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A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI.
Li, Lin; Shiradkar, Rakesh; Leo, Patrick; Algohary, Ahmad; Fu, Pingfu; Tirumani, Sree Harsha; Mahran, Amr; Buzzy, Christina; Obmann, Verena C; Mansoori, Bahar; El-Fahmawi, Ayah; Shahait, Mohammed; Tewari, Ashutosh; Magi-Galluzzi, Cristina; Lee, David; Lal, Priti; Ponsky, Lee; Klein, Eric; Purysko, Andrei S; Madabhushi, Anant.
Afiliación
  • Li L; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Shiradkar R; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Leo P; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Algohary A; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Fu P; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
  • Tirumani SH; Department of Radiology, University Hospitals, Cleveland, OH, USA.
  • Mahran A; Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
  • Buzzy C; Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
  • Obmann VC; 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.
  • Mansoori B; Department of Radiology, Abdominal Imaging Division, University of Washington, Seattle, WA, USA.
  • El-Fahmawi A; Penn Medicine, University of Pennsylvania Health System, PA, USA.
  • Shahait M; Penn Medicine, University of Pennsylvania Health System, PA, USA.
  • Tewari A; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Magi-Galluzzi C; Department of Pathology, University of Alabama at Birmingham, AL, USA.
  • Lee D; Penn Medicine, University of Pennsylvania Health System, PA, USA.
  • Lal P; Penn Medicine, University of Pennsylvania Health System, PA, USA.
  • Ponsky L; Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Case Western Reserve University School of Medicine, Cleveland, OH, USA.
  • Klein E; Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Purysko AS; Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA; Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Madabhushi A; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, USA. Electronic address: axm788@case.edu.
EBioMedicine ; 63: 103163, 2021 Jan.
Article en En | MEDLINE | ID: mdl-33321450
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 (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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Diagnóstico por Imagen / Atención Perioperativa Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Aged80 / Humans / Male / Middle aged Idioma: En Revista: EBioMedicine Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Diagnóstico por Imagen / Atención Perioperativa Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Aged80 / Humans / Male / Middle aged Idioma: En Revista: EBioMedicine Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos