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Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

PURPOSE: Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 ver...

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Autores principales: Cao, Ke, Yeung, Josephine, Arafat, Yasser, Choi, CheukShan, Wei, Matthew Y. K., Chan, Steven, Lee, Margaret, Baird, Paul N., Yeung, Justin M. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590342/
https://www.ncbi.nlm.nih.gov/pubmed/37540253
http://dx.doi.org/10.1007/s00432-023-05227-7
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author Cao, Ke
Yeung, Josephine
Arafat, Yasser
Choi, CheukShan
Wei, Matthew Y. K.
Chan, Steven
Lee, Margaret
Baird, Paul N.
Yeung, Justin M. C.
author_facet Cao, Ke
Yeung, Josephine
Arafat, Yasser
Choi, CheukShan
Wei, Matthew Y. K.
Chan, Steven
Lee, Margaret
Baird, Paul N.
Yeung, Justin M. C.
author_sort Cao, Ke
collection PubMed
description PURPOSE: Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients. METHODS: 203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance. RESULTS: A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10(–3)). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50–0.54; males: AUC = 0.50–0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males). CONCLUSION: Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00432-023-05227-7.
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spelling pubmed-105903422023-10-23 Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy? Cao, Ke Yeung, Josephine Arafat, Yasser Choi, CheukShan Wei, Matthew Y. K. Chan, Steven Lee, Margaret Baird, Paul N. Yeung, Justin M. C. J Cancer Res Clin Oncol Research PURPOSE: Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients. METHODS: 203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance. RESULTS: A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10(–3)). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50–0.54; males: AUC = 0.50–0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males). CONCLUSION: Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00432-023-05227-7. Springer Berlin Heidelberg 2023-08-04 2023 /pmc/articles/PMC10590342/ /pubmed/37540253 http://dx.doi.org/10.1007/s00432-023-05227-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Cao, Ke
Yeung, Josephine
Arafat, Yasser
Choi, CheukShan
Wei, Matthew Y. K.
Chan, Steven
Lee, Margaret
Baird, Paul N.
Yeung, Justin M. C.
Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
title Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
title_full Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
title_fullStr Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
title_full_unstemmed Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
title_short Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
title_sort can ai-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590342/
https://www.ncbi.nlm.nih.gov/pubmed/37540253
http://dx.doi.org/10.1007/s00432-023-05227-7
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