Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer

• Wenhao Lu ^[3] ; Rongbin Zhou ^[3] ; Xiao Li ^[3] ; Fubo Wang ^[3] ; Huiyong Zhang ^[1] ; Shaohua Chen ^[1] ; Chengbang Wang ^[1] ; Chunmeng Wei ^[1] ; Zuheng Wang ^[1] ; Lin Zhao ^[4] ; Shenfan Wang ^[4] ; Jin Ji ^[4] ; Kangxian Jiang ^[2] ; Xuedong Wei ^[5] ; Wenlei Hou ^[6]
  1. [1] Guangxi Medical University

     Guangxi Medical University

     China

     
  2. [2] Second Affiliated Hospital of Fujian Medical University

     Second Affiliated Hospital of Fujian Medical University

     China

     
  3. [3] Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed By the Province and Ministry, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning City 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China
  4. [4] Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, People’s Republic of China
  5. [5] Xuedong Wei
  6. [6] Information Technology School of Guangxi Police College, Nanning 530021, Guangxi, People’s Republic of China

  Mostrar afiliaciones +
• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 26, Nº. 9, 2024, págs. 2369-2379
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Purpose Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa).

    Materials and methods A total of 647 PCa patients were retrospectively evaluated. Clinical parameters were identified using LASSO regression. Then, cohort was split into training and validation datasets with a ratio of 0.75:0.25 and BCR-related features were included in Cox regression and five ML algorithm to construct BCR prediction models. The clinical utility of each model was evaluated by concordance index (C-index) values and decision curve analyses (DCA). Besides, Shapley Additive Explanation (SHAP) values were used to explain the features in the models.

    Results We identified 11 BCR-related features using LASSO regression, then establishing five ML-based models, including random survival forest (RSF), survival support vector machine (SSVM), survival Tree (sTree), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and a Cox regression model, C-index were 0.846 (95%CI 0.796–0.894), 0.774 (95%CI 0.712–0.834), 0.757 (95%CI 0.694–0.818), 0.820 (95%CI 0.765–0.869), 0.793 (95%CI 0.735–0.852), and 0.807 (95%CI 0.753–0.858), respectively. The DCA showed that RSF model had significant advantages over all models. In interpretability of ML models, the SHAP value demonstrated the tangible contribution of each feature in RSF model.

    Conclusions Our score system provide reference for the identification for BCR, and the crafting of a framework for making therapeutic decisions for PCa on a personalized basis