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Decazes P, Ammari S, Belkouchi Y, Mottay L, Lawrance L, de Prévia A, Talbot H, Farhane S, Cournède PH, Marabelle A, Guisier F, Planchard D, Ibrahim T, Robert C, Barlesi F, Vera P, Lassau N. Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer. J Immunother Cancer 2023; 11:e007315. [PMID: 37678919 PMCID: PMC10496660 DOI: 10.1136/jitc-2023-007315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy. METHODS We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis. RESULTS In the overall cohort, 1-year mortality rate was 0.496 (95% CI: 0.457 to 0.537) for 309 events and 5-year mortality rate was 0.196 (95% CI: 0.165 to 0.233) for 477 events. In the univariate Kaplan-Meier analysis, prognosis was worse (p<0.001) for patients with low SFM (<3.95 kg/m2), low FBM (<3.26 kg/m2), low VFM (<0.91 kg/m2), low MBM (<5.85 kg/m2) and low BMI (<24.97 kg/m2). The same parameters were significant in the Cox univariate analysis (p<0.001) and, in the multivariate stepwise Cox analysis, the significant parameters were MBM (p<0.0001), SFM (0.013) and WL (0.0003). In subanalyses according to the type of cancer, all body composition parameters were statistically significant for NSCLC in ROC, KM and Cox univariate analysis while, for melanoma, none of them, except MBM, was statistically significant. In multivariate Cox analysis, the significant parameters for NSCLC were MBM (HR=0.81, p=0.0002), SFM (HR=0.94, p=0.02) and WL (HR=1.06, p=0.004). For NSCLC, a KM analysis combining SFM and MBM was able to separate the population in three categories with the worse prognostic for the patients with both low SFM (<5.22 kg/m2) and MBM (<6.86 kg/m2) (p<0001). On the external validation cohort, combination of low SFM and low MBM was pejorative with 63% of mortality at 1 year versus 25% (p=0.0029). CONCLUSIONS 3D measured low SFM and MBM are significant prognosis factors of NSCLC treated by immune checkpoint inhibitors and can be combined to improve the prognostic value.
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Affiliation(s)
- Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, France
- QuantIF-LITIS (EA[Equipe d'Accueil] 4108), Faculty of Medicine, University of Rouen, 76000 Rouen, France
| | - Samy Ammari
- Biomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
| | - Younes Belkouchi
- Biomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, France
- Centre de Vision Numérique, CentraleSupélec, Inria, Université Paris-Saclay, 91190 Gif-Sur-Yvette, France
| | - Léo Mottay
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, France
- QuantIF-LITIS (EA[Equipe d'Accueil] 4108), Faculty of Medicine, University of Rouen, 76000 Rouen, France
| | - Littisha Lawrance
- Biomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, France
| | - Antoine de Prévia
- Biomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, France
| | - Hugues Talbot
- Centre de Vision Numérique, CentraleSupélec, Inria, Université Paris-Saclay, 91190 Gif-Sur-Yvette, France
| | - Siham Farhane
- Département des Innovations Thérapeutiques et Essais Précoces, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France
| | - Paul-Henry Cournède
- MICS Lab, CentraleSupelec, Universite Paris-Saclay, 91190 Gif-Sur-Yvette, France
| | - Aurelien Marabelle
- Department of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
| | - Florian Guisier
- QuantIF-LITIS (EA[Equipe d'Accueil] 4108), Faculty of Medicine, University of Rouen, 76000 Rouen, France
- Department of Pneumology and Inserm CIC-CRB 1404, CHU Rouen, 76000 Rouen, France
| | - David Planchard
- Department of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
| | - Tony Ibrahim
- Department of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
| | - Caroline Robert
- Department of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
| | - Fabrice Barlesi
- Department of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
| | - Pierre Vera
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, France
- QuantIF-LITIS (EA[Equipe d'Accueil] 4108), Faculty of Medicine, University of Rouen, 76000 Rouen, France
| | - Nathalie Lassau
- Biomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, France
- Department of Imaging, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, France
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Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs. Diagnostics (Basel) 2023; 13:diagnostics13020205. [PMID: 36673015 PMCID: PMC9858245 DOI: 10.3390/diagnostics13020205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/08/2023] Open
Abstract
