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Peer-reviewedCC BY-NC-ND 4.0NPJ Precision Oncology

AI in predicting the macrotrabecular-massive subtype of HCC and informing treatment selection: a multi-center and prospective validation study

  • Ran Wei1
  • Hanyu Jiang2
  • Mengxuan Zuo3,4,5
  • Xuelei He6
  • Fei Cao3,4,5
  • Bin Song2
  • Shaolong Li4,5,7
  • Wang Li3,4,5
  • Wendao Liu8
  • Chengzhi Li9
  • Xin Li10
  • Jianjun Han11
6 more authors
  • Yan Fu12
  • Dong Yan13
  • Weiling He14
  • Feng Duan10
  • Xinya Zhao15
  • Chao An3,4,5,10
15 affiliations
  1. 1Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
  2. 2Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
  3. 3Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
  4. 4State Key Laboratory of Oncology in South China, Guangzhou, China
  5. 5Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China
  6. 6Shcool of Electronic Information, Nortwest University, Xi’an, China
  7. 7Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
  8. 8Department of Interventional Therapy, Guangdong Provincial Hospital of Chinese Medicine and Guangdong Provincial Academy of Chinese Medical Sciences, Guangdong, China
  9. 9Department of Interventional Radiology and Vascular Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
  10. 10Department of Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
  11. 11Interventional Radiology Department, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an, China
  12. 12Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
  13. 13Department of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China
  14. 14Department of Gastrointestinal Surgery, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
  15. 15Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Abstract

The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is associated with aggressive biology and poor prognosis. We aimed to develop a CT-based artificial intelligence model (DeepCT-MTM) for the noninvasive prediction of MTM-HCC and investigate its prognostic utilities as well as biological underpinnings. A total of 3118 patients with HCC were included from 20 tertiary-care hospitals. DeepCT-MTM was developed and validated among 832 patients with early-stage HCC undergoing resection (the resection set) and extrapolated to 2286 patients (including 480 prospectively-collected ones) with intermediate/advanced-stage HCC receiving IATs. DeepCT-MTM’s predictive performance for MTM-HCC was evaluated using the area under the receiver operating characteristic curve (AUC), and its prognostic values were investigated for progression-free survival (PFS) and overall survival (OS). In the external test cohort of the resection set, DeepCT-MTM predicted MTM-HCC with an AUC of 0.845. The DeepCT-MTM-predicted high-risk group had worse PFS and OS across all IAT sets (all P < 0.05).. DeepCT-MTM is effective for noninvasively predicting MTM-HCC and may help selecting patients who benefit from a combination of IAT with immunotherapy and anti-angiogenic therapy. However, prospective validations are warranted for these hypothesis-generating findings.

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Cite

BibTeXRIS
BibTeX
@article{wei2026predicting,
  title = {{AI in predicting the macrotrabecular-massive subtype of HCC and informing treatment selection: a multi-center and prospective validation study}},
  author = {Ran Wei and Hanyu Jiang and Mengxuan Zuo and Xuelei He and Fei Cao and Bin Song and Shaolong Li and Wang Li and Wendao Liu and Chengzhi Li and Xin Li and Jianjun Han and Yan Fu and Dong Yan and Weiling He and Feng Duan and Xinya Zhao and Chao An},
  journal = {NPJ Precision Oncology},
  year = {2026},
  month = jul,
  doi = {10.1038/s41698-026-01607-7},
  url = {https://doi.org/10.1038/s41698-026-01607-7},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - AI in predicting the macrotrabecular-massive subtype of HCC and informing treatment selection: a multi-center and prospective validation study
AU  - Ran Wei
AU  - Hanyu Jiang
AU  - Mengxuan Zuo
AU  - Xuelei He
AU  - Fei Cao
AU  - Bin Song
AU  - Shaolong Li
AU  - Wang Li
AU  - Wendao Liu
AU  - Chengzhi Li
AU  - Xin Li
AU  - Jianjun Han
AU  - Yan Fu
AU  - Dong Yan
AU  - Weiling He
AU  - Feng Duan
AU  - Xinya Zhao
AU  - Chao An
PY  - 2026
DA  - 2026/07/11
JO  - NPJ Precision Oncology
DO  - 10.1038/s41698-026-01607-7
UR  - https://doi.org/10.1038/s41698-026-01607-7
AB  - The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is associated with aggressive biology and poor prognosis. We aimed to develop a CT-based artificial intelligence model (DeepCT-MTM) for the noninvasive prediction of MTM-HCC and investigate its prognostic utilities as well as biological underpinnings. A total of 3118 patients with HCC were included from 20 tertiary-care hospitals. DeepCT-MTM was developed and validated among 832 patients with early-stage HCC undergoing resection (the resection set) and extrapolated to 2286 patients (including 480 prospectively-collected ones) with intermediate/advanced-stage HCC receiving IATs. DeepCT-MTM’s predictive performance for MTM-HCC was evaluated using the area under the receiver operating characteristic curve (AUC), and its prognostic values were investigated for progression-free survival (PFS) and overall survival (OS). In the external test cohort of the resection set, DeepCT-MTM predicted MTM-HCC with an AUC of 0.845. The DeepCT-MTM-predicted high-risk group had worse PFS and OS across all IAT sets (all P < 0.05).. DeepCT-MTM is effective for noninvasively predicting MTM-HCC and may help selecting patients who benefit from a combination of IAT with immunotherapy and anti-angiogenic therapy. However, prospective validations are warranted for these hypothesis-generating findings.
SN  - 2397-768X
ER  -