Scholarlink

<p>Gallbladder cancer (GBC) is globally rare but prevalent in India. GBC is often diagnosed at an advanced stage, leading to a poor outcome. The identification of key prognostic factors and accurate survival prediction are crucial to optimizing treatment strategies. Retrospective data from 698 patients with gallbladder cancer treated at Tata Medical Center, Kolkata were obtained from a published dataset. Different machine learning models were used to predict survival outcomes. The performance of the model was evaluated, and explainable AI techniques were used to interpret the model’s output and identify significant prognostic factors. The analysis identified elevated liver enzymes and bilirubin levels as significant prognostic factors. Advanced age has also been shown to be correlated with terminal disease outcomes. When comparing different modeling systems, the stacking model obtained perfect matrix scores. These results confirmed the resilience of the stacking model for survival prediction. Other models also showed high prediction performance. Explainable AI techniques provide details of the relative importance of these prognostic factors. Compared to prevailing models of survival prediction for gallbladder cancer, this one represents a major improvement, correlating a rigorously validated stacking ensemble with comprehensive explainability, as demonstrated by SHAP, PDP, ICE, and LIME. This results in almost perfect discrimination (AUROC = 0.9949) and a significantly boosted interpretability of prognostic factors. The partial dependence plots present the effects of liver and glycemic markers on survival prognosis. SHAP values illustrated that the final stage of the tumor and the absence of surgery are the top negative prognostic indicators. The explainability of SHAP also demonstrated the importance of liver enzymes, bilirubin, age, and albumin levels in predicting survival. Hence, explainable machine learning models help predict more accurately and also support an understanding of the main factors contributing to survival. If they are ever integrated into clinical environments, such models can help personalize treatment strategies and improve care outcomes. All codes and datasets used in this study are available at <a href="https://github.com/devnarayan87/GBC_Cancer_Data">https://github.com/devnarayan87/GBC_Cancer_Data</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring gallbladder cancer prognosis using machine learning and explainable AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

查看全文
  • Shantam Srivastava,
  • Ankita Dutta,
  • Debashree Guha,
  • Debnarayan Khatua,
  • Dilip K. Prasad,
  • Arif Ahmed Sekh

摘要

Gallbladder cancer (GBC) is globally rare but prevalent in India. GBC is often diagnosed at an advanced stage, leading to a poor outcome. The identification of key prognostic factors and accurate survival prediction are crucial to optimizing treatment strategies. Retrospective data from 698 patients with gallbladder cancer treated at Tata Medical Center, Kolkata were obtained from a published dataset. Different machine learning models were used to predict survival outcomes. The performance of the model was evaluated, and explainable AI techniques were used to interpret the model’s output and identify significant prognostic factors. The analysis identified elevated liver enzymes and bilirubin levels as significant prognostic factors. Advanced age has also been shown to be correlated with terminal disease outcomes. When comparing different modeling systems, the stacking model obtained perfect matrix scores. These results confirmed the resilience of the stacking model for survival prediction. Other models also showed high prediction performance. Explainable AI techniques provide details of the relative importance of these prognostic factors. Compared to prevailing models of survival prediction for gallbladder cancer, this one represents a major improvement, correlating a rigorously validated stacking ensemble with comprehensive explainability, as demonstrated by SHAP, PDP, ICE, and LIME. This results in almost perfect discrimination (AUROC = 0.9949) and a significantly boosted interpretability of prognostic factors. The partial dependence plots present the effects of liver and glycemic markers on survival prognosis. SHAP values illustrated that the final stage of the tumor and the absence of surgery are the top negative prognostic indicators. The explainability of SHAP also demonstrated the importance of liver enzymes, bilirubin, age, and albumin levels in predicting survival. Hence, explainable machine learning models help predict more accurately and also support an understanding of the main factors contributing to survival. If they are ever integrated into clinical environments, such models can help personalize treatment strategies and improve care outcomes. All codes and datasets used in this study are available at https://github.com/devnarayan87/GBC_Cancer_Data.