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Clinical Studies

A histopathology-based artificial intelligence system assisting the screening of genetic alteration in intrahepatic cholangiocarcinoma

British Journal of Cancer volume 132, pages 195–202 (2025)Cite this article

Subjects

Abstract

Background

Targeted therapy for intrahepatic cholangiocarcinoma (ICC) shows superior survival outcomes but patients with certain targetable alterations are no more than 20%. Genetic alteration screening for all ICC patients is of high cost and not routinely performed. This study intends to develop a histopathology-based artificial intelligence (AI)-assisted system for predicting genetic alteration of ICC.

Methods

We constructed a Genetic Alteration Prediction (GAP) system based on multi-instance learning and self-supervised learning to predict genetic alterations using whole-slide images (WSIs) of H&E-stained slides. A total of 2069 WSIs from 232 ICC patients underwent surgery of the FAH-SYSU dataset were used for model construction and adjustment by five-fold cross-validation. Another 150 patients from three medical centres were used as independent external validations. We also compared the cost-effectiveness of GAP-assisted precise treatment and all-sequencing strategy to non-sequencing strategy.

Results

The GAP was able to predict actionable genetic alterations of ICC, including FGFR2 and IDH. The area under the receiver operating characteristic curves (AUC) for FGFR2 and IDH were 0.754 and 0.713 in the internal dataset, and 0.724 and 0.656 in the external dataset, respectively. Furthermore, compared to giving chemotherapy without sequencing for every patient, GAP-assisted precise treatment could increase 1 progression-free quality-adjusted life month with a cost of $13871.72, the co-responding figure for all-sequencing strategy is $44538.93. Decision curve analysis showed that AI-assisted strategy provides better clinical benefits.

Conclusions

We constructed an AI-assisted genetic alteration screening system which is predictable to ICC actionable targets and has potential to assist precise targeted treatment of advanced ICC.

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Fig. 1: Datasets and clinical application scenario for the ICC GAP system.
Fig. 2: Optimisation of the GAP system.
Fig. 3: Performance of the GAP system for genetic alteration prediction in the FAH-SYSU dataset and the SSP dataset.
Fig. 4: Clustering and visualisation of the alteration-related features.
Fig. 5: Clinical efficiency of the GAP system.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank all participants for their endeavour and contribution in this study. We thank all medical centres involved for their support of this research project.

Funding

The work was supported by the National Key Research and Development Program of China (No. 2020AAA0109504), the National Science Foundation for Distinguished Young Scholars (Nos. 81825013, 82322034), the National Natural Science Foundation of China (Nos. 82272942, 82302283, 82172047), the Guangdong Natural Science Foundation of Distinguished Youth Scholar (No. 2022B1515020060), the Natural Science Foundation of Guangdong Province (No. 2021A1515010450), and the Kelin Outstanding Young Scientist of the First Affiliated Hospital, Sun Yat-sen University (No. R08030).

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Author notes

  1. These authors contributed equally: Han Xiao, Jianping Wang, Zongpeng Weng.

Authors and Affiliations

  1. Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Han Xiao & Shuling Chen

  2. Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Jianping Wang, Xiaoxuan Lin & Sui Peng

  3. Department of Biology and Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

    Zongpeng Weng

  4. Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Man Shu & Lihong Wei

  5. State Key Laboratory of Oncology in Southern China, Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China

    Jingxian Shen

  6. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China

    Peng Sun, Muyan Cai & Jingping Yun

  7. Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China

    Peng Sun, Muyan Cai & Jingping Yun

  8. Center of Hepato‑Pancreato‑Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Xiao Xiang, Jiaming Lai & Ming Kuang

  9. Department of Hepatobiliary Surgery, Peking University People’s Hospital, Beijing, China

    Xiao Xiang

  10. Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Bin Li & Sui Peng

  11. Department of CSE, University of Notre Dame, Notre Dame, IN, USA

    Yiyu Shi

  12. Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Shuling Chen & Sui Peng

Authors
  1. Han Xiao
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  2. Jianping Wang
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  3. Zongpeng Weng
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  15. Jingping Yun
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  17. Sui Peng
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Contributions

Project administration: Peng S; Conceptualisation: Xiao H, Wang. JP, Weng ZP, Chen SL and Peng S; Data curation: Xiao H, Wang. JP, Weng ZP, Shu M, Shen JX, Sun P, Cai MY, Xiang X, Wei LH, Lai JM and Yun JP; Writing—original draft: Xiao H, Wang JP and Weng ZP; Writing—review & editing: Xiao H, Wang JP, Chen SL, Kuang M and Peng S; Formal analysis: Weng ZP, Lin XX, Li B and Shi YY; Methodology: Weng ZP, Lin XX, Li B and Shi YY; Investigation: Xiao H, Wang JP, Chen SL and Peng S; Resources: Shi YY, Kuang M and Peng S; Validation: Shen JX, Sun P, Cai MY, Xiang X, Lai JM and Yun JP; Supervision: Yun JP, Chen SL and Peng S; Funding acquisition: Xiao H, Kuang M, Chen SL and Peng S.

Corresponding authors

Correspondence to Jingping Yun, Shuling Chen or Sui Peng.

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Competing interests

The authors declare no conflict of interest.

Ethical approval

This study was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The protocol was reviewed and approved by Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University, approval number No. [2023]107.

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Informed consent was waived since it’s a retrospective study.

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Xiao, H., Wang, J., Weng, Z. et al. A histopathology-based artificial intelligence system assisting the screening of genetic alteration in intrahepatic cholangiocarcinoma. Br J Cancer 132, 195–202 (2025). https://doi.org/10.1038/s41416-024-02910-5

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