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Clinical Studies

Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study

British Journal of Cancer (2024)Cite this article

Subjects

Abstract

Importance

Intra-arterial therapies(IATs) are promising options for unresectable hepatocellular carcinoma(HCC). Stratifying the prognostic risk before administering IAT is important for clinical decision-making and for designing future clinical trials.

Objective

To develop and validate a machine learning(ML)-based decision support model(MLDSM) for recommending IAT modalities for unresectable HCC.

Design, setting, and participants

Between October 2014 and October 2022, a total of 2,959 patients with HCC who underwent initial IATs were enroled retrospectively from 13 tertiary hospitals. These patients were divided into the training cohort (n = 1700), validation cohort (n = 428), and test cohort (n = 200).

Main outcomes and measures

Thirty-two clinical variables were input, and five supervised ML algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM) and Random Forest (RF), were compared using the areas under the receiver operating characteristic curve (AUC) with the DeLong test.

Results

A total of 1856 patients were assigned to the IAT alone Group(I-A), and 1103 patients were assigned to the IAT combination Group(I-C). The 12-month death rates were 31.9% (352/1103) in the I-A group and 50.4% (936/1856) in the I-C group. For the test cohort, in the I-C group, the CatBoost model achieved the best discrimination when 30 variables were input, with an AUC of 0.776 (95% confidence intervals [CI], 0.833–0.868). In the I-A group, the LGBM model achieved the best discrimination when 24 variables were input, with an AUC of 0.776 (95% CI, 0.833–0.868). According to the decision trees, BCLC grade, local therapy, and diameter as top three variables were used to guide clinical decisions between IAT modalities.

Conclusions and relevance

The MLDSM can accurately stratify prognostic risk for HCC patients who received IATs, thus helping physicians to make decisions about IAT and providing guidance for surveillance strategies in clinical practice.

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Fig. 1: Enrolment pathway of unresectable HCC patients who underwent various IAT schemes.
Fig. 2: ROC comparison between five ML models.
Fig. 3: SHAP values of individual features of ML models.
Fig. 4: Risk strata for predicting the prognosis of HCC patients who underwent IATs.
Fig. 5: Using MLSDM, top divergence variables were used in classifying treatment selection.

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Data availability

The in-house developed medical database of this study is publicly accessible at: http://www.yunedc.cn/#/login. In addition, we also provided the codes of development of the ML based model are available in opensource repositories (https://github.com/tyewu/DiseasePredict) for the convenience of public use.

Code availability

In addition, we also provided the codes of development of the ML based model are available in opensource repositories (https://github.com/tyewu/DiseasePredict) for the convenience of public use.

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Funding

Beijing Municipal Education Commission Science and Technology Project KM202010025005; The capital health research and development of special (2022- 2-7083); Beijing Municipal Natural Science Foundation7222100; Tongzhou District Science and Technology Commission Project KJ2022CX021; Beijing Xisike Clinical Oncology Research Foundation Y-Young2024-0090. This article was supported by the National Natural Science Foundation of China (NSFC No. 82272101) and the Natural Science Foundation of Shandong Province (No. ZR2021MH060).

Author information

Author notes

  1. These authors contributed equally: Chao An, Ran Wei.

Authors and Affiliations

  1. Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China

    Chao An & Dong Yan

  2. Department of Minimal Invasive intervention, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China

    Chao An, Mengxuan Zuo, Wang Li & Peihong Wu

  3. Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat sen University, Guangzhou, 510080, Province Guangdong, China

    Ran Wei

  4. Department of Interventional therapy, Guangdong Provincial Hospital of Chinese Medicine and Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, 510080, Province Guangdong, China

    Wendao Liu

  5. Department 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, 100021, China

    Yan Fu & Jianjun Han

  6. Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Interventional Radiology Department, No. 440, Jiyan Road, Jinan, Shandong Province Jinan, Shandong, China

    Xiaolong Gong

  7. Department of Interventional Radiology and Vascular Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, 510060, China

    Chengzhi Li

  8. DHC Mediway Technology Co., Ltd., Beijing, 100190, China

    Wang Yao, Yansheng Li, Fatian Wu & Kejia Liu

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  1. Chao An
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Contributions

Conception and design: Wendao Liu, Chao An. Development of methodology: Ran Wei,Wang Li. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Yan Fu, Xiaolong Gong,Wang Yao, Chengzhi Li. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Yansheng Li, Fatian Wu, Kejia Liu. Writing, review, and/or revision of the manuscript: Chao An, Mengxuan Zuo. Administrative, technical, or material support (i.e., reporting or organising data, constructing databases): Chengzhi Li, Dong Yan, Jianjun Han. Study supervision: Peihong Wu.

Corresponding authors

Correspondence to Dong Yan, Peihong Wu or Jianjun Han.

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The authors declare no competing interests.

Ethical approval

This retrospective study was approved from the Institutional Review Board of National Cancer Centre (NCC-010298) and was conducted following the principles of the Declaration of Helsinki. The requirement for written informed consent was waived because of the retrospective nature of the study.

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An, C., Wei, R., Liu, W. et al. Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02784-7

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