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Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings
OBJECTIVE: To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa. METHODS: This retrospective s...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163353/ https://www.ncbi.nlm.nih.gov/pubmed/35669420 http://dx.doi.org/10.3389/fonc.2022.841801 |
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author | Shiradkar, Rakesh Ghose, Soumya Mahran, Amr Li, Lin Hubbard, Isaac Fu, Pingfu Tirumani, Sree Harsha Ponsky, Lee Purysko, Andrei Madabhushi, Anant |
author_facet | Shiradkar, Rakesh Ghose, Soumya Mahran, Amr Li, Lin Hubbard, Isaac Fu, Pingfu Tirumani, Sree Harsha Ponsky, Lee Purysko, Andrei Madabhushi, Anant |
author_sort | Shiradkar, Rakesh |
collection | PubMed |
description | OBJECTIVE: To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa. METHODS: This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (C(S) ) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (C(R) ) was trained for BCR. An integrated classifier C(S) (+)(R) was then trained using predictions from C(S) and C(R) . These models were trained on D(1) (N = 71, 27 BCR+) and evaluated on independent hold-out set D(2) (N = 62, 12 BCR+). C(S) (+)(R) was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years. RESULTS: C(S) (+)(R) resulted in a higher AUC (0.75) compared to C(R) (0.70, p = 0.04) and C(S) (0.69, p = 0.01) on D(2) in predicting BCR. On univariable analysis, C(S) (+)(R) achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. C(S) (+)(R) , pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). C(S) (+)(R) resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07). CONCLUSIONS: Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy. |
format | Online Article Text |
id | pubmed-9163353 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91633532022-06-05 Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings Shiradkar, Rakesh Ghose, Soumya Mahran, Amr Li, Lin Hubbard, Isaac Fu, Pingfu Tirumani, Sree Harsha Ponsky, Lee Purysko, Andrei Madabhushi, Anant Front Oncol Oncology OBJECTIVE: To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa. METHODS: This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (C(S) ) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (C(R) ) was trained for BCR. An integrated classifier C(S) (+)(R) was then trained using predictions from C(S) and C(R) . These models were trained on D(1) (N = 71, 27 BCR+) and evaluated on independent hold-out set D(2) (N = 62, 12 BCR+). C(S) (+)(R) was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years. RESULTS: C(S) (+)(R) resulted in a higher AUC (0.75) compared to C(R) (0.70, p = 0.04) and C(S) (0.69, p = 0.01) on D(2) in predicting BCR. On univariable analysis, C(S) (+)(R) achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. C(S) (+)(R) , pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). C(S) (+)(R) resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07). CONCLUSIONS: Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy. Frontiers Media S.A. 2022-05-20 /pmc/articles/PMC9163353/ /pubmed/35669420 http://dx.doi.org/10.3389/fonc.2022.841801 Text en Copyright © 2022 Shiradkar, Ghose, Mahran, Li, Hubbard, Fu, Tirumani, Ponsky, Purysko and Madabhushi https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Shiradkar, Rakesh Ghose, Soumya Mahran, Amr Li, Lin Hubbard, Isaac Fu, Pingfu Tirumani, Sree Harsha Ponsky, Lee Purysko, Andrei Madabhushi, Anant Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings |
title | Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings |
title_full | Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings |
title_fullStr | Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings |
title_full_unstemmed | Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings |
title_short | Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings |
title_sort | prostate surface distension and tumor texture descriptors from pre-treatment mri are associated with biochemical recurrence following radical prostatectomy: preliminary findings |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9163353/ https://www.ncbi.nlm.nih.gov/pubmed/35669420 http://dx.doi.org/10.3389/fonc.2022.841801 |
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