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

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

EBioMedicine (Jan 2021)

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

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

Affiliations

Lin Li: Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
Rakesh Shiradkar: Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
Patrick Leo: Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
Ahmad Algohary: Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
Pingfu Fu: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
Sree Harsha Tirumani: Department of Radiology, University Hospitals, Cleveland, OH, USA
Amr Mahran: Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
Christina Buzzy: Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
Verena C Obmann: 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
Bahar Mansoori: Department of Radiology, Abdominal Imaging Division, University of Washington, Seattle, WA, USA
Ayah El-Fahmawi: Penn Medicine, University of Pennsylvania Health System, PA, USA
Mohammed Shahait: Penn Medicine, University of Pennsylvania Health System, PA, USA
Ashutosh Tewari: Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Cristina Magi-Galluzzi: Department of Pathology, University of Alabama at Birmingham, AL, USA
David Lee: Penn Medicine, University of Pennsylvania Health System, PA, USA
Priti Lal: Penn Medicine, University of Pennsylvania Health System, PA, USA
Lee Ponsky: Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA
Eric Klein: Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
Andrei S. Purysko: Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA; Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
Anant Madabhushi: Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, USA; Corresponding author at: Center for Computational Imaging and Personalized Diagnostics, 2071 Martin Luther King Drive, Cleveland, Ohio 44106-7207, Wickenden 523.

Journal volume & issue: Vol. 63
p. 103163

Abstract

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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 (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). 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.

Published in EBioMedicine

ISSN: 2352-3964 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Medicine: Medicine (General)
Website: http://www.journals.elsevier.com/ebiomedicine/

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