Background: Body composition could help to better define the prognosis of cancers treated with anti-angiogenics. The aim of this study is to evaluate the prognostic value of 3D and 2D anthropometric parameters in patients given anti-angiogenic treatments. Methods: 526 patients with different types of cancers were retrospectively included. The software Anthropometer3DNet was used to measure automatically fat body mass (FBM3D), muscle body mass (MBM3D), visceral fat mass (VFM3D) and subcutaneous fat mass (SFM3D) in 3D computed tomography. For comparison, equivalent two-dimensional measurements at the L3 level were also measured. The area under the curve (AUC) of the receiver operator characteristics (ROC) was used to determine the parameters’ predictive power and optimal cut-offs. A univariate analysis was performed using Kaplan−Meier on the overall survival (OS). Results: In ROC analysis, all 3D parameters appeared statistically significant: VFM3D (AUC = 0.554, p = 0.02, cutoff = 0.72 kg/m2), SFM3D (AUC = 0.544, p = 0.047, cutoff = 3.05 kg/m2), FBM3D (AUC = 0.550, p = 0.03, cutoff = 4.32 kg/m2) and MBM3D (AUC = 0.565, p = 0.007, cutoff = 5.47 kg/m2), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, p = 0.034). In log-rank tests, low VFM3D (p = 0.014), low SFM3D (p < 0.0001), low FBM3D (p = 0.00019) and low VFA2D (p = 0.0063) were found as a significant risk factor. Conclusion: automatic and 3D body composition on pre-therapeutic CT is feasible and can improve prognostication in patients treated with anti-angiogenic drugs. Moreover, the 3D measurements appear to be more effective than their 2D counterparts.
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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. [PMID: 34839215 DOI: 10.1016/j.ejrad.2021.109943] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/06/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE OF THE REVIEW We aim to review the methods, current research evidence, and future directions in body composition analysis (BCA) with CT imaging. RECENT FINDINGS CT images can be used to evaluate muscle tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) compartments. Manual and semiautomatic segmentation methods are still the gold standards. The segmentation of skeletal muscle tissue and VAT and SAT compartments is most often performed at the level of the 3rd lumbar vertebra. A decreased amount of CT-determined skeletal muscle mass is a marker of impaired survival in many patient populations, including patients with most types of cancer, some surgical patients, and those admitted to the intensive care unit (ICU). Patients with increased VAT are more susceptible to impaired survival / worse outcomes; however, those patients who are critically ill or admitted to the ICU or who will undergo surgery appear to be exceptions. The independent significance of SAT is less well established. Recently, the roles of the CT-determined decrease of muscle mass and increased VAT area and epicardial adipose tissue (EAT) volume have been shown to predict a more debilitating course of illness in patients suffering from severe acute respiratory syndrome coronavirus 2 (COVID-19) infection. SUMMARY The field of CT-based body composition analysis is rapidly evolving and shows great potential for clinical implementation.
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Affiliation(s)
- Antti Tolonen
- Faculty of Medicine and Health Sciences, Tampere University, Kauppi Campus, Arvo Ylpön katu 34, 33520 Tampere, Finland.
| | - Tomppa Pakarinen
- Faculty of Medicine and Health Sciences, Tampere University, Kauppi Campus, Arvo Ylpön katu 34, 33520 Tampere, Finland; Department of Radiology, Tampere University Hospital, Elämänaukio, Kuntokatu 2, 33520 Tampere, Finland
| | - Antti Sassi
- Faculty of Medicine and Health Sciences, Tampere University, Kauppi Campus, Arvo Ylpön katu 34, 33520 Tampere, Finland; Department of Radiology, Tampere University Hospital, Elämänaukio, Kuntokatu 2, 33520 Tampere, Finland
| | - Jere Kyttä
- Faculty of Medicine and Health Sciences, Tampere University, Kauppi Campus, Arvo Ylpön katu 34, 33520 Tampere, Finland
| | - William Cancino
- Connectivity and Signal Processing Group, Universidad Industrial de Santander, Cl. 9 #Cra 27, Bucaramanga, Colombia
| | - Irina Rinta-Kiikka
- Faculty of Medicine and Health Sciences, Tampere University, Kauppi Campus, Arvo Ylpön katu 34, 33520 Tampere, Finland; Department of Radiology, Tampere University Hospital, Elämänaukio, Kuntokatu 2, 33520 Tampere, Finland
| | - Said Pertuz
- Connectivity and Signal Processing Group, Universidad Industrial de Santander, Cl. 9 #Cra 27, Bucaramanga, Colombia
| | - Otso Arponen
- Faculty of Medicine and Health Sciences, Tampere University, Kauppi Campus, Arvo Ylpön katu 34, 33520 Tampere, Finland; Department of Radiology, Tampere University Hospital, Elämänaukio, Kuntokatu 2, 33520 Tampere, Finland
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Ha J, Park T, Kim HK, Shin Y, Ko Y, Kim DW, Sung YS, Lee J, Ham SJ, Khang S, Jeong H, Koo K, Lee J, Kim KW. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci Rep 2021; 11:21656. [PMID: 34737340 PMCID: PMC8568923 DOI: 10.1038/s41598-021-00161-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/22/2021] [Indexed: 12/15/2022] Open
Abstract
As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38-3.10 cm2. A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.
