in the initial analysis were included. For this purpose, the “Brush” tool was used in Horos, while the zoom was modified as needed with the “Magnify” tool. The “Brush” tool in Horos drew or erased pixels in the ROI independently of intensity. The “UCLA” palette within the “Color Look Up Table” in Horos and multiplanar reconstruction (MPR) were used on an ad hoc basis at the discretion of the researcher (A.J.S.) to improve the identification of structures. In CoreSlicer, the brush tool (with built-in intensity thresholds depending on the tissue of interest) was used for this task. In this program, a new region called “IMAT” was created in the built-in toolbox in the right side of the screen by selecting “THRESHOLD TYPE: Fat” and then clicking on the plus icon. Segmentations were carried out simultaneously in both software programs to maximize intra-operator repeatability so that measured differences could be mainly attributable to inter-software differences. Segmentation colors were kept the same in both software programs to facilitate a later review of high-resolution screenshots by a certified radiologist (E.S.R.). If human-made Diagnostics 2024,14, 1696 5 of 17 errors or inconsistencies were detected, the segmentation process was repeated, applying the necessary corrections. After image analysis was completed, the numerical values of the CSAs (cm 2 ) and intensities (HU) of MT, SAT, VAT, and IMAT were later registered for both programs on an Excel spreadsheet (Microsoft Corporation, Redmond, WA, USA). Therefore, all tissue segmentation procedures were blinded to these numerical values. This information was displayed using “ROI Info” of the .roi file of each segmented tissue in Horos and the “measurements.csv” file resulting from each CoreSlicer segmentation. 2.2.2. Operative Definitions of Dynapenia, Muscle Atrophy, Sarcopenia, and Visceral Obesity The diagnosis of muscle atrophy was based on the Skeletal Muscle Index (SMI; cm2/m2), which was obtained from the measured MT CSA (cm2) using the formula SMI =(MT −CSA)/height2 The diagnosis of myosteatosis was based on the measured MT intensity. The selected cutoff points for the diagnosis of both conditions were the p5 reference values for a healthy population published by Van Vugt et al. [ 45 ], as well as the prognostic thresholds provided by Dolan et al. in colorectal cancer [ 46 ]. To diagnose dynapenia, maximal handgrip strength was determined as previously described [ 35 ] on the same day of the CT scan. In this study, we used the maximal handgrip strength normative values developed by Dodds et al. [ 47 ], and dynapenia was defined with a percentile-based approach (maximal strength below the corresponding 10th percentile based on age and sex) [ 48 ]. Sarcopenia was defined as the conjunction of muscle atrophy and dynapenia using the EWGSOP-II criteria [ 48 ]. The diagnosis of visceral obesity was based on the VAT CSA (cm2) using the cutoff points published by Doyle et al. [49]. 2.2.3. Basic Anthropometry Protocol The height and weight were measured on the same day of the CT scan following ESPEN guidance [ 50 ]. A description of the instruments used for this task is available elsewhere [35]. 2.2.4. Clinical Variables and Cancer Staging Clinical variables were defined and obtained from digitized health records (“DIRAYA Clinical Station”) as previously published [ 35 ], including information about surgical treatment. 