Student and Faculty Publications
Publication Date
1-20-2023
Journal
Scientific Reports
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.
Keywords
Humans, Female, Triple Negative Breast Neoplasms, Breast Neoplasms, Multiparametric Magnetic Resonance Imaging, Deep Learning, Neoadjuvant Therapy, Prospective Studies, Magnetic Resonance Imaging, Retrospective Studies, Cancer, Breast cancer, Cancer imaging, Machine learning, Predictive markers
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Neoplasms Commons, Oncology Commons
Comments
Supplementary Materials
Data Availability Statement
PMID: 36670144