Cargando…
Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic ana...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382057/ https://www.ncbi.nlm.nih.gov/pubmed/37511674 http://dx.doi.org/10.3390/jpm13071062 |
_version_ | 1785080598102016000 |
---|---|
author | Faraz, Khuram Dauce, Grégoire Bouhamama, Amine Leporq, Benjamin Sasaki, Hajime Bito, Yoshitaka Beuf, Olivier Pilleul, Frank |
author_facet | Faraz, Khuram Dauce, Grégoire Bouhamama, Amine Leporq, Benjamin Sasaki, Hajime Bito, Yoshitaka Beuf, Olivier Pilleul, Frank |
author_sort | Faraz, Khuram |
collection | PubMed |
description | Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER(+) vs. ER(−), PR(+) vs. PR(−), HER2(+) vs. HER2(−), and IDC vs. ILC classification tasks. The best results were obtained for the ER(+) vs. ER(−) and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers. |
format | Online Article Text |
id | pubmed-10382057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103820572023-07-29 Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches Faraz, Khuram Dauce, Grégoire Bouhamama, Amine Leporq, Benjamin Sasaki, Hajime Bito, Yoshitaka Beuf, Olivier Pilleul, Frank J Pers Med Article Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER(+) vs. ER(−), PR(+) vs. PR(−), HER2(+) vs. HER2(−), and IDC vs. ILC classification tasks. The best results were obtained for the ER(+) vs. ER(−) and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers. MDPI 2023-06-28 /pmc/articles/PMC10382057/ /pubmed/37511674 http://dx.doi.org/10.3390/jpm13071062 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Faraz, Khuram Dauce, Grégoire Bouhamama, Amine Leporq, Benjamin Sasaki, Hajime Bito, Yoshitaka Beuf, Olivier Pilleul, Frank Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches |
title | Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches |
title_full | Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches |
title_fullStr | Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches |
title_full_unstemmed | Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches |
title_short | Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches |
title_sort | characterization of breast tumors from mr images using radiomics and machine learning approaches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382057/ https://www.ncbi.nlm.nih.gov/pubmed/37511674 http://dx.doi.org/10.3390/jpm13071062 |
work_keys_str_mv | AT farazkhuram characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT daucegregoire characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT bouhamamaamine characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT leporqbenjamin characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT sasakihajime characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT bitoyoshitaka characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT beufolivier characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches AT pilleulfrank characterizationofbreasttumorsfrommrimagesusingradiomicsandmachinelearningapproaches |