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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
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Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using
Olga Vershinina1,2, Nikita Sushentsev3, Alexey Zaikin4,5
1Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
Cancers
|November 27, 2025
Summary
Machine learning models accurately predict prostate cancer (PCa) progression in patients on active surveillance (AS). Radiomic analysis of MRI scans and PSA density enhances prediction, aiding personalized treatment strategies for PCa.
Area of Science:
- Oncology
- Medical Imaging
- Machine Learning
Background:
- Half of prostate cancer (PCa) patients have low- or intermediate-risk disease suitable for active surveillance (AS).
- A significant number of patients on AS experience pathological progression.
- Predicting this progression is crucial for effective PCa management.
Purpose of the Study:
- Develop and validate predictive models for histopathological PCa progression in patients undergoing AS.
- Integrate radiomic features from MRI and clinical data for enhanced prediction.
- Improve clinical decision-making for AS patients.
Main Methods:
- Utilized a dataset of biopsy-confirmed PCa patients with at least two years of follow-up and multiple MRI scans.
- Defined histopathological progression as a grade group upgrade on repeat biopsy.
- Employed machine learning (ML) models integrating radiomic and PSA density (PSAd) variables.
- Applied SHapley Additive exPlanations (SHAP) for feature interpretability.
Main Results:
- A baseline model (radiomic + PSAd) achieved an AUC of 0.793.
- A delta model (feature changes + PSAd) improved prediction with an AUC of 0.913.
- A time series model (all scans + PSAd) showed the highest performance with an AUC of 0.917.
- All models demonstrated strong ability to differentiate progressors from non-progressors.
Conclusions:
- Radiomic analysis combined with ML offers a powerful tool for predicting PCa progression in AS patients.
- These models can significantly enhance PCa management and personalized treatment strategies.
- The findings support the integration of advanced imaging and ML in routine clinical practice for AS.
