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Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard
We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933973/ https://www.ncbi.nlm.nih.gov/pubmed/35532558 http://dx.doi.org/10.4103/aja202215 |
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author | Chen, Zhang-Zhe Gu, Wei-Jie Zhou, Bing-Ni Liu, Wei Gan, Hua-Lei Zhang, Yong Zhou, Liang-Ping Liu, Xiao-Hang |
author_facet | Chen, Zhang-Zhe Gu, Wei-Jie Zhou, Bing-Ni Liu, Wei Gan, Hua-Lei Zhang, Yong Zhou, Liang-Ping Liu, Xiao-Hang |
author_sort | Chen, Zhang-Zhe |
collection | PubMed |
description | We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and 30 with complete response or minimum residual disease [CR/MRD]). Totally, 100 significant residual, 52 CR/MRD lesions, and 70 benign tissues were selected according to pathology. First, 381 radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps. Optimal features were selected using a support vector machine with a recursive feature elimination algorithm (SVM-RFE). Then, ADC values of significant residual, CR/MRD lesions, and benign tissues were compared by one-way analysis of variance. Logistic regression was used to construct models with SVM features to differentiate between each pair of tissues. Third, the efficiencies of ADC value and radiomics models for differentiating the three tissues were assessed by area under receiver operating characteristic curve (AUC). The ADC value (mean ± standard deviation [s.d.]) of significant residual lesions ([1.10 ± 0.02] × 10(-3) mm(2) s(-1)) was significantly lower than that of CR/MRD ([1.17 ± 0.02] × 10(-3) mm(2) s(-1)), which was significantly lower than that of benign tissues ([1.30 ± 0.02] × 10(-3) mm(2) s(-1); both P < 0.05). The SVM feature models were comparable to ADC value in distinguishing CR/MRD from benign tissue (AUC: 0.766 vs 0.792) and distinguishing residual from benign tissue (AUC: 0.825 vs 0.835) (both P > 0.05), but superior to ADC value in differentiating significant residual from CR/MRD (AUC: 0.748 vs 0.558; P = 0.041). Radiomics approach with biparametric MRI could promote the detection of significant residual prostate cancer after ADT. |
format | Online Article Text |
id | pubmed-9933973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-99339732023-02-17 Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard Chen, Zhang-Zhe Gu, Wei-Jie Zhou, Bing-Ni Liu, Wei Gan, Hua-Lei Zhang, Yong Zhou, Liang-Ping Liu, Xiao-Hang Asian J Androl Original Article We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and 30 with complete response or minimum residual disease [CR/MRD]). Totally, 100 significant residual, 52 CR/MRD lesions, and 70 benign tissues were selected according to pathology. First, 381 radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps. Optimal features were selected using a support vector machine with a recursive feature elimination algorithm (SVM-RFE). Then, ADC values of significant residual, CR/MRD lesions, and benign tissues were compared by one-way analysis of variance. Logistic regression was used to construct models with SVM features to differentiate between each pair of tissues. Third, the efficiencies of ADC value and radiomics models for differentiating the three tissues were assessed by area under receiver operating characteristic curve (AUC). The ADC value (mean ± standard deviation [s.d.]) of significant residual lesions ([1.10 ± 0.02] × 10(-3) mm(2) s(-1)) was significantly lower than that of CR/MRD ([1.17 ± 0.02] × 10(-3) mm(2) s(-1)), which was significantly lower than that of benign tissues ([1.30 ± 0.02] × 10(-3) mm(2) s(-1); both P < 0.05). The SVM feature models were comparable to ADC value in distinguishing CR/MRD from benign tissue (AUC: 0.766 vs 0.792) and distinguishing residual from benign tissue (AUC: 0.825 vs 0.835) (both P > 0.05), but superior to ADC value in differentiating significant residual from CR/MRD (AUC: 0.748 vs 0.558; P = 0.041). Radiomics approach with biparametric MRI could promote the detection of significant residual prostate cancer after ADT. Wolters Kluwer - Medknow 2022-05-03 /pmc/articles/PMC9933973/ /pubmed/35532558 http://dx.doi.org/10.4103/aja202215 Text en Copyright: © The Author(s)(2022) https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Original Article Chen, Zhang-Zhe Gu, Wei-Jie Zhou, Bing-Ni Liu, Wei Gan, Hua-Lei Zhang, Yong Zhou, Liang-Ping Liu, Xiao-Hang Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
title | Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
title_full | Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
title_fullStr | Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
title_full_unstemmed | Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
title_short | Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
title_sort | radiomics based on biparametric mri for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933973/ https://www.ncbi.nlm.nih.gov/pubmed/35532558 http://dx.doi.org/10.4103/aja202215 |
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