Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs
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 o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/2/205 |
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| author | Pierre Decazes Samy Ammari Antoine De Prévia Léo Mottay Littisha Lawrance Younes Belkouchi Baya Benatsou Laurence Albiges Corinne Balleyguier Pierre Vera Nathalie Lassau |
| author_facet | Pierre Decazes Samy Ammari Antoine De Prévia Léo Mottay Littisha Lawrance Younes Belkouchi Baya Benatsou Laurence Albiges Corinne Balleyguier Pierre Vera Nathalie Lassau |
| author_sort | Pierre Decazes |
| collection | DOAJ |
| description | 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, <i>p</i> = 0.02, cutoff = 0.72 kg/m<sup>2</sup>), SFM3D (AUC = 0.544, <i>p</i> = 0.047, cutoff = 3.05 kg/m<sup>2</sup>), FBM3D (AUC = 0.550, <i>p</i> = 0.03, cutoff = 4.32 kg/m<sup>2</sup>) and MBM3D (AUC = 0.565, <i>p</i> = 0.007, cutoff = 5.47 kg/m<sup>2</sup>), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, <i>p</i> = 0.034). In log-rank tests, low VFM3D (<i>p</i> = 0.014), low SFM3D (<i>p</i> < 0.0001), low FBM3D (<i>p</i> = 0.00019) and low VFA2D (<i>p</i> = 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. |
| first_indexed | 2024-03-09T13:03:27Z |
| format | Article |
| id | doaj.art-131162e177c8470cbf86d9e6830ac4b5 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T13:03:27Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-131162e177c8470cbf86d9e6830ac4b52023-11-30T21:51:26ZengMDPI AGDiagnostics2075-44182023-01-0113220510.3390/diagnostics13020205Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic DrugsPierre Decazes0Samy Ammari1Antoine De Prévia2Léo Mottay3Littisha Lawrance4Younes Belkouchi5Baya Benatsou6Laurence Albiges7Corinne Balleyguier8Pierre Vera9Nathalie Lassau10Department of Medical Imaging and Nuclear Medicine, Henri Becquerel Cancer Center, 76038 Rouen, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceQuantIF-LITIS (EA [Equipe d’ Accueil] 4108), Faculty of Medicine, University of Rouen, 76000 Rouen, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceDepartment of Cancer Medicine, Gustave Roussy Cancer Campus, Université Paris-Saclay, 94800 Villejuif, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceDepartment of Medical Imaging and Nuclear Medicine, Henri Becquerel Cancer Center, 76038 Rouen, FranceBiomaps, UMR1281 INSERM, CEA, CNRS, University of Paris-Saclay, 94805 Villejuif, FranceBackground: 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, <i>p</i> = 0.02, cutoff = 0.72 kg/m<sup>2</sup>), SFM3D (AUC = 0.544, <i>p</i> = 0.047, cutoff = 3.05 kg/m<sup>2</sup>), FBM3D (AUC = 0.550, <i>p</i> = 0.03, cutoff = 4.32 kg/m<sup>2</sup>) and MBM3D (AUC = 0.565, <i>p</i> = 0.007, cutoff = 5.47 kg/m<sup>2</sup>), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, <i>p</i> = 0.034). In log-rank tests, low VFM3D (<i>p</i> = 0.014), low SFM3D (<i>p</i> < 0.0001), low FBM3D (<i>p</i> = 0.00019) and low VFA2D (<i>p</i> = 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.https://www.mdpi.com/2075-4418/13/2/205body compositiondeep learningangiogenesis inhibitorcomputed tomographymuscleadipose tissue |
| spellingShingle | Pierre Decazes Samy Ammari Antoine De Prévia Léo Mottay Littisha Lawrance Younes Belkouchi Baya Benatsou Laurence Albiges Corinne Balleyguier Pierre Vera Nathalie Lassau Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs Diagnostics body composition deep learning angiogenesis inhibitor computed tomography muscle adipose tissue |
| title | Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs |
| title_full | Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs |
| title_fullStr | Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs |
| title_full_unstemmed | Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs |
| title_short | Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs |
| title_sort | body composition to define prognosis of cancers treated by anti angiogenic drugs |
| topic | body composition deep learning angiogenesis inhibitor computed tomography muscle adipose tissue |
| url | https://www.mdpi.com/2075-4418/13/2/205 |
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