Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI

Purpose: To develop and evaluate a multimodal approach including clinical parameters and biparametric MRI-based artificial intelligence (AI) model for determining the necessity of prostate biopsy in patients with PI-RADS 3 lesions.

Methods: This retrospective study included a prospectively recruited patient cohort with PI-RADS 3 lesions who underwent prostate MRI and MRI/US fusion-guided biopsy between April 2019 and February 2024 in a single institution. The study examined demographic data, PSA and PSA density (PSAD) levels, prostate volumes, prospective PI-RADS v2.1-compliant interpretations of a genitourinary radiologist, lesion characteristics, history of prior biopsies, and AI evaluations, focusing mainly on the detection of clinically significant prostate cancer (csPCa) (International Society of Urological Pathology grade group ≥2) on MRI/US fusion-guided biopsy. The AI model lesion segmentations were compared to manual segmentations and biopsy results. The statistical methods employed included Fisher's exact test and logistic regression.

Results: The cohort was comprised of 248 patients with 312 PI-RADS 3 lesions in total (n = 268 non-csPCa, n = 44 csPCa). The AI model's negative predictive value (NPV) was 89.2 % for csPCa in all lesions. In patient-level analysis, the NPV was 91.2 % for patients with a highest PI-RADS score of 3. PSAD was a significant predictor of csPCa (odds ratio = 5.8, p = 0.038). Combining AI and PSAD, where AI correctly mapped a lesion or PSAD ≥0.15 ng/mL², achieved higher sensitivity (77.8 %) while maintaining a high NPV (93.1 %).

Conclusion: Combining AI and PSAD has the potential to enhance biopsy decision-making for PI-RADS 3 lesions by minimizing missed csPCa occurrences and reducing unnecessary biopsies.