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

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...

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Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:EBioMedicine
Subjects:
MRI
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396420305399
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language English
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author 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
spellingShingle 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
A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI
EBioMedicine
Prostate cancer
Biochemical recurrence
Adverse pathology
Radiomic
MRI
Prognosis
author_facet 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
author_sort Lin Li
title A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI
title_short A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI
title_full A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI
title_fullStr A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI
title_full_unstemmed A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI
title_sort novel imaging based nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric mri
publisher Elsevier
series EBioMedicine
issn 2352-3964
publishDate 2021-01-01
description 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.
topic Prostate cancer
Biochemical recurrence
Adverse pathology
Radiomic
MRI
Prognosis
url http://www.sciencedirect.com/science/article/pii/S2352396420305399
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spelling doaj-16f2d48814ce47b5950cc438e51e9b262021-01-22T04:50:12ZengElsevierEBioMedicine2352-39642021-01-0163103163A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRILin Li0Rakesh Shiradkar1Patrick Leo2Ahmad Algohary3Pingfu Fu4Sree Harsha Tirumani5Amr Mahran6Christina Buzzy7Verena C Obmann8Bahar Mansoori9Ayah El-Fahmawi10Mohammed Shahait11Ashutosh Tewari12Cristina Magi-Galluzzi13David Lee14Priti Lal15Lee Ponsky16Eric Klein17Andrei S. Purysko18Anant Madabhushi19Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USACenter for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USACenter for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USACenter for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USADepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USADepartment of Radiology, University Hospitals, Cleveland, OH, USAUrology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USAUrology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USADepartment 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, SwitzerlandDepartment of Radiology, Abdominal Imaging Division, University of Washington, Seattle, WA, USAPenn Medicine, University of Pennsylvania Health System, PA, USAPenn Medicine, University of Pennsylvania Health System, PA, USADepartment of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USADepartment of Pathology, University of Alabama at Birmingham, AL, USAPenn Medicine, University of Pennsylvania Health System, PA, USAPenn Medicine, University of Pennsylvania Health System, PA, USAUrology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USAGlickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USAGlickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA; Imaging Institute, Cleveland Clinic, Cleveland, OH, USACenter 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.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.http://www.sciencedirect.com/science/article/pii/S2352396420305399Prostate cancerBiochemical recurrenceAdverse pathologyRadiomicMRIPrognosis