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Affiliation(s)
- Jiyeon Ha
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Taeyong Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Hong-Kyu Kim
- Health Screening and Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Youngbin Shin
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Yousun Ko
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Dong Wook Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Yu Sub Sung
- Clinical Research Center, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea
| | - Jiwoo Lee
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Su Jung Ham
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Seungwoo Khang
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Heeryeol Jeong
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Kyoyeong Koo
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Jeongjin Lee
- School of Computer Science and Engineering, Soongsil University, Seoul, Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro, 43-gil, Songpa-gu, Seoul, 05505, Korea.
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Cespedes Feliciano EM, Popuri K, Cobzas D, Baracos VE, Beg MF, Khan AD, Ma C, Chow V, Prado CM, Xiao J, Liu V, Chen WY, Meyerhardt J, Albers KB, Caan BJ. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. J Cachexia Sarcopenia Muscle 2020; 11:1258-1269. [PMID: 32314543 PMCID: PMC7567141 DOI: 10.1002/jcsm.12573] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/26/2020] [Accepted: 03/11/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Body composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset. METHODS Among patients with non-metastatic colorectal (n = 3102) and breast (n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel-level image overlap using Jaccard scores and agreement between methods using intra-class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area. RESULTS Mean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra-class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1-2% versus manual analysis: mean differences were small at -2.35, -1.97 and -2.38 cm2 , respectively. ABACS's performance was lowest for the <2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00-1.52) versus 1.38 (95% CI: 1.11-1.70) for colorectal cancer patients and 1.30 (95% CI: 1.01-1.66) versus 1.29 (95% CI: 1.00-1.65) for breast cancer patients. CONCLUSIONS In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non-metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.
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Affiliation(s)
| | - Karteek Popuri
- School of Engineering, Simon Fraser University, Vancouver, Canada
| | - Dana Cobzas
- Department of Computing Science, MacEwan University, Edmonton, Canada
| | | | - Mirza Faisal Beg
- School of Engineering, Simon Fraser University, Vancouver, Canada
| | - Arafat Dad Khan
- School of Engineering, Simon Fraser University, Vancouver, Canada
| | - Cydney Ma
- School of Engineering, Simon Fraser University, Vancouver, Canada
| | - Vincent Chow
- School of Engineering, Simon Fraser University, Vancouver, Canada
| | - Carla M Prado
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada
| | - Jingjie Xiao
- Covenant Health Palliative Institute, Edmonton, Canada
| | - Vincent Liu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Wendy Y Chen
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Jeffrey Meyerhardt
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Kathleen B Albers
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Bette J Caan
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
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Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT. J Digit Imaging 2020; 32:241-250. [PMID: 30756268 DOI: 10.1007/s10278-019-00178-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Anthropometric parameters like muscle body mass (MBM), fat body mass (FBM), lean body mass (LBM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) are used in oncology. Our aim was to develop and evaluate the software Anthropometer3D measuring these anthropometric parameters on the CT of PET/CT. This software performs a multi-atlas segmentation of CT of PET/CT with extrapolation coefficients for the body parts beyond the usual acquisition range (from the ischia to the eyes). The multi-atlas database is composed of 30 truncated CTs manually segmented to isolate three types of voxels (muscle, fat, and visceral fat). To evaluate Anthropomer3D, a leave-one-out cross-validation was performed to measure MBM, FBM, LBM, VAT, and SAT. The reference standard was based on the manual segmentation of the corresponding whole-body CT. A manual segmentation of one CT slice at level L3 was also used. Correlations were analyzed using Dice coefficient, intra-class coefficient correlation (ICC), and Bland-Altman plot. The population was heterogeneous (sex ratio 1:1; mean age 57 years old [min 23; max 74]; mean BMI 27 kg/m2 [min 18; max 40]). Dice coefficients between reference standard and Anthropometer3D were excellent (mean+/-SD): muscle 0.95 ± 0.02, fat 1.00 ± 0.01, and visceral fat 0.97 ± 0.02. The ICC was almost perfect (minimal value of 95% CI of 0.97). All Bland-Altman plot values (mean difference, 95% CI and slopes) were better for Anthropometer3D compared to L3 level segmentation. Anthropometer3D allows multiple anthropometric measurements based on an automatic multi-slice segmentation. It is more precise than estimates using L3 level segmentation.