2.2.5. Data Quality All measurements were carried out by a single researcher with experience in body composition analysis (A.J.S.). Image analysis was supervised by a certified radiologist (E.S.R.) with extensive experience in abdomen imaging. Cancer stagings, treatments, and performance scores were registered in the database as recorded by oncologists (J.R.R.-M.) in health records. The type of surgery was registered in the database as recorded by surgeons (I.R.-S.) in health records. 2.3. Data Analysis For the statistical analysis, the packages tidyverse [ 51 ], cowplot [ 52 ], DescTools [ 53 ], ggpubr [ 54 ], and Rcmdr [ 55 ] were used in RStudio software (version 2023.06.1+524) [ 38 ]. Normality was analyzed with the Shapiro–Wilk test. Normally distributed variables were depicted as the mean and standard deviation (SD), and non-normally distributed variables were described as the median and interquartile range (IQR). Central tendency measurements of the CSA and intensity were compared in all tissues of interest (MT, SAT, VAT, and IMAT) depending on the software program (Horos vs. CoreSlicer), both as raw Diagnostics 2024,14, 1696 6 of 17 measurements and relative differences ( ∆ ) of CoreSlicer to Horos, with the latter computed as follows: ∆=(Parameter(CoreSlicer)−Parameter(Horos))/Parameter(Horos) At-test (in the presence of normality and homoscedasticity) or a Wilcoxon signed-rank test were used otherwise. Simple correlation was calculated with the Pearson correlation coefficient (r). Accuracy and precision regarding the CSA and intensity for each tissue of interest (MT, SAT, VAT, and IMAT) in Horos and CoreSlicer were determined using the Bland–Altman analysis [ 56 ] and Lin’s Concordance Correlation Coefficient ( ρ ), considering values > 0.99 as “near perfect”, 0.95 to 0.99 as “substantial”, 0.90 to 0.98 as “moderate”, and < 0.90 as “poor” [ 57 ]. Differences in the prevalence of sarcopenia, myosteatosis, and excess VAT were compared using an X 2 test. Categorical agreement in this regard was studied using Cohen’s kappa [ 58 ]. Outliers were not censored, and all measurements were included for statistical analysis. Statistical significance was determined in all two-tailed tests as a p-value < 0.05. 3. Results 3.1. Clinical and Demographical Descriptions of the Study Sample A total of n= 68 participants were measured and included for the analysis. Their clinical data are registered in Table 1. The study participants were mostly affected by right colon (n= 13) and sigmoid (n= 13) neoplasms. The modal TNM stage at diagnosis was IIIB (n= 22). A vast majority of participants had undergone surgery (n= 58). The most frequent type of surgery was low anterior resection (n= 16), followed by right hemicolectomy (n= 15). Only n= 17 participants were under active chemotherapy at the time of measurement. The sample modal ECOG score was 1. The modal BMI was normal weight, followed by overweight. No participant displayed clinically evident signs of volume overload. Table 1. Clinical and demographic characteristics of the study sample. Parameter Results Sample size (ni)n= 68 Age (years) Me = 64.72 IQR = 12.67 Older than 65 (ni)n= 32 (47.05%) Female (ni)n= 31 (45.58%) Neoplasm location (ni) Right colon, n= 13 Transverse colon, n= 4 Left colon, n= 6 Rectosigmoid, n= 5 Sigma, n= 13 Rectum, n= 27 Stage (TNM) at 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) Previous surgery Yes, n= 58 No, n= 10 First surgery Abdomino-perineal resection, n= 3 Colostomy, n= 2 Hepatectomy, n= 3 Left hemi-colectomy, n= 8 Low anterior resection, n= 16 Diagnostics 