What question did this study set out to answer?

The aim is to create a prediction model for prostate cancer diagnosis using machine learning techniques based on various clinical parameters.

April 1, 2026

Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate‐Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters

Key Points

The aim is to create a prediction model for prostate cancer diagnosis using machine learning techniques based on various clinical parameters.
Evaluated patients undergoing systematic or combined biopsy based on mpMRI findings and pre-biopsy PSA levels.
Recorded laboratory findings, mpMRI results, and biopsy outcomes for model development.
Applied five different machine learning algorithms: logistic regression, random forest, extra trees, XGBoost, and light gradient-boosting machine.
1223 patients were diagnosed without malignancy, while 225 had malignancy.
The XGBoost model achieved the highest accuracy with a sensitivity of 94.74% and specificity of 100%.
Random forest model showed a sensitivity of 78.95% and a specificity of 100%.
Area under the curve (AUC) values were 0.97 for XGBoost and 0.89 for RF.
Key variables for prediction included free/total PSA, Prostate Imaging-Reporting and Data System Score 4, and platelet-to-lymphocyte ratio.

Abstract

ABSTRACT Introduction Prostate‐specific antigen (PSA) alone is insufficient for the diagnosis of prostate cancer (PCa), particularly within the gray zone range of 4–10 ng/mL. Multiparametric magnetic resonance imaging (mpMRI), although widely used, has notable limitations in the diagnostic pathway. The aim of this study was to develop a biopsy prediction model by evaluating multiple machine learning (ML) algorithms incorporating PSA‐related variables, mpMRI findings, and hematologic parameters. Materials and Methods This study included patients who underwent either systematic biopsy or combined biopsy (systematic plus fusion) based on mpMRI findings and had pre‐biopsy PSA levels ≤ 10 ng/mL between 2017 and 2024 at our center. Laboratory findings, mpMRI results, and prostate biopsy outcomes were recorded. Based on the pathological evaluation of biopsy specimens, the patients were divided into two groups: those without malignancy (Group 1) and those with malignancy (Group 2). To develop a useful model for prediction, five ML techniques were applied: logistic regression, random forest (RF), extra trees, extreme gradient boosting (XGBoost) classifier, and light gradient‐boosting machine classifier. Results There were 1223 patients (84.5%) in Group 1 and 225 patients (15.5%) in Group 2. The model with the best accuracy was XGBoost, with a sensitivity of 94.74% and a specificity of 100% on the test data set. For the RF model, sensitivity and specificity on the test data set were determined to be 78.95% and 100%, respectively. The area under the curve (AUC) values for XGBoost and RF were 0.97 and 0.89, respectively. According to the permutation feature importance analysis, the three most influential variables were free/total PSA, Prostate Imaging‐Reporting and Data System Score 4, and platelet‐to‐lymphocyte ratio. Conclusion XGBoost and RF models demonstrated excellent performance, with high AUC values. To generalize the results, it is necessary to confirm the accuracy of ML models through external validation in different populations.

KI fragen

Bookmark

Cite This Study

Özlü et al. (Mon,) studied this question.

synapsesocial.com/papers/69ccb74216edfba7beb891d4 https://doi.org/https://doi.org/10.1002/pros.70168

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

KI fragen

Bookmark