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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
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744939/ https://www.ncbi.nlm.nih.gov/pubmed/33321450 http://dx.doi.org/10.1016/j.ebiom.2020.103163 |
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author | 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 |
author_facet | 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 |
author_sort | Li, Lin |
collection | PubMed |
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 (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. |
format | Online Article Text |
id | pubmed-7744939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-77449392020-12-21 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 EBioMedicine Research Paper 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. Elsevier 2020-12-13 /pmc/articles/PMC7744939/ /pubmed/33321450 http://dx.doi.org/10.1016/j.ebiom.2020.103163 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Paper 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 A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI |
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_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_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_sort | novel imaging based nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric mri |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744939/ https://www.ncbi.nlm.nih.gov/pubmed/33321450 http://dx.doi.org/10.1016/j.ebiom.2020.103163 |
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