2024,14, 1696 7 of 17 Table 1. Cont. Parameter Results First surgery Right hemi-colectomy, n= 15 Sigmoidectomy, n= 10 Sub-total colectomy, n= 1 Active chemotherapy (ni)Yes, n= 17 No, n= 51 ECOG (ni)0, n= 46 1, n= 22 Weight (kg) Mean = 74.17 SD = 14.61 Height (m) Mean = 1.644 SD = 0.092 BMI (kg/m2)Me = 27.0 IQR = 4.6 BMI by group (ni) Underweight, n= 4 Normal weight, n= 20 Overweight, n= 30 Grade 1 obesity, n= 7 Grade 2 obesity, n= 7 Clinical characteristics of the sample. Numerical values are expressed as absolute frequencies (n i ), percentages (%), medians (Me), and interquartile ranges (IQRs). 3.2. Image Analysis Characteristics Regarding imaging, n= 63 studies were undertaken using a GE Revolution EVO scanner and n= 5 using a Toshiba Aquilion. All studies used intravenous contrast and had either a 1.25 mm slice thickness (n= 59) or 1.00 mm slice thickness (n= 9). The image acquisition parameters were as following: voltage = 120 kV in all cases, and amperage = 5(3) mAs. The first study was acquired on 11 July 2022 and the last on 25 April 2023. 3.3. Comparisons of Tissue CSAs and Tissue Intensities between Software Programs When comparing CSAs in the tissues of interest (MT, SAT, VAT, and IMAT) using both software programs (CoreSlicer and Horos), no significant differences were found in either MT (130.682 vs. 130.852 cm 2 ) or IMAT (8.700 vs. 8.317 cm 2 ). Although the magnitude of the difference in IMAT was small in absolute terms (0.187 cm 2 ), it proved large in relative terms (+18.045%) due to its small CSA. Regarding the other adipose tissue compartments, statistically significant differences were found in both SAT (188.911 vs. 187.352 cm 2 ) and VAT (182.990 vs. 166.092 cm 2 ). A detailed description of these data is in Table 2, with their graphical description in Figure 2. Table 2. Comparisons in the CSA (cm2) for the different tissues using Horos and CoreSlicer. CoreSlicer Horos Absolute Differences (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: Cross-Sectional Area; ∆ = (CoreSlicer CSA − Horos CSA) ÷ Horos CSA; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. Diagnostics 2024,14, 1696 8 of 17 Diagnostics 2024, 14, x FOR PEER REVIEW 8 of 17 CSA: Cross-Sectional Area; ΔCSA = (CoreSlicer CSA − Horos CSA) ÷ Horos CSA; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. Figure 2. Comparisons of the CSA (Cross-Sectional Area) in cm2 for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose tissue (SAT) (B); visceral adipose tissue (VAT) (C); intramuscular adipose tissue (IMAT) (D). The significance of the performed Wilcoxon signed-rank test appears as either “ns” (not significant) or with the following symbols representing p-values: * <0.05; ** <0.01; *** <0.001; **** <0.0001. When comparing the intensity in the tissues of interest (MT, SAT, VAT, and IMAT) using both software programs (CoreSlicer and Horos), all tissues presented significant differences: MT (33.237 vs. 35.398 cm2), SAT (−103.945 vs. −106.530 cm2), VAT (−87.868 vs. −92.723 cm2), and IMAT (−64.353 vs. −65.272 cm2). A detailed description of these data is in Table 3, with their graphical description in Figure 3. Table 3. Comparisons of the intensity (HU) for the different tissues using Horos and CoreSlicer. CoreSlicer Horos Absolute Differences (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: Hounsfield Units; ΔHU = (CoreSlicer HU − Horos HU) ÷ Horos HU; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. ns 75 100 125 150 175 200 CoreSlicer Horos Absolute