Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast i...

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Autores principales:	Shuyi Peng, Leqing Chen, Juan Tao, Jie Liu, Wenying Zhu, Huan Liu, Fan Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	breast cancer magnetic resonance imaging radiomics neoadjuvant treatment treatment response Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/7a6af5d3bd4144bf9d63ef33532b735a
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spelling	oai:doaj.org-article:7a6af5d3bd4144bf9d63ef33532b735a2021-11-25T17:21:30ZRadiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer10.3390/diagnostics111120862075-4418https://doaj.org/article/7a6af5d3bd4144bf9d63ef33532b735a2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2086https://doaj.org/toc/2075-4418Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.Shuyi PengLeqing ChenJuan TaoJie LiuWenying ZhuHuan LiuFan YangMDPI AGarticlebreast cancermagnetic resonance imagingradiomicsneoadjuvant treatmenttreatment responseMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2086, p 2086 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast cancer magnetic resonance imaging radiomics neoadjuvant treatment treatment response Medicine (General) R5-920
spellingShingle	breast cancer magnetic resonance imaging radiomics neoadjuvant treatment treatment response Medicine (General) R5-920 Shuyi Peng Leqing Chen Juan Tao Jie Liu Wenying Zhu Huan Liu Fan Yang Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer
description	Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.
format	article
author	Shuyi Peng Leqing Chen Juan Tao Jie Liu Wenying Zhu Huan Liu Fan Yang
author_facet	Shuyi Peng Leqing Chen Juan Tao Jie Liu Wenying Zhu Huan Liu Fan Yang
author_sort	Shuyi Peng
title	Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer
title_short	Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer
title_full	Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer
title_fullStr	Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer
title_full_unstemmed	Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer
title_sort	radiomics analysis of multi-phase dce-mri in predicting tumor response to neoadjuvant therapy in breast cancer
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7a6af5d3bd4144bf9d63ef33532b735a
work_keys_str_mv	AT shuyipeng radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer AT leqingchen radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer AT juantao radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer AT jieliu radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer AT wenyingzhu radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer AT huanliu radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer AT fanyang radiomicsanalysisofmultiphasedcemriinpredictingtumorresponsetoneoadjuvanttherapyinbreastcancer
_version_	1718412478677778432

Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer

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