Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.
OpenAlex 토픽 ·
Prostate Cancer Diagnosis and Treatment
Radiomics and Machine Learning in Medical Imaging
Prostate Cancer Treatment and Research
[OBJECTIVES] The aim of this study was to develop and validate an MRI radiomics-based predictive model to discriminate significant prostate cancer (sPCa), compare it with PI-RADS, and determine whethe
- p-value p < 0.05
- 95% CI 0.853-0.930
- Specificity 29.41%
APA
Andreu Antolín, Richard Mast, et al. (2026). Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.. Insights into imaging, 17(1). https://doi.org/10.1186/s13244-026-02295-4
MLA
Andreu Antolín, et al.. "Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.." Insights into imaging, vol. 17, no. 1, 2026.
PMID
42034832
Abstract
[OBJECTIVES] The aim of this study was to develop and validate an MRI radiomics-based predictive model to discriminate significant prostate cancer (sPCa), compare it with PI-RADS, and determine whether incorporating PI-RADS and other clinical variables improves clinical performance.
[MATERIALS AND METHODS] A retrospective observational study was conducted using a cohort of 1497 MRI cases from 1395 men to develop the models. For each case, the index-lesion PI-RADS score, systematic ± targeted biopsy results, and six additional clinical variables were collected. Prostate biopsy samples served as the reference standard, defining sPCa as Gleason Grade ≥ 7. Handcrafted radiomic features were extracted from automatically segmented prostate glands. Four machine learning models were developed: (1) Radiomics, (2) PI-RADS, (3) PI-RADS + Radiomics, and (4) PI-RADS + Radiomics + Clinical Variables. Model performance and comparisons were evaluated using the area under the curve (AUC), while clinical utility was assessed through the decision curve analysis plot, Clinical Utility plot, and the number of avoided biopsies.
[RESULTS] The radiomics model did not perform significantly better than PI-RADS in the validation cohort (AUC 0.838 vs. 0.833, p = 0.874). The combination of radiomics, PI-RADS, and clinical variables achieved the highest performance, with an AUC of 0.891 (95% CI: 0.853-0.930), significantly outperforming the other models (p < 0.05). It also showed the highest specificity (29.41%) and biopsy avoidance rate (18.15%), although the differences were not statistically significant (p = 0.313).
[CONCLUSIONS] Incorporating radiomics and clinical variables into PI-RADS enhances its ability to discriminate sPCa, potentially decreasing false positives and unnecessary biopsies.
[CRITICAL RELEVANCE STATEMENT] The incorporation of clinical variables and radiomics into PI-RADS enhances its ability to predict significant prostate cancer, helping mitigate some of PI-RADS's current limitations, such as a significant false-positive rate, and might help reduce unnecessary biopsies.
[KEY POINTS] PI-RADS limitations result in overdiagnosis of indolent prostatic lesions and unnecessary biopsies. Radiomics and clinical variables enhance PI-RADS ability to detect significant prostate cancer. Combined clinical-radiological models reduce false positives and help avoid unnecessary biopsies.
[MATERIALS AND METHODS] A retrospective observational study was conducted using a cohort of 1497 MRI cases from 1395 men to develop the models. For each case, the index-lesion PI-RADS score, systematic ± targeted biopsy results, and six additional clinical variables were collected. Prostate biopsy samples served as the reference standard, defining sPCa as Gleason Grade ≥ 7. Handcrafted radiomic features were extracted from automatically segmented prostate glands. Four machine learning models were developed: (1) Radiomics, (2) PI-RADS, (3) PI-RADS + Radiomics, and (4) PI-RADS + Radiomics + Clinical Variables. Model performance and comparisons were evaluated using the area under the curve (AUC), while clinical utility was assessed through the decision curve analysis plot, Clinical Utility plot, and the number of avoided biopsies.
[RESULTS] The radiomics model did not perform significantly better than PI-RADS in the validation cohort (AUC 0.838 vs. 0.833, p = 0.874). The combination of radiomics, PI-RADS, and clinical variables achieved the highest performance, with an AUC of 0.891 (95% CI: 0.853-0.930), significantly outperforming the other models (p < 0.05). It also showed the highest specificity (29.41%) and biopsy avoidance rate (18.15%), although the differences were not statistically significant (p = 0.313).
[CONCLUSIONS] Incorporating radiomics and clinical variables into PI-RADS enhances its ability to discriminate sPCa, potentially decreasing false positives and unnecessary biopsies.
[CRITICAL RELEVANCE STATEMENT] The incorporation of clinical variables and radiomics into PI-RADS enhances its ability to predict significant prostate cancer, helping mitigate some of PI-RADS's current limitations, such as a significant false-positive rate, and might help reduce unnecessary biopsies.
[KEY POINTS] PI-RADS limitations result in overdiagnosis of indolent prostatic lesions and unnecessary biopsies. Radiomics and clinical variables enhance PI-RADS ability to detect significant prostate cancer. Combined clinical-radiological models reduce false positives and help avoid unnecessary biopsies.