differences in MT CSA (cm²) A **** 0 100 200 300 400 500 CoreSlicer Horos Absolute differences in SAT CSA (cm²) B **** 0 100 200 300 400 500 CoreSlicer Horos Absolute differences in VAT CSA (cm²) C ns 0 10 20 30 40 CoreSlicer Horos Absolute differences in IMAT CSA (cm²) D **** 20 30 40 50 60 CoreSlicer Horos Absolute differences in MT intensity (HU) A **** −100 −75 −50 CoreSlicer Horos Absolute differences in SAT intensity (HU) B **** −100 −80 −60 CoreSlicer Horos Absolute differences in VAT intensity (HU) C * −80 −70 −60 −50 CoreSlicer Horos Absolute differences in IMAT intensity (HU) D Figure 2. Comparisons of the CSA (Cross-Sectional Area) in cm 2 for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose tissue (SAT) (B); visceral adipose tissue (VAT) (C); intramuscular adipose tissue (IMAT) (D). The significance of the performed Wilcoxon signed-rank test appears as either “ns” (not significant) or with the following symbols representing p-values: **** < 0.0001. When comparing the intensity in the tissues of interest (MT, SAT, VAT, and IMAT) using both software programs (CoreSlicer and Horos), all tissues presented significant differences: MT (33.237 vs. 35.398 cm 2 ), SAT ( − 103.945 vs. − 106.530 cm 2 ), VAT ( − 87.868 vs. − 92.723 cm 2 ), and IMAT ( − 64.353 vs. − 65.272 cm 2 ). A detailed description of these data is in Table 3, with their graphical description in Figure 3. Diagnostics 2024, 14, x FOR PEER REVIEW 8 of 17 CSA: Cross-Sectional Area; ΔCSA = (CoreSlicer CSA − Horos CSA) ÷ Horos CSA; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. Figure 2. Comparisons of the CSA (Cross-Sectional Area) in cm2 for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose tissue (SAT) (B); visceral adipose tissue (VAT) (C); intramuscular adipose tissue (IMAT) (D). The significance of the performed Wilcoxon signed-rank test appears as either “ns” (not significant) or with the following symbols representing p-values: * <0.05; ** <0.01; *** <0.001; **** <0.0001. When comparing the intensity in the tissues of interest (MT, SAT, VAT, and IMAT) using both software programs (CoreSlicer and Horos), all tissues presented significant differences: MT (33.237 vs. 35.398 cm2), SAT (−103.945 vs. −106.530 cm2), VAT (−87.868 vs. −92.723 cm2), and IMAT (−64.353 vs. −65.272 cm2). A detailed description of these data is in Table 3, with their graphical description in Figure 3. Table 3. Comparisons of the intensity (HU) for the different tissues using Horos and CoreSlicer. CoreSlicer Horos Absolute Differences (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: Hounsfield Units; ΔHU = (CoreSlicer HU − Horos HU) ÷ Horos HU; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. ns 75 100 125 150 175 200 CoreSlicer Horos Absolute differences in MT CSA (cm²) A **** 0 100 200 300 400 500 CoreSlicer Horos Absolute differences in SAT CSA (cm²) B **** 0 100 200 300 400 500 CoreSlicer Horos Absolute differences in VAT CSA (cm²) C ns 0 10 20 30 40 CoreSlicer Horos Absolute differences in IMAT CSA (cm²) D **** 20 30 40 50 60 CoreSlicer Horos Absolute differences in MT intensity (HU) A **** −100 −75 −50 CoreSlicer Horos Absolute differences in SAT intensity (HU) B **** −100 −80 −60 CoreSlicer Horos Absolute differences in VAT intensity (HU) C * −80 −70 −60 −50 CoreSlicer Horos Absolute differences in IMAT intensity (HU) D Figure 3. Comparisons of intensity in HU (Hounsfield Units) for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose tissue (SAT) (B); visceral adipose