TY - EJOU
AU - Arafa, Mostafa Ahmed
AU - Farhat, Karim Hamda
AU - Lotfy, Nesma
AU - Khan, Farrukh Kamel
AU - Mokhtar, Alaa
AU - Althunayan, Abdulaziz Mohammed
AU - Al-Taweel, Waleed
AU - Al-Khateeb, Sultan Saud
AU - Azhari, Sami
AU - Rabah, Danny Munther
TI - Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness
T2 - Canadian Journal of Urology
PY - 2025
VL - 32
IS - 3
SN - 1488-5581
AB - Background: Transrectal (TR) and transperineal (TP) biopsies are commonly used methods for diagnosing prostate cancer. However, their comparative effectiveness in conjunction with machine learning (ML) techniques remains underexplored. This study aimed to evaluate the predictive accuracy of ML algorithms in detecting prostate cancer using data derived from TR and TP biopsies. Methods: The clinical records of patients who underwent prostate biopsy at King Saud University Medical City and King Faisal Specialist Hospital and Research Centerin Riyadh, Saudi Arabia, between 2018 and 2025 were analyzed. Data were used to train and test ML models, including eXtreme Gradient Boosting (XGBoost), Decision Tree, Random Forest, and Extra Trees. Results: The two datasets are comparable. The models demonstrated exceptional performance, achieving accuracies of up to 96.49% and 95.56% on TP and TR biopsy datasets, respectively. The area under the curve (AUC) values were also high, reaching 0.9988 for TP and 0.9903 for TR biopsy predictions. Conclusion: These findings highlight the potential of ML to enhance the diagnostic accuracy of prostate cancer detection irrespective of the biopsy method. However, TP biopsy data showed marginally higher accuracy, possibly because of the lower risk of contamination. While ML holds great promise for transforming prostate cancer care, further research is needed to address limitations. Collaboration between clinicians, data scientists, and researchers is crucial to ensure the clinical relevance and interpretability of ML models.
KW - machine learning; prediction effectiveness; prostate cancer; transperineal biopsy; transrectal biopsy
DO - 10.32604/cju.2025.066016