Objective:

To evaluate a machine learning radiomics model integrating PET/CT and MRI for risk stratification in patients with suspected prostate cancer.

Key Findings:

• Multimodal models outperformed single-modality approaches for clinically significant prostate cancer, indicating a potential for improved diagnostic accuracy.
• In the internal test cohort, logistic regression, support vector machine, and LightGBM achieved AUCs of 0.91, suggesting strong model performance.
• In external validation, LightGBM achieved AUCs of 0.82 and 0.89, while logistic regression achieved AUCs of 0.80 and 0.85, highlighting variability in model performance across cohorts.

Interpretation:

The model shows promise for risk stratification but its clinical role remains uncertain due to modest negative predictive values, which may limit its utility in clinical practice.

Limitations:

• Retrospective design and small external validation cohorts.
• Possible selection bias and variability in imaging protocols across centers.
• Lack of prospective validation and generalizability limited to Chinese academic centers.
• Ambiguity in automated segmentation analysis regarding feature selection thresholds.

Conclusion:

Further study of multimodal radiomics is warranted as a decision-support tool for prostate cancer risk stratification, not as a replacement for histopathologic confirmation, and should include direct comparisons with established methods.