tissue (VAT) (C); intramuscular adipose tissue (IMAT) (D). The significance of the performed Wilcoxon signed-rank test appears as either “ns” (not significant) or with the following symbols representing p-values: * < 0.05; **** < 0.0001. Diagnostics 2024,14, 1696 9 of 17 Table 3. Comparisons of the intensity (HU) for the different tissues using Horos and CoreSlicer. CoreSlicer Horos Absolute Differences (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: Hounsfield Units; ∆ = (CoreSlicer HU − Horos HU) ÷ Horos HU; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. 3.4. Correlations of Tissue CSAs and Tissue Intensities between Software Programs Pearson correlation coefficients for both the CSA and intensity in the different tissues of interest between Horos and CoreSlicer are described in Table 4. The correlation was very strong for all parameters, the strongest being the MT CSA (r= 0.998; IC95%: 0.997 to 0.998), SAT CSA (r= 0.998; IC95%: 0.997 to 0.999), and VAT CSA (r= 0.998; IC95%: 0.996 to 0.998). The IMAT intensity had the weakest correlation (r= 0.843; IC95%: 0.756 to 0.900). The graphical representation of these data is depicted in Figure 4. Table 4. Pearson correlation coefficients (r) for the CSA and intensity in the different tissues using Horos and CoreSlicer. Measured Tissue CSA (cm2, 95% CI) Intensity (HU, 95% CI) MT 0.998 (0.997 to 0.998) 0.982 (0.971 to 0.989) SAT 0.998 (0.997 to 0.999) 0.984 (0.975 to 0.990) VAT 0.998 (0.996 to 0.998) 0.946 (0.914 to 0.966) IMAT 0.985 (0.976 to 0.990) 0.843 (0.756 to 0.900) 95% CI: 95% confidence interval; CSA: Cross-Sectional Area; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. The 95% confidence intervals are represented in parenthesis. Diagnostics 2024, 14, x FOR PEER REVIEW 9 of 17 Figure 3. Comparisons of intensity in HU (Hounsfield Units) for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose tissue (SAT) (B); visceral adipose tissue (VAT) (C); intramuscular adipose tissue (IMAT) (D). The significance of the performed Wilcoxon signed-rank test appears as either “ns” (not significant) or with the following symbols representing p-values: * < 0.05; ** <0.01; *** <0.001; **** <0.0001. 3.4. Correlations of Tissue CSAs and Tissue Intensities between Software Programs Pearson correlation coefficients for both the CSA and intensity in the different tissues of interest between Horos and CoreSlicer are described in Table 4. The correlation was very strong for all parameters, the strongest being the MT CSA (r = 0.998; IC95%: 0.997 to 0.998), SAT CSA (r = 0.998; IC95%: 0.997 to 0.999), and VAT CSA (r = 0.998; IC95%: 0.996 to 0.998). The IMAT intensity had the weakest correlation (r = 0.843; IC95%: 0.756 to 0.900). The graphical representation of these data is depicted in Figure 4. Table 4. Pearson correlation coefficients (r) for the CSA and intensity in the different tissues using Horos and CoreSlicer. Measured Tissue CSA (cm2, 95% CI) Intensity (HU, 95% CI) MT 0.998 (0.997 to 0.998) 0.982 (0.971 to 0.989) SAT 0.998 (0.997 to 0.999) 0.984 (0.975 to 0.990) VAT 0.998 (0.996 to 0.998) 0.946 (0.914 to 0.966) IMAT 0.985 (0.976 to 0.990) 0.843 (0.756 to 0.900) 95% CI: 95% confidence interval; CSA: Cross-Sectional Area; MT: muscle tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; IMAT: intramuscular adipose tissue. The 95% confidence intervals are represented in parenthesis. Figure 4. Simple linear regression of the CSA (Cross-Sectional Area) in cm2 for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose