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Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning

OBJECTIVE: This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer....

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Autores principales: Lee, Hyo-jae, Nguyen, Anh-Tien, Ki, So Yeon, Lee, Jong Eun, Do, Luu-Ngoc, Park, Min Ho, Lee, Ji Shin, Kim, Hye Jung, Park, Ilwoo, Lim, Hyo Soon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679659/
https://www.ncbi.nlm.nih.gov/pubmed/34926256
http://dx.doi.org/10.3389/fonc.2021.744460
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author Lee, Hyo-jae
Nguyen, Anh-Tien
Ki, So Yeon
Lee, Jong Eun
Do, Luu-Ngoc
Park, Min Ho
Lee, Ji Shin
Kim, Hye Jung
Park, Ilwoo
Lim, Hyo Soon
author_facet Lee, Hyo-jae
Nguyen, Anh-Tien
Ki, So Yeon
Lee, Jong Eun
Do, Luu-Ngoc
Park, Min Ho
Lee, Ji Shin
Kim, Hye Jung
Park, Ilwoo
Lim, Hyo Soon
author_sort Lee, Hyo-jae
collection PubMed
description OBJECTIVE: This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer. MATERIALS AND METHODS: One hundred seventy-four MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance. RESULTS: The RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC: 73.3%, 69.6% for the SUB RA model; 70.0%, 75.1% for the T2 RA model; 73.3%, 72.0% for the CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared with the RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively. CONCLUSION: Our study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone.
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spelling pubmed-86796592021-12-18 Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning Lee, Hyo-jae Nguyen, Anh-Tien Ki, So Yeon Lee, Jong Eun Do, Luu-Ngoc Park, Min Ho Lee, Ji Shin Kim, Hye Jung Park, Ilwoo Lim, Hyo Soon Front Oncol Oncology OBJECTIVE: This study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer. MATERIALS AND METHODS: One hundred seventy-four MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance. RESULTS: The RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC: 73.3%, 69.6% for the SUB RA model; 70.0%, 75.1% for the T2 RA model; 73.3%, 72.0% for the CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared with the RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively. CONCLUSION: Our study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone. Frontiers Media S.A. 2021-12-02 /pmc/articles/PMC8679659/ /pubmed/34926256 http://dx.doi.org/10.3389/fonc.2021.744460 Text en Copyright © 2021 Lee, Nguyen, Ki, Lee, Do, Park, Lee, Kim, Park and Lim 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
Lee, Hyo-jae
Nguyen, Anh-Tien
Ki, So Yeon
Lee, Jong Eun
Do, Luu-Ngoc
Park, Min Ho
Lee, Ji Shin
Kim, Hye Jung
Park, Ilwoo
Lim, Hyo Soon
Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning
title Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning
title_full Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning
title_fullStr Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning
title_full_unstemmed Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning
title_short Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning
title_sort classification of mr-detected additional lesions in patients with breast cancer using a combination of radiomics analysis and machine learning
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679659/
https://www.ncbi.nlm.nih.gov/pubmed/34926256
http://dx.doi.org/10.3389/fonc.2021.744460
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