AUTHOR=Panthi Bikash , Mohamed Rania M. , Adrada Beatriz E. , Boge Medine , Candelaria Rosalind P. , Chen Huiqin , Hunt Kelly K. , Huo Lei , Hwang Ken-Pin , Korkut Anil , Lane Deanna L. , Le-Petross Huong C. , Leung Jessica W. T. , Litton Jennifer K. , Pashapoor Sanaz , Perez Frances , Son Jong Bum , Sun Jia , Thompson Alastair , Tripathy Debu , Valero Vicente , Wei Peng , White Jason , Xu Zhan , Yang Wei , Zhou Zijian , Yam Clinton , Rauch Gaiane M. , Ma Jingfei TITLE=Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer JOURNAL=Frontiers in Oncology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1264259 DOI=10.3389/fonc.2023.1264259 ISSN=2234-943X ABSTRACT=

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.