Pediatric surgery international · 2026
Transfer learning prediction of 2-year native liver survival in biliary atresia following Kasai portoenterostomy using early perioperative biochemical data up to 3 months postoperatively.
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Xie Chuanping, Yan Wenqing, Zhao Yong, Li Shuangshuang, Liao Junmin, Zhang Yanan, Hua Kaiyun, Gu Yichao, Wang Peize, Sun Jie, Jin Yuyan, Liu Zhaozhou, Sun Dayan, Wang Dingding, Huang Jinshi
Аннотация
BACKGROUND: Biliary atresia (BA), a rare but life-threatening neonatal cholestatic disorder, necessitates Kasai portoenterostomy (KPE) as the primary surgical intervention. However, despite timely KPE within the recommended 60-day window, approximately 60% of infants ultimately require liver transplantation before age 2. Current prognostic tools lack the capacity to predict long-term native liver survival (NLS) using early perioperative biomarkers.
METHODS: The study subjects were BA patients treated by a senior KPE surgeon at our center (93 cases) and another senior KPE surgeon at Jiangxi Children's Hospital (61 cases) from January 2017 to December 2022. Clinical and pathological follow-up data were collected. Five traditional machine learning (ML) algorithms (LRM, DET, MLP, SVC, RF) were trained on preoperative, intraoperative, and postoperative biomarkers up to 3 months post-KPE, encompassing biochemical profiles. The best-performing RF model was further enhanced using transfer learning (TL).
RESULTS: For a combined cohort of 154 patients, traditional statistical analysis revealed significant differences in perioperative factors between NLS and non-NLS groups, with multivariate logistic regression identifying surgical age (P = 0.029), globular proteins at 1 week postoperatively (GLO_1W, P < 0.001), aspartate aminotransferase at 1 month postoperatively (AST_1M, P = 0.007), albumin at 3 months postoperatively (ALB_3M, P = 0.029), gamma-glutamyl transpeptidase at 3 months postoperatively (GGT_3M, P < 0.001), direct bilirubin at 3 months postoperatively (DBIL_3M, P = 0.005), and prealbumin at 3 months postoperatively (PALB_3M, P = 0.004) as independent risk factors. The five traditional ML models showed varying performance, with RF demonstrating the highest average AUC in the training set (AUC = 0.795, 95% CI 0.709-0.881). The TL model, built on the RF base, achieved superior performance in the training set (AUC = 0.888, 95% CI 0.819-0.943) and validation set (AUC = 0.856, 95% CI 0.738-0.947). GGT_3M, ALB_3M, DBIL_3M, DBIL_1M and GLO_1W were identified as the most critical predictive features. A TL-based clinical decision support system was deployed, providing dynamic visualization of individualized 24-month NLS trajectories.
CONCLUSION: Early perioperative biochemical data-driven ML models, enhanced by transfer learning, enable accurate prediction of 2-year NLS in BA. GGT_3M, ALB_3M, DBIL_3M, DBIL_1M, and GLO_1W are among the most influential predictors for 2-year NLS after KPE procedure.
METHODS: The study subjects were BA patients treated by a senior KPE surgeon at our center (93 cases) and another senior KPE surgeon at Jiangxi Children's Hospital (61 cases) from January 2017 to December 2022. Clinical and pathological follow-up data were collected. Five traditional machine learning (ML) algorithms (LRM, DET, MLP, SVC, RF) were trained on preoperative, intraoperative, and postoperative biomarkers up to 3 months post-KPE, encompassing biochemical profiles. The best-performing RF model was further enhanced using transfer learning (TL).
RESULTS: For a combined cohort of 154 patients, traditional statistical analysis revealed significant differences in perioperative factors between NLS and non-NLS groups, with multivariate logistic regression identifying surgical age (P = 0.029), globular proteins at 1 week postoperatively (GLO_1W, P < 0.001), aspartate aminotransferase at 1 month postoperatively (AST_1M, P = 0.007), albumin at 3 months postoperatively (ALB_3M, P = 0.029), gamma-glutamyl transpeptidase at 3 months postoperatively (GGT_3M, P < 0.001), direct bilirubin at 3 months postoperatively (DBIL_3M, P = 0.005), and prealbumin at 3 months postoperatively (PALB_3M, P = 0.004) as independent risk factors. The five traditional ML models showed varying performance, with RF demonstrating the highest average AUC in the training set (AUC = 0.795, 95% CI 0.709-0.881). The TL model, built on the RF base, achieved superior performance in the training set (AUC = 0.888, 95% CI 0.819-0.943) and validation set (AUC = 0.856, 95% CI 0.738-0.947). GGT_3M, ALB_3M, DBIL_3M, DBIL_1M and GLO_1W were identified as the most critical predictive features. A TL-based clinical decision support system was deployed, providing dynamic visualization of individualized 24-month NLS trajectories.
CONCLUSION: Early perioperative biochemical data-driven ML models, enhanced by transfer learning, enable accurate prediction of 2-year NLS in BA. GGT_3M, ALB_3M, DBIL_3M, DBIL_1M, and GLO_1W are among the most influential predictors for 2-year NLS after KPE procedure.