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

Abstract

ObjectiveTo 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.MethodsThis 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 (CS) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (CR) was trained for BCR. An integrated classifier CS+R was then trained using predictions from CS and CR. These models were trained on D1 (N = 71, 27 BCR+) and evaluated on independent hold-out set D2 (N = 62, 12 BCR+). CS+R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years.ResultsCS+R resulted in a higher AUC (0.75) compared to CR (0.70, p = 0.04) and CS (0.69, p = 0.01) on D2 in predicting BCR. On univariable analysis, CS+R achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. CS+R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). CS+R resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07).ConclusionsRadiomic 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.

The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature

“…Similar results for BCR prediction at various timepoints were obtained by three other studies, which all included external cohorts in the construction of their radiomic models [ 39 , 41 , 42 ]. Furthermore, two of these three studies included head-to-head comparisons between their radiomic model and commonly used clinical nomograms for prediction of BCR.…”

“…To date, there have been only a handful of investigations utilizing radiomic features to predict biochemical recurrence (BCR) following RP. Table 3 illustrates the four studies [ 39 , 40 , 41 , 42 ] utilizing preoperative MRI-derived radiomics to predict post-RP BCR, defined as two consecutive serum PSA levels greater than or equal to 0.2 ng/mL.…”

“…Furthermore, two of these three studies included head-to-head comparisons between their radiomic model and commonly used clinical nomograms for prediction of BCR. Yan et al reported ROC-AUCs ranging from 0.84 to 0.88 in predicting three-year BCR [ 41 ], while Shiradkar et al reported their radiomic model [ 42 ] to be significantly and independently correlated with three-year BCR in cox proportional hazards regression modeling (HR: 2.91, 95% CI: 1.45–11.51, p = 0.02). In addition, both studies compared their radiomic models to the UCSF-CAPRA and CAPRA-S scores.…”

Ten quick tips for computational analysis of medical images

“…Additionally, we can consider using computational imaging approaches that are independent of image intensity measurements such as shape and volume based features [ 68 ].…”

“…Computationally derived features provide alternate representations that enhance specific attributes and patterns for specific tasks. For instance, feature representations have been used for coregistration [ 74 ], segmentation [ 75 ], and classification [ 68 ].…”