Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer

Abstract The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that com...

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Autores principales:	Sunghoon Joo, Eun Sook Ko, Soonhwan Kwon, Eunjoo Jeon, Hyungsik Jung, Ji-Yeon Kim, Myung Jin Chung, Young-Hyuck Im
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89bbfdeddf364266aeeb4cea1e90b29a
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spelling	oai:doaj.org-article:89bbfdeddf364266aeeb4cea1e90b29a2021-12-02T18:13:45ZMultimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer10.1038/s41598-021-98408-82045-2322https://doaj.org/article/89bbfdeddf364266aeeb4cea1e90b29a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98408-8https://doaj.org/toc/2045-2322Abstract The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.Sunghoon JooEun Sook KoSoonhwan KwonEunjoo JeonHyungsik JungJi-Yeon KimMyung Jin ChungYoung-Hyuck ImNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sunghoon Joo Eun Sook Ko Soonhwan Kwon Eunjoo Jeon Hyungsik Jung Ji-Yeon Kim Myung Jin Chung Young-Hyuck Im Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
description	Abstract The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.
format	article
author	Sunghoon Joo Eun Sook Ko Soonhwan Kwon Eunjoo Jeon Hyungsik Jung Ji-Yeon Kim Myung Jin Chung Young-Hyuck Im
author_facet	Sunghoon Joo Eun Sook Ko Soonhwan Kwon Eunjoo Jeon Hyungsik Jung Ji-Yeon Kim Myung Jin Chung Young-Hyuck Im
author_sort	Sunghoon Joo
title	Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
title_short	Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
title_full	Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
title_fullStr	Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
title_full_unstemmed	Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
title_sort	multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/89bbfdeddf364266aeeb4cea1e90b29a
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Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer

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