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.
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 (D1, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D2, N = 127).
"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)."
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.
The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.
EBioMedicine. 2020 Dec 12 [Epub ahead of print]
Lin Li, Rakesh Shiradkar, Patrick Leo, Ahmad Algohary, Pingfu Fu, Sree Harsha Tirumani, Amr Mahran, Christina Buzzy, Verena C Obmann, Bahar Mansoori, Ayah El-Fahmawi, Mohammed Shahait, Ashutosh Tewari, Cristina Magi-Galluzzi, David Lee, Priti Lal, Lee Ponsky, Eric Klein, Andrei S Purysko, Anant Madabhushi
Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA., Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA., Department of Radiology, University Hospitals, Cleveland, OH, USA., Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA., Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Centers, Cleveland, OH, USA; Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland., Department of Radiology, Abdominal Imaging Division, University of Washington, Seattle, WA, USA., Penn Medicine, University of Pennsylvania Health System, PA, USA., Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA., Department of Pathology, University of Alabama at Birmingham, AL, USA., Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA., Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA., Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA; Imaging Institute, Cleveland Clinic, Cleveland, OH, USA., Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, USA. Electronic address: .
PubMed http://www.ncbi.nlm.nih.gov/pubmed/33321450