Predicting clinically significant prostate cancer in PI-RADS 3 lesions using MRI-based radiomics: a literature review...

To evaluate the current state of MRI-based radiomics for predicting clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions and assess the quality of these radiomic studies via a systematic review of the published literature.

We conducted a literature search in PubMed, EMBASE, and SCOPUS databases from January 2017 to September 2024, using search terms containing variations of PI-RADS-3 and radiomics in abstract and titles. We collected details from the radiomic workflow for each study, including statistical performance of the radiomics models (area under the curve (AUC)). We calculated the pooled AUC across the studies and a radiomics quality score (RQS) to evaluate the quality of radiomics methodology.

Of 52 articles retrieved, 14 met the selection criteria. Of these, 12 studies employed 3T MRI scanners, 8 studies T2WI, DWI, ADC images for feature extraction, and 13 studies performed manual segmentation. All but two studies used the PyRadiomics platform as their feature extraction tool. The most commonly used radiomic selection methods were Least Absolute Shrinkage and Selection Operator (LASSO). The total number of features extracted ranged between 107 and 2553. The median number of radiomics features selected for use in models was 10. Nine studies (9/14) explored clinical variables in their radiomics models, with the most common being age and PSA. For building the final model, Logistic Regression, and Univariate and Multivariate modeling methods were featured across eight studies (8/14). Overall performance of the models by pooled AUC was 0.823 (95% CI, 0.72, 0.92). The mean RQS score was 15/36 (range 13-19).

MRI-based radiomic models have potential in predicting csPCa in PI-RADS-3 lesions. However, using RQS as a guide, we determined there is a clear need to improve the methodological quality of existing and future studies by focusing on extensive validation and open publishing of data for reproducibility.