Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

Frontiers in Oncology

Oncology

Anil Korkut¹  Jia Sun²  Peng Wei²  Huiqin Chen²  Rania M. Mohamed³  Frances Perez³  Wei Yang³  Deanna L. Lane³  Beatriz E. Adrada³  Jessica W. T. Leung³  Huong C. Le-Petross³  Rosalind P. Candelaria³  Sanaz Pashapoor³  Gaiane M. Rauch⁴  Medine Boge⁵  Jennifer K. Litton⁶  Vicente Valero⁶  Jason White⁶  Debu Tripathy⁶  Clinton Yam⁶  Kelly K. Hunt⁷  Jingfei Ma⁸  Jong Bum Son⁸  Bikash Panthi⁸  Zhan Xu⁸  Zijian Zhou⁸  Ken-Pin Hwang⁸  Lei Huo⁹  Alastair Thompson^1,10 

[1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Koc University Hospital, Istanbul, Türkiye;Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Surgery, Baylor College of Medicine, Houston, TX, United States;

关键词: triple-negative breast cancer;    dynamic contrast-enhanced MRI;    neoadjuvant systemic therapy;    radiomic analysis;    pathologic complete response;   

DOI  :  10.3389/fonc.2023.1264259

 received in 2023-07-20, accepted in 2023-10-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.

【 授权许可】

Unknown   
Copyright © 2023 Panthi, Mohamed, Adrada, Boge, Candelaria, Chen, Hunt, Huo, Hwang, Korkut, Lane, Le-Petross, Leung, Litton, Pashapoor, Perez, Son, Sun, Thompson, Tripathy, Valero, Wei, White, Xu, Yang, Zhou, Yam, Rauch and Ma