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

Abstract

Background We developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher. Methods A retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D 1 , N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D 1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D 2 , N = 127). Findings “RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79).” Interpretation RadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy. Funding The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

“…These models were already studied in a variety of cancers [ 35 , 36 , 37 , 38 , 39 ]. The recent rise in artificial intelligence (AI) and machine learning (ML) algorithms has introduced new classifications for PCa, regarding the differentiation of favorable from unfavorable disease [ 40 , 41 ]; the quantitative assessment of information predicting the tumor Gleason score [ 31 , 32 , 42 , 43 , 44 ] and biochemical recurrence (BCR)-free survival [ 45 ]; the identification of tumors through mpMRI [ 43 , 46 ]; the development of new detection features, such as advanced zoomed diffusion-weighted imaging (DWI) and conventional full-field-of-view DWI [ 47 ]; texture analysis of prostate MRI in the prostate imaging reporting and data system (PIRADS) for PI-RADS 3 score lesions [ 48 ]; the creation of frameworks for automated PCa localization and detection [ 49 ]; and, finally, the management of radiotherapy treatment and toxicity [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ], and the prediction of BCR [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. Additionally, radiomics and AI algorithms will help to limit the discrepancies between different readers [ 66 ].…”

“…This suggests that Decipher ® added on to MRI will probably not lead to significantly more detections of cancer. Conversely, despite technical advancements in prostate imaging, such as MRI, not appearing to be superior to Decipher ® testing in the prediction of adverse pathology, recently, Li et al [ 45 ] proposed a new imaging-based nomogram to predict biochemical recurrence and adverse pathology, reporting promising results with an AUC (0.71, 95% CI 0.62–0.81) higher than Decipher ® AUC (0.66, 95% CI 0.56–0.77) and prostate cancer risk assessment (CAPRA) score AUC (0.69, 95% CI 0.59–0.79).…”

An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

“…Although a few studies have shown the association of radiomic features with biochemical recurrence-free survival, 28,29 to our knowledge, none of these studies have evaluated the association of deep learning predictions and representations with biochemical recurrence-free survival. Shiradkar and colleagues 28 showed that a machine learning classifier trained on radiomic signatures can predict biochemical recurrence.…”

“…Shiradkar and colleagues 28 showed that a machine learning classifier trained on radiomic signatures can predict biochemical recurrence. Similarly, in a study by our group, Li and colleagues 29 constructed a radiomics-based nomogram, including GGG, PSA, and radiomics-based imaging biomarkers, using multivariable analysis to predict biochemical recurrence. Here, our findings showed that a clinical nomogram that was trained to identify clinically significant prostate cancer lesions on biparametric MRI could, as an additional feature, also predict biochemical recurrence-free survival, which might help to identify patients who would benefit from adjuvant therapy.…”

Radiation Therapy After Radical Prostatectomy: What Has Changed Over Time?

“…For the future, new PET tracers and the extraction and quantification of MRI imaging features (radiomics) (68,69) may guide future research in patients stratification into high potential responder (negative findings or recurrence confined to the prostate) and poor potential responder (positive nodes or distant disease) to SRT.…”