tissue (SAT) (B); visceral adipose tissue (VAT) (C); intramuscular adipose tissue (IMAT) (D). Simple linear regression of the intensity in HU (Hounsfield Units) for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (E); subcutaneous adipose tissue (SAT) (F); visceral adipose tissue (VAT) (G); intramuscular adipose tissue (IMAT) (H). In all cases, the perfect bisector of inter-software regression is shown as a solid red line, and the 75 100 125 150 175 100 125 150 175 MT CSA (cm²) using CoreSlicer MT CSA (cm²) using Horos A 0 100 200 300 400 500 0 100 200 300 400 500 SAT CSA (cm²) using CoreSlicer SAT CSA (cm²) using Horos B 0 100 200 300 400 0 100 200 300 400 500 VAT CSA (cm²) using CoreSlicer VAT CSA (cm²) using Horos C 0 10 20 30 40 0 10203040 IMAT CSA (cm²) using CoreSlicer IMAT CSA (cm²) using Horos D 20 30 40 50 20 30 40 50 MT intensity (HU) using CoreSlicer MT intensity (HU) using Horos E −100 −80 −60 −100 −80 −60 −40 SAT intensity (HU) using CoreSlicer SAT intensity (HU) using Horos F −100 −90 −80 −70 −100 −90 −80 −70 −60 −50 VAT intensity (HU) using CoreSlicer VAT intensity (HU) using Horos G −80 −70 −60 −50 −70 −60 IMAT intensity (HU) using CoreSlicer IMAT intensity (HU) using Horos H Figure 4. Simple linear regression of the CSA (Cross-Sectional Area) in cm 2 for the different tissues of interest between software programs (Horos and CoreSlicer): muscle tissue (MT) (A); subcutaneous adipose Diagnostics 2024,14, 1696 16 of 17 25. Hopkins, J.J.; Skubleny, D.; Bigam, D.L.; Baracos, V.E.; Eurich, D.T.; Sawyer, M.B. Barriers to the Interpretation of Body Composition in Colorectal Cancer: A Review of the Methodological Inconsistency and Complexity of the CT-Defined Body Habitus. Ann. Surg. Oncol. 2018,25, 1381–1394. [CrossRef] [PubMed] 26. Hacker, U.T.; Hasenclever, D.; Linder, N.; Stocker, G.; Chung, H.-C.; Kang, Y.-K.; Moehler, M.; Busse, H.; Lordick, F. Prognostic Role of Body Composition Parameters in Gastric/Gastroesophageal Junction Cancer Patients from the EXPAND Trial. J. Cachexia Sarcopenia Muscle 2020,11, 135–144. [CrossRef] [PubMed] 27. van Baar, H.; Beijer, S.; Bours, M.J.L.; Weijenberg, M.P.; van Zutphen, M.; van Duijnhoven, F.J.B.; Slooter, G.D.; Pruijt, J.F.M.; Dronkers, J.J.; Haringhuizen, A.; et al. Low Radiographic Muscle Density Is Associated with Lower Overall and Disease-Free Survival in Early-Stage Colorectal Cancer Patients. J. Cancer Res. Clin. Oncol. 2018,144, 2139–2147. [CrossRef] [PubMed] 28. Kroenke, C.H.; Prado, C.M.; Meyerhardt, J.A.; Weltzien, E.K.; Xiao, J.; Cespedes Feliciano, E.M.; Caan, B.J. Muscle Radiodensity and Mortality in Patients with Colorectal Cancer. Cancer 2018,124, 3008–3015. [CrossRef] [PubMed] 29. Liu, Z.; Lu, S.; Wang, Y.; Lin, X.; Ran, P.; Zhou, X.; Fu, W.; Wang, H. Impact of Body Composition During Neoadjuvant Chemoradiotherapy on Complications, Survival and Tumor Response in Patients With Locally Advanced Rectal Cancer. Front. Nutr. 2022,9, 796601. [CrossRef] 30. Kim, J.-M.; Chung, E.; Cho, E.-S.; Lee, J.-H.; Shin, S.-J.; Lee, H.S.; Park, E.J.; Baik, S.H.; Lee, K.Y.; Kang, J. Impact of Subcutaneous and Visceral Fat Adiposity in Patients with Colorectal Cancer. Clin. Nutr. 2021,40, 5631–5638. [CrossRef] 31. Słomian, G.; ´ Swi˛etochowska, E.; Nowak, G.; Pawlas, K.; ˙ Zelazko, A.; Nowak, P. Chemotherapy and Plasma Adipokines Level in Patients with Colorectal Cancer. Adv. Hyg. Exp. Med. 2017,71, 281–290. [CrossRef] [PubMed] 32. Tolonen, A.; Pakarinen, T.; Sassi, A.; Kyttä, J.; Cancino, W.; Rinta-Kiikka, I.; Pertuz, S.; Arponen, O. Methodology, Clinical Applications, and Future Directions of Body Composition Analysis Using Computed Tomography (CT) Images: A Review. Eur. J. Radiol. 