Journal of Zhejiang University SCIENCE  B

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Adverse impacts of body composition abnormalities on outcome after liver transplantation for hepatocellular carcinoma: Development of a multi-task classifier chain surrogate model


Author(s):  Junjie YIN1, 2*, Zhu LI3*, Jinyan CHEN1, Qingyang QUE1, Huigang LI1, Ruijie ZHAO1, Jianyong ZHUO6, Chiyu HE1, Wei SHEN1, Peiru ZHANG5, Yifan GU1, Chenghao CAO1, Jiaqi ZHENG1, Yuhang LI1, Rongsen WANG3, Zuyuan LIN1, Shengjun XU2, Xuyong WEI2, Shusen ZHENG4, Di LU3, Xiao XU1, 3, 4, 6

Affiliation(s):  1Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China 2Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou 310006, China 3School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou 310000, China 4NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310000, China 5The Fourth School of Clinical Medicine Zhejiang Chinese Medical University, Hangzhou 310003, China 6Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China

Corresponding email(s):  Di LU, zjuludi@zju.edu.cn Xiao XU, zjxu@zju.edu.cn

Key Words:  Hepatocellular carcinoma; Liver transplantation; Body composition; Multi-task learning


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Junjie YIN1,2*, Zhu LI3*, Jinyan CHEN1, Qingyang QUE1, Huigang LI1, Ruijie ZHAO1, Jianyong ZHUO6, Chiyu HE1, Wei SHEN1, Peiru ZHANG5, Yifan GU1, Chenghao CAO1, Jiaqi ZHENG1, Yuhang LI1, Rongsen WANG3, Zuyuan LIN1, Shengjun XU2, Xuyong WEI2, Shusen ZHENG4, Di LU3, Xiao XU1,3,4,6. Adverse impacts of body composition abnormalities on outcome after liver transplantation for hepatocellular carcinoma: Development of a multi-task classifier chain surrogate model[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2600062

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author="Junjie YIN1,2*, Zhu LI3*, Jinyan CHEN1, Qingyang QUE1, Huigang LI1, Ruijie ZHAO1, Jianyong ZHUO6, Chiyu HE1, Wei SHEN1, Peiru ZHANG5, Yifan GU1, Chenghao CAO1, Jiaqi ZHENG1, Yuhang LI1, Rongsen WANG3, Zuyuan LIN1, Shengjun XU2, Xuyong WEI2, Shusen ZHENG4, Di LU3, Xiao XU1,3,4,6",
journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.B2600062"
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%A Qingyang QUE1
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%A Jianyong ZHUO6
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%A Wei SHEN1
%A Peiru ZHANG5
%A Yifan GU1
%A Chenghao CAO1
%A Jiaqi ZHENG1
%A Yuhang LI1
%A Rongsen WANG3
%A Zuyuan LIN1
%A Shengjun XU2
%A Xuyong WEI2
%A Shusen ZHENG4
%A Di LU3
%A Xiao XU1
%A 3
%A 4
%A 6
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doi="https://doi.org/10.1631/jzus.B2600062"

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A1 - Qingyang QUE1
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A1 - Jianyong ZHUO6
A1 - Chiyu HE1
A1 - Wei SHEN1
A1 - Peiru ZHANG5
A1 - Yifan GU1
A1 - Chenghao CAO1
A1 - Jiaqi ZHENG1
A1 - Yuhang LI1
A1 - Rongsen WANG3
A1 - Zuyuan LIN1
A1 - Shengjun XU2
A1 - Xuyong WEI2
A1 - Shusen ZHENG4
A1 - Di LU3
A1 - Xiao XU1
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A1 - 6
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doi="https://doi.org/10.1631/jzus.B2600062"


Abstract: Background: Sarcopenia and adipose abnormalities are co-occurring risks in liver transplantation for hepatocellular carcinoma (HCC), yet reliance on computed tomography (CT) segmentation limits routine assessment. Thus, we aimed to quantify the cumulative prognostic burden of these correlated phenotypes and develop a multi-task classifier chain surrogate to predict them using standard clinical data. Methods: In a cohort of 409 HCC transplant recipients, we analyzed the impact of four CT-derived phenotypes on recurrence-free survival (RFS) and overall survival (OS). To predict these correlated comorbidities without CT, we designed a Multi-task Classifier Chain framework using 37 routine peri-transplant variables. This architecture was explicitly trained to exploit inter-phenotype dependencies, comparing performance against standard multi-task baselines. Results: All four phenotypes could independently predict poor outcomes. A profound "dose-response" relationship was observed: 5-year OS dropped from 77.3% ( ≤1 abnormality) to 19.6% (≥3 abnormalities; P<0.0001). In the test set, the Classifier Chain framework significantly outperformed baselines by capturing inter-phenotype dependencies. It achieved high discrimination for sarcopenia (area under the receiver operating characteristic curve (AUC), 0.88), myosteatosis (AUC 0.88), visceral obesity (AUC 0.87), and high subcutaneous adipose tissue density (SATD) (AUC 0.79), yielding the greatest net clinical benefit. Conclusions: Body composition abnormalities exert a severe, additive negative impact on post-transplant outcomes. By modeling inter-phenotype dependencies, our multi-task classifier chain provides an accurate, non-invasive surrogate, enabling robust risk stratification using routine clinical data alone.

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On-line Access: 2026-07-13

Received: 2026-02-01

Revision Accepted: 2026-06-01

Crosschecked: 0000-00-00

Cited: 0

Clicked: 9

Citations:  Bibtex RefMan EndNote GB/T7714

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