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Popinat G, Cousse S, Goldfarb L, Becker S, Gardin I, Salaün M, Thureau S, Vera P, Guisier F, Decazes P. Sub-cutaneous Fat Mass measured on multislice computed tomography of pretreatment PET/CT is a prognostic factor of stage IV non-small cell lung cancer treated by nivolumab. Oncoimmunology 2019; 8:e1580128. [PMID: 31069139 DOI: 10.1080/2162402x.2019.1580128] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 12/11/2018] [Accepted: 01/07/2019] [Indexed: 12/26/2022] Open
Abstract
Introduction: Our aim was to explore the prognostic value of anthropometric parameters in patients treated with nivolumab for stage IV non-small cell lung cancer (NSCLC). Methods: We retrospectively included 55 patients with NSCLC treated by nivolumab with a pretreatment 18FDG positron emission tomography coupled with computed tomography (PET/CT). Anthropometric parameters were measured on the CT of PET/CT by in-house software (Anthropometer3D) allowing an automatic multi-slice measurement of Lean Body Mass (LBM), Fat Body Mass (FBM), Muscle Body Mass (MBM), Visceral Fat Mass (VFM) and Sub-cutaneous Fat Mass (SCFM). Clinical and tumor parameters were also retrieved. Receiver operator characteristics (ROC) analysis was performed and overall survival at 1 year was studied using Kaplan-Meier and Cox analysis. Results: FBM and SCFM were highly correlated (ρ = 0.99). In ROC analysis, only FBM, SCFM, VFM, body mass index (BMI) and metabolic tumor volume (MTV) had an area under the curve (AUC) significantly higher than 0.5. In Kaplan-Meier analysis using medians as cut-offs, prognosis was worse for patients with low SCFM (<5.69 kg/m2; p = 0.04, survivors 41% vs 75%). In Cox univariate analysis using continuous values, BMI (HR = 0.84, p= 0.007), SCFM (HR = 0.75, p = 0.003) and FBM (HR = 0.80, p= 0.004) were significant prognostic factors. In multivariate analysis using clinical parameters (age, gender, WHO performance status, number prior regimens) and SCFM, only SCFM was significantly associated with poor survival (HR = 0.75, p = 0.006). Conclusions: SCFM is a significant prognosis factor of stage IV NSCLC treated by nivolumab.
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Affiliation(s)
- Geoffrey Popinat
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France
| | - Stéphanie Cousse
- Department of Pulmonology, Thoracic Oncology, and Respiratory Intensive Care, Rouen University Hospital, Rouen, France
| | - Lucas Goldfarb
- Department of Nuclear Medicine, Hôpital Avicenne, Bobigny, France
| | - Stéphanie Becker
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France.,Faculty of Medicine, University of Rouen, Rouen, France
| | - Isabelle Gardin
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France.,Faculty of Medicine, University of Rouen, Rouen, France
| | - Mathieu Salaün
- Department of Pulmonology, Thoracic Oncology, and Respiratory Intensive Care, Rouen University Hospital, Rouen, France.,Faculty of Medicine, University of Rouen, Rouen, France
| | - Sébastien Thureau
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France.,Faculty of Medicine, University of Rouen, Rouen, France.,Department of Radiotherapy and Oncology, Henri Becquerel Cancer Center, Rouen, France
| | - Pierre Vera
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France.,Faculty of Medicine, University of Rouen, Rouen, France
| | - Florian Guisier
- Department of Pulmonology, Thoracic Oncology, and Respiratory Intensive Care, Rouen University Hospital, Rouen, France
| | - Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France.,Faculty of Medicine, University of Rouen, Rouen, France
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Ozola-Zālīte I, Mark EB, Gudauskas T, Lyadov V, Olesen SS, Drewes AM, Pukitis A, Frokjær JB. Reliability and validity of the new VikingSlice software for computed tomography body composition analysis. Eur J Clin Nutr 2018; 73:54-61. [DOI: 10.1038/s41430-018-0110-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 11/08/2017] [Accepted: 01/12/2018] [Indexed: 12/17/2022]
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