Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer
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-checkpoi...
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BMJ Publishing Group
2023-09-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/11/9/e007315.full |
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| author | Caroline Robert Aurélien Marabelle Fabrice Barlesi Samy Ammari David Planchard Siham Farhane Paul-Henry Cournède Pierre Decazes Younes Belkouchi Léo Mottay Littisha Lawrance Antoine de Prévia Hugues Talbot Florian Guisier Tony Ibrahim Pierre Vera Nathalie Lassau |
| author_facet | Caroline Robert Aurélien Marabelle Fabrice Barlesi Samy Ammari David Planchard Siham Farhane Paul-Henry Cournède Pierre Decazes Younes Belkouchi Léo Mottay Littisha Lawrance Antoine de Prévia Hugues Talbot Florian Guisier Tony Ibrahim Pierre Vera Nathalie Lassau |
| author_sort | Caroline Robert |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-45c25158807d48c79b5b5abe405896a3 |
| institution | OA Journals |
| issn | 2051-1426 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | BMJ Publishing Group |
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| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-45c25158807d48c79b5b5abe405896a32025-08-20T02:11:29ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262023-09-0111910.1136/jitc-2023-007315Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancerCaroline Robert0Aurélien Marabelle1Fabrice Barlesi2Samy Ammari3David Planchard4Siham Farhane5Paul-Henry Cournède6Pierre Decazes7Younes Belkouchi8Léo Mottay9Littisha Lawrance10Antoine de Prévia11Hugues Talbot12Florian Guisier13Tony Ibrahim14Pierre Vera15Nathalie Lassau164 Department of Medical Oncology, Institut Gustave Roussy, Villejuif, FranceDrug Development Department, Gustave Roussy, Villejuif, Île-de-France, FranceHopital de la Timone, Marseille, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, FranceDepartment of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, FranceDépartement des Innovations Thérapeutiques et Essais Précoces, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, FranceMICS Lab, CentraleSupelec, Universite Paris-Saclay, 91190 Gif-Sur-Yvette, FranceQuantIF-LITIS (EA[Equipe d`Accueil] 4108), Faculty of Medicine, University of Rouen, 76000 Rouen, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, FranceDepartment of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, FranceUniversite Paris-Saclay, CentraleSupelec, Inria, Gif-sur-Yvette, FranceDepartment of Pneumology and Inserm CIC-CRB 1404, Normandie Univ, UNIROUEN, LITIS Lab QuantIF team EA4108, CHU Rouen, Rouen, FranceDepartment of Cancer Medicine, Gustave Roussy Cancer Campus, University of Paris-Saclay, 94800 Villejuif, FranceDepartment of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94800 Villejuif, FranceBackground 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.https://jitc.bmj.com/content/11/9/e007315.full |
| spellingShingle | Caroline Robert Aurélien Marabelle Fabrice Barlesi Samy Ammari David Planchard Siham Farhane Paul-Henry Cournède Pierre Decazes Younes Belkouchi Léo Mottay Littisha Lawrance Antoine de Prévia Hugues Talbot Florian Guisier Tony Ibrahim Pierre Vera Nathalie Lassau Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer Journal for ImmunoTherapy of Cancer |
| title | Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer |
| title_full | Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer |
| title_fullStr | Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer |
| title_full_unstemmed | Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer |
| title_short | Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer |
| title_sort | synergic prognostic value of 3d ct scan subcutaneous fat and muscle masses for immunotherapy treated cancer |
| url | https://jitc.bmj.com/content/11/9/e007315.full |
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