2021,145, 109943. [CrossRef] [PubMed] 33. Bocca, G.; Mastoridis, S.; Yeung, T.; James, D.R.C.; Cunningham, C. Visceral-to-Subcutaneous Fat Ratio Exhibits Strongest Association with Early Post-Operative Outcomes in Patients Undergoing Surgery for Advanced Rectal Cancer. Int. J. Colorectal. Dis. 2022,37, 1893–1900. [CrossRef] [PubMed] 34. Choe, E.K.; Park, K.J.; Ryoo, S.B.; Moon, S.H.; Oh, H.K.; Han, E.C. Prognostic Impact of Changes in Adipose Tissue Areas after Colectomy in Colorectal Cancer Patients. J. Korean Med. Sci. 2016,31, 1571–1578. [CrossRef] [PubMed] 35. Jiménez-Sánchez, A.; Pereira-Cunill, J.L.; Limón-Mirón, M.L.; López-Ladrón, A.; Salvador-Bofill, F.J.; García-Luna, P.P. A CrossSectional Validation Study of Camry EH101 versus JAMAR Plus Handheld Dynamometers in Colorectal Cancer Patients and Their Correlations with Bioelectrical Impedance and Nutritional Status. Nutrients 2024,16, 1824. [CrossRef] [PubMed] 36. Lu, M.-J.; Zhong, W.-H.; Liu, Y.-X.; Miao, H.-Z.; Li, Y.-C.; Ji, M.-H. Sample Size for Assessing Agreement between Two Methods of Measurement by Bland-Altman Method. Int. J. Biostat. 2016,12, 20150039. [CrossRef] [PubMed] 37. Wisniewski, N. blandPower: Tools for Bland-Altman Analysis. R Package Version 0.1.0. Available online: https://rdrr.io/github/ nwisn/blandPower (accessed on 22 June 2024). 38. RStudio Team. RStudio: Integrated Development for R. Available online: https://www.rstudio.com (accessed on 22 June 2024). 39. Mullie, L.; Afilalo, J. CoreSlicer: A Web Toolkit for Analytic Morphomics. BMC Med. Imaging 2019,19, 15. [CrossRef] 40. Tschann, P.; Weigl, M.P.; Clemens, P.; Szeverinski, P.; Attenberger, C.; Kowatsch, M.; Jäger, T.; Emmanuel, K.; Brock, T.; Königsrainer, I. Sarcopenic Obesity Is a Risk Factor for Worse Oncological Long-Term Outcome in Locally Advanced Rectal Cancer Patients: A Retrospective Single-Center Cohort Study. Nutrients 2023,15, 2632. [CrossRef] 41. Rollins, K.E.; Awwad, A.; Macdonald, I.A.; Lobo, D.N. A Comparison of Two Different Software Packages for Analysis of Body Composition Using Computed Tomography Images. Nutrition 2019,57, 92–96. [CrossRef] 42. van Vugt, J.L.A.; Levolger, S.; Gharbharan, A.; Koek, M.; Niessen, W.J.; Burger, J.W.A.; Willemsen, S.P.; de Bruin, R.W.F.; IJzermans, J.N.M. A Comparative Study of Software Programmes for Cross-Sectional Skeletal Muscle and Adipose Tissue Measurements on Abdominal Computed Tomography Scans of Rectal Cancer Patients. J. Cachexia Sarcopenia Muscle 2017,8, 285–297. [CrossRef] 43. Pescatori, L.C.; Savarino, E.; Mauri, G.; Silvestri, E.; Cariati, M.; Sardanelli, F.; Sconfienza, L.M. Quantification of Visceral Adipose Tissue by Computed Tomography and Magnetic Resonance Imaging: Reproducibility and Accuracy. Radiol. Bras. 2019,52, 1–6. [CrossRef] [PubMed] 44. Cevik, J.; Rozen, W. A Novel Optimization Technique of Computed Tomography Angiographic 3D-Reconstructions for PreOperative Planning of DIEP Flaps. JPRAS Open 2023,35, 38–41. [CrossRef] [PubMed] 45. Van Vugt, J.L.A.; Van Putten, Y.; Van Der Kall, I.M.; Buettner, S.; D’Ancona, F.C.H.; Dekker, H.M.; Kimenai, H.J.A.N.; De Bruin, R.W.F.; Warlé, M.C.; IJzermans, J.N.M. Estimated Skeletal Muscle Mass and Density Values Measured on Computed Tomography Examinations in over 1000 Living Kidney Donors. Eur. J. Clin. Nutr. 