Artificial Intelligence-Driven Body Composition Analysis Enhances Chemotherapy Toxicity Prediction in Colorectal Cancer.
Authors
Affiliations (5)
Affiliations (5)
- Department of Statistics, National Cheng Kung University, No. 1, University Road, East District, Tainan 701, Taiwan. Electronic address: [email protected].
- Department of Statistics, National Cheng Kung University, No. 1, University Road, East District, Tainan 701, Taiwan. Electronic address: [email protected].
- Department of Statistics, National Cheng Kung University, No. 1, University Road, East District, Tainan 701, Taiwan. Electronic address: [email protected].
- Department of Medicine, College of Medicine, National Cheng Kung University, No. 1, University Road, East District, Tainan 701, Taiwan; Department of Pharmacology, College of Medicine, National Cheng Kung University, No. 1, University Road, East District, Tainan 701, Taiwan. Electronic address: [email protected].
- Clinical Innovation and Research Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138, Sheng Li Road, North District, Tainan 704, Taiwan; Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138, Sheng Li Road, North District, Tainan 704, Taiwan. Electronic address: [email protected].
Abstract
Body surface area (BSA)-based chemotherapy dosing remains standard despite its limitations in predicting toxicity. Variations in body composition, particularly skeletal muscle and adipose tissue, influence drug metabolism and toxicity risk. This study aims to investigate the mediating role of body composition in the relationship between BSA-based dosing and dose-limiting toxicities (DLTs) in colorectal cancer patients receiving oxaliplatin-based chemotherapy. We retrospectively analyzed 483 stage III colorectal cancer patients treated at National Cheng Kung University Hospital (2013-2021). An artificial intelligence (AI)-driven algorithm quantified skeletal muscle and adipose tissue compartments from lumbar 3 (L3) vertebral-level computed tomography (CT) scans. Mediation analysis evaluated body composition's role in chemotherapy-related toxicities. Among the cohort, 18.2% (n = 88) experienced DLTs. While BSA alone was not significantly associated with DLTs (OR = 0.473, p = 0.376), increased intramuscular adipose tissue (IMAT) significantly predicted higher DLT risk (OR = 1.047, p = 0.038), whereas skeletal muscle area was protective. Mediation analysis confirmed that IMAT partially mediated the relationship between BSA and DLTs (indirect effect: 0.05, p = 0.040), highlighting adipose infiltration's role in chemotherapy toxicity. BSA-based dosing inadequately accounts for interindividual variations in chemotherapy tolerance. AI-assisted body composition analysis provides a precision oncology framework for identifying high-risk patients and optimizing chemotherapy regimens. Prospective validation is warranted to integrate body composition into routine clinical decision-making.