Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa. This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier ( ) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier ( ) was trained for BCR. An integrated classifier was then trained using predictions from and . These models were trained on D ( = 71, 27 BCR+) and evaluated on independent hold-out set D ( = 62, 12 BCR+). was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years. resulted in a higher AUC (0.75) compared to (0.70, = 0.04) and (0.69, = 0.01) on D in predicting BCR. On univariable analysis, achieved a higher hazard ratio (2.89, 95% CI 0.35-12.81, < 0.01) compared to other pre-RP clinical variables for bFS. , pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS ( < 0.05). resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, < 0.01) and Decipher risk (0.59 ± 0.06, < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, = 0.07). Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.