Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI

• Published in: Scientific reports 2024-09, Vol.14 (1), p.21691-13, Article 21691
• Main Authors: Lee, Hyo-jae, Lee, Jeong Hoon, Lee, Jong Eun, Na, Yong Min, Park, Min Ho, Lee, Ji Shin, Lim, Hyo Soon
• Format: Article
• Language: English
• Subjects: 692/308; 692/4028; Adult; Aged; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Chemotherapy; Female; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Middle Aged; multidisciplinary; Neoadjuvant Therapy; Radiomics; Retrospective Studies; ROC Curve; Science; Science (multidisciplinary); Treatment Outcome; Triple Negative Breast Neoplasms - diagnostic imaging; Triple Negative Breast Neoplasms - drug therapy; Triple Negative Breast Neoplasms - pathology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-72581-y

This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644–0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614–0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699–0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.