Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score

Abstract

Background Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic‐assisted laparoscopic prostatectomy (RALP). Purpose To evaluate the performance of a routine clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP. Study Type Retrospective cohort study. Subjects Ninety‐one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow‐up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml. Field Strength/Sequence mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T2w, diffusion‐weighted imaging (DWI), or dynamic contrast‐enhanced (DCE) MRI. Assessment All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI‐RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re‐reported by reader B to evaluate interreader variability. Statistical Tests Univariate and multivariate analysis using greedy feature selection and tournament leave‐pair‐out cross‐validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group [GGG], extracapsular extension, seminal vesicle invasion, intraoperative surgical margins [PSM], final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI‐RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume [MRI‐lesion‐percentage], mpMRI ECE, mpMRI seminal vesicle invasion [SVI]) variables. The evaluation metric was the area under the curve (AUC). Results Forty‐eight (53%) patients developed BCR. The best‐performing individual features with TLPOCV AUC of 0.73 (95% confidence interval [CI] 0.64–0.82) were RALP GGG, MRI‐lesion‐percentage followed by biopsy GGG (0.72, 0.62–0.82), and Decipher score (0.71, 0.60–0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively Data Conclusion Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1075–1085.

A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

“…First, implicit bias may exist due to the retrospective nature of the study; however, the multi-institutional validation suggests the robustness of RadClip. In addition, even though 198 patients were included in this study, this number is actually larger compared to other recent related studies [17,18,40]. However, we acknowledge that prior to clinical deployment, the approach needs additional retrospective and potentially prospective clinical trial validation in the future to further confirm the generalizability of our findings.…”

“…al. illustrated that combing clinicopathologic parameters with mpMRI was comparable in prediction of BCR as a combination of Decipher and clinicopathologic parameters [40]. This study was however limited in its sample size, single institutional design and the fact that PI-RADS is known to be subject to interreader variability [41].…”

“…The application of assessing the association between mpMRI derived parameters, e.g. PI-RADS v2 score or lesion volume, with BCR, PCa aggressiveness and molecular tests like Decipher is not new [39,40]. Jambor et.…”

“…presence or absence of BCR) [17,29,40], our approach employed a Cox proportional hazards approach to build an imaging based prognostic model accounting for time to BCR as also censoring information introduced by the variance of follow-up time among patients. Moreover, this was also one of the very few studies we are aware of to validate an MRI based PCa prognostic tool in a multi-institutional setting [19,29,40]. The findings of RadClip on scans from 4 institutions, suggest that the approach is relatively robust and resilient to scanner and site-specific MRI variations.…”

Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy

“…In this retrospective multi-institutional study, we investigated and compared the potential of CAPRA score and predictive models in predicting the BCR risk after RP using routine variables. We note that the CAPRA score is a commonly used prediction model for the occurrence of biochemical and clinical recurrences developed from the CaPSURE registry ( 5 ) with many studies providing external validation with other cohorts ( 7 , 19 ). In this study, we found that CAPRA score showed a c-index of 0.63 to predict the 3-year-BCR rates with the prognostic variables obviously differing from those of the original CaPSURE cohort.…”