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A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion

BACKGROUND: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE: To develop an artificial intelligence (AI)‐based model (named AI‐biopsy) for the early diagnosis of prost...

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Autores principales: Khosravi, Pegah, Lysandrou, Maria, Eljalby, Mahmoud, Li, Qianzi, Kazemi, Ehsan, Zisimopoulos, Pantelis, Sigaras, Alexandros, Brendel, Matthew, Barnes, Josue, Ricketts, Camir, Meleshko, Dmitry, Yat, Andy, McClure, Timothy D., Robinson, Brian D., Sboner, Andrea, Elemento, Olivier, Chughtai, Bilal, Hajirasouliha, Iman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360022/
https://www.ncbi.nlm.nih.gov/pubmed/33719168
http://dx.doi.org/10.1002/jmri.27599
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author Khosravi, Pegah
Lysandrou, Maria
Eljalby, Mahmoud
Li, Qianzi
Kazemi, Ehsan
Zisimopoulos, Pantelis
Sigaras, Alexandros
Brendel, Matthew
Barnes, Josue
Ricketts, Camir
Meleshko, Dmitry
Yat, Andy
McClure, Timothy D.
Robinson, Brian D.
Sboner, Andrea
Elemento, Olivier
Chughtai, Bilal
Hajirasouliha, Iman
author_facet Khosravi, Pegah
Lysandrou, Maria
Eljalby, Mahmoud
Li, Qianzi
Kazemi, Ehsan
Zisimopoulos, Pantelis
Sigaras, Alexandros
Brendel, Matthew
Barnes, Josue
Ricketts, Camir
Meleshko, Dmitry
Yat, Andy
McClure, Timothy D.
Robinson, Brian D.
Sboner, Andrea
Elemento, Olivier
Chughtai, Bilal
Hajirasouliha, Iman
author_sort Khosravi, Pegah
collection PubMed
description BACKGROUND: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE: To develop an artificial intelligence (AI)‐based model (named AI‐biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. STUDY TYPE: Retrospective. POPULATION: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in‐house and 172 from external publicly available databases). FIELD STRENGTH/SEQUENCE: 1.5 to 3.0 Tesla, T2‐weighted image pulse sequences. ASSESSMENT: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high‐risk tumor from low‐risk tumor. STATISTICAL TESTS: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. RESULTS: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86–0.92]) and 0.78 (95% CI: [0.74–0.82]) to classify cancer vs. benign and high‐ vs. low‐risk of prostate disease, respectively. DATA CONCLUSION: AI‐biopsy provided a data‐driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI‐biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag‐and‐drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI‐assessed MR images in real time. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2
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spelling pubmed-83600222021-08-17 A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion Khosravi, Pegah Lysandrou, Maria Eljalby, Mahmoud Li, Qianzi Kazemi, Ehsan Zisimopoulos, Pantelis Sigaras, Alexandros Brendel, Matthew Barnes, Josue Ricketts, Camir Meleshko, Dmitry Yat, Andy McClure, Timothy D. Robinson, Brian D. Sboner, Andrea Elemento, Olivier Chughtai, Bilal Hajirasouliha, Iman J Magn Reson Imaging Research Articles BACKGROUND: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE: To develop an artificial intelligence (AI)‐based model (named AI‐biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. STUDY TYPE: Retrospective. POPULATION: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in‐house and 172 from external publicly available databases). FIELD STRENGTH/SEQUENCE: 1.5 to 3.0 Tesla, T2‐weighted image pulse sequences. ASSESSMENT: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high‐risk tumor from low‐risk tumor. STATISTICAL TESTS: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. RESULTS: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86–0.92]) and 0.78 (95% CI: [0.74–0.82]) to classify cancer vs. benign and high‐ vs. low‐risk of prostate disease, respectively. DATA CONCLUSION: AI‐biopsy provided a data‐driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI‐biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag‐and‐drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI‐assessed MR images in real time. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2 John Wiley & Sons, Inc. 2021-03-14 2021-08 /pmc/articles/PMC8360022/ /pubmed/33719168 http://dx.doi.org/10.1002/jmri.27599 Text en © 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC. on behalf of International Society for Magnetic Resonance in Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Khosravi, Pegah
Lysandrou, Maria
Eljalby, Mahmoud
Li, Qianzi
Kazemi, Ehsan
Zisimopoulos, Pantelis
Sigaras, Alexandros
Brendel, Matthew
Barnes, Josue
Ricketts, Camir
Meleshko, Dmitry
Yat, Andy
McClure, Timothy D.
Robinson, Brian D.
Sboner, Andrea
Elemento, Olivier
Chughtai, Bilal
Hajirasouliha, Iman
A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion
title A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion
title_full A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion
title_fullStr A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion
title_full_unstemmed A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion
title_short A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion
title_sort deep learning approach to diagnostic classification of prostate cancer using pathology–radiology fusion
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360022/
https://www.ncbi.nlm.nih.gov/pubmed/33719168
http://dx.doi.org/10.1002/jmri.27599
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