2019,73, 879–886. [CrossRef] [PubMed] 46. Dolan, R.D.; Almasaudi, A.S.; Dieu, L.B.; Horgan, P.G.; McSorley, S.T.; McMillan, D.C. The Relationship between Computed Tomography-derived Body Composition, Systemic Inflammatory Response, and Survival in Patients Undergoing Surgery for Colorectal Cancer. J. Cachexia Sarcopenia Muscle 2019,10, 111–122. [CrossRef] [PubMed] 47. Dodds, R.M.; Syddall, H.E.; Cooper, R.; Benzeval, M.; Deary, I.J.; Dennison, E.M.; Der, G.; Gale, C.R.; Inskip, H.M.; Jagger, C.; et al. Grip Strength across the Life Course: Normative Data from Twelve British Studies. PLoS ONE 2014,9, e113637. [CrossRef] [PubMed] 48. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European Consensus on Definition and Diagnosis. Age Ageing 2019,48, 16–31. [CrossRef] [PubMed] Diagnostics 2024,14, 1696 17 of 17 49. Doyle, S.L.; Bennett, A.M.; Donohoe, C.L.; Mongan, A.M.; Howard, J.M.; Lithander, F.E.; Pidgeon, G.P.; Reynolds, J.V.; Lysaght, J. Establishing Computed Tomography-Defined Visceral Fat Area Thresholds for Use in Obesity-Related Cancer Research. Nutr. Res. 2013,33, 171–179. [CrossRef] [PubMed] 50. Earthman, C.P. Body Composition Tools for Assessment of Adult Malnutrition at the Bedside: A Tutorial on Research Considerations and Clinical Applications. JPEN J. Parenter. Enter. Nutr. 2015,39, 787–822. [CrossRef] [PubMed] 51. Wickham, H.; Averick, M.; Bryan, J.; Chang, W.; McGowan, L.D.; François, R.; Grolemund, G.; Hayes, A.; Henry, L.; Hester, J.; et al. Welcome to the tidyverse. JOSS 2019,4, 1–6. [CrossRef] 52. Wilke, C. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R Package Version 1.1.3. Available online: https://CRAN.R-project.org/package=cowplot (accessed on 22 June 2024). 53. Signorell, A. DescTools: Tools for Descriptive Statistics. R Package Version 0.99.54. Available online: https://CRAN.R-project. org/package=DescTools (accessed on 22 June 2024). 54. Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R Package Version 0.6.0. Available online: https://CRAN.Rproject.org/package=ggpubr (accessed on 22 June 2024). 55. Fox, J. The R Commander: A Basic-Statistics Graphical User Interface to R. J. Stat. Soft. 2005,14, 1–42. [CrossRef] 56. Bland, J.M.; Altman, D.G. Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement. Lancet 1986,1, 307–310. [CrossRef] 57. Akoglu, H. User’s Guide to Correlation Coefficients. Turk. J. Emerg. Med. 2018,18, 91–93. [CrossRef] [PubMed] 58. McHugh, M.L. Interrater Reliability: The Kappa Statistic. Biochem. Med. 2012,22, 276–282. [CrossRef] 59. Barbalho, E.R.; Rocha, I.M.G.D.; Medeiros, G.O.C.D.; Friedman, R.; Fayh, A.P.T. Agreement between Software Programmes of Body Composition Analyses on Abdominal Computed Tomography Scans of Obese Adults. Arch. Endocrinol. Metab. 2019,64, 24–29. [CrossRef] [PubMed] 60. Nappi, C.; Megna, R.; Volpe, F.; Ponsiglione, A.; Caiazzo, E.; Piscopo, L.; Mainolfi, C.G.; Vergara, E.; Imbriaco, M.; Klain, M.; et al. Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs. Appl. Sci. 2022,12, 5468. [CrossRef] 61. Viddeleer, A.R.; Vedder, I.R.; Dob, R.; Bokkers, R.P.H. Objective Comparison of Commonly Used Computed Tomography Body Composition Analysis Software. Nutrition 2024,123, 112421. [CrossRef] 62. Martin, L.; Tom, M.; Basualdo-Hammond, C.; Baracos, V.; Gramlich, L. Piloting a Training Program in Computed Tomography Skeletal Muscle Assessment for Registered Dietitians. JPEN J. Parenter. Enteral. Nutr. 2022,46, 1317–1325. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.