HPB : the official journal of the International Hepato Pancreato Biliary Association · 2026
Optimizing graft weight prediction in living donor liver transplantation: a machine learning approach integrating pathologic, radiological and biological factors.
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Yin Shih-Min, Chuang Po-Chun, Lin Yu-Hung, Chih-Yi Chen Itsuko, Cheng Yu-Fan, Guo Shu-Mei, Liu Ting-Ting, Chen Chao-Long, James Lien Jenn-Jier, Ou Hsin-You
Аннотация
BACKGROUND: Graft weight (GW) prediction is essential in living donor liver transplantation (LDLT) to mitigate the risks associated with small-for-size grafts. This study aimed to develop and validate a machine learning (ML)-based model integrating clinical, biochemical, radiological, and histopathological features to enhance GW prediction accuracy.
MATERIALS AND METHODS: This study included 1001 LDLT donors between 2014 and 2023. Preoperative clinical data, graft volume (GV), radiological and histological steatosis grading were analysed. Multiple models were developed using an 80% training and 20% test split. Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) and compared with conventional GW prediction methods.
RESULTS: The ML model demonstrated superior performance, with an MAE of 48.51 g, an RMSE of 65.12 g, and a MAPE of 8.49%. Compared with traditional methods, the ML approach significantly reduced prediction errors ≥15% (10.94% vs. 37.31% for conventional models, p < 0.001). Feature analysis highlighted GV, donor anthropometrics, lipid profiles, and CT attenuation values as key predictors, while hepatic steatosis had minimal impact.
CONCLUSIONS: ML-based GW prediction outperformed conventional methods by incorporating radiological and biochemical parameters, achieving greater accuracy and reducing clinically significant errors.
MATERIALS AND METHODS: This study included 1001 LDLT donors between 2014 and 2023. Preoperative clinical data, graft volume (GV), radiological and histological steatosis grading were analysed. Multiple models were developed using an 80% training and 20% test split. Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) and compared with conventional GW prediction methods.
RESULTS: The ML model demonstrated superior performance, with an MAE of 48.51 g, an RMSE of 65.12 g, and a MAPE of 8.49%. Compared with traditional methods, the ML approach significantly reduced prediction errors ≥15% (10.94% vs. 37.31% for conventional models, p < 0.001). Feature analysis highlighted GV, donor anthropometrics, lipid profiles, and CT attenuation values as key predictors, while hepatic steatosis had minimal impact.
CONCLUSIONS: ML-based GW prediction outperformed conventional methods by incorporating radiological and biochemical parameters, achieving greater accuracy and reducing clinically significant errors.