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F-Chain: personalized overall survival prediction based on incremental adaptive indicators and multi-source clinical records

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Abstract

The abundance of biomarkers across histology, imaging, and clinical endpoints poses a challenge in selecting indicators for personalized clinical decision support. Patient heterogeneity necessitates an adaptive and incremental approach to indicator selection, leading to complex demands due to missing data. To address these challenges, we propose Forest Chain (F-Chain), a learning framework that incrementally selects prognostic indicators for each patient. Using a proposed surrogate preference function, F-Chain achieves consistent evaluations across multiple doctors and data sources. We introduce an indicator selection strategy that integrates data information, gradually adding relevant indicators. Additionally, we develop a missingness-incorporated decision tree for predicting outcomes on multi-source datasets with substantial missing values. We validate the F-Chain model using the SEER database and real clinical data from a hospital, demonstrating superior OS prediction results compared to state-of-the-art methods.

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Acknowledgements

This work was supported by Joint Medical-Industrial Intersection Fundation of Dalian University of Technology (DUT23YG204), the National Natural Science Foundation of China (62006035), Dalian Science and Technology Innovation Foundation (2023JJ13SN065), and the Fundamental Research Funds for the Central Universities (DUT22RC(3)011).

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Author notes

  1. Qiucen Li and Zedong Du contributed equally to this work and should be regarded as co-first authors.

Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, 116620, Liaoning, China

    Qiucen Li, Huicen Guo, Dan Lin & Zhikui Chen

  2. Department of Oncology, Chengdu Second People’s Hospital, Chengdu, 610017, Sichuan, China

    Zedong Du

  3. Division of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China

    Qiu Li & Pengfei Zhang

  4. School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, NSW, 2640, Australia

    Xiaodi Huang

Authors
  1. Qiucen Li
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  3. Qiu Li
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Contributions

Qiucen Li and Zedong Du contributed equally to this work and should be regarded as co-first authors. Qiucen L. and Z.D. performed conceptualization, methodology, formal analysis, investigation, and writing, including the original draft, review, and editing. Qiu.L. and P.Z. were responsible for supervision and data curation. H.G. verified the code and methods, and conducted data preprocessing. X.H. visualized the experimental results. D.L. verified the code and methods, and revised the manuscript. Z.C. provided funding support for the entire project and was responsible for project management and supervision. All authors reviewed the manuscript.

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Correspondence to Zhikui Chen.

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Li, Q., Du, Z., Li, Q. et al. F-Chain: personalized overall survival prediction based on incremental adaptive indicators and multi-source clinical records. Memetic Comp. 16, 269–284 (2024). https://doi.org/10.1007/s12293-024-00415-5

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