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Related Experiment Video

Updated: Jun 14, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

Shengsheng Tang¹, Hongzheng Zhang¹, Junhao Liang¹

¹Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.

|September 2, 2024

Summary

Machine learning models accurately predict prostate cancer treatment options, identifying key factors like cancer stage and PSA levels. Surgery significantly improves survival rates, with radical prostatectomy offering the best outcomes.

Keywords:

SEER database SHAP interpreter machine learning prostate cancer survival analysis

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Published on: October 10, 2019

Area of Science:

Oncology
Medical Informatics
Biostatistics

Background:

Prostate cancer treatment decisions require accurate prediction models.
Existing models may lack transparency or comprehensive validation.

Purpose of the Study:

To develop and validate machine learning models for predicting prostate cancer treatment options (surgical vs. non-surgical).
To identify key factors influencing treatment prediction.
To compare survival rates between different treatment modalities.

Main Methods:

Utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database.
Applied 10 machine learning algorithms, evaluating performance using AUC, accuracy, sensitivity, and specificity.
Employed Shapley Additive Explanations (SHAP) for factor importance and survival analysis for outcome comparison.

Main Results:

The CatBoost model achieved the highest performance (AUC=0.939, accuracy=0.877).
Key predictors included T stage, cancer stage, age, cores positive percentage, prostate-specific antigen (PSA), and Gleason score.
Surgery improved 10-year survival by 20.36% compared to non-surgical treatments; radical prostatectomy showed the highest 10-year survival (89.2%).

Conclusions:

Developed a robust, transparent predictive model to aid prostate cancer treatment decisions.
Identified critical factors for personalized treatment planning.
Demonstrated the survival benefit of surgical intervention, particularly radical prostatectomy.