Deep Learning and Radiomics in Triple-Negative Breast Cancer: Predicting Long-Term Prognosis and Clinical Outcomes.

Abstract

Triple-negative breast cancer (TNBC) is a unique breast cancer subtype characterized by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression in tumor cells. TNBC represents about 15% to 20% of all breast cancers and is aggressive and highly malignant. Currently, TNBC diagnosis primarily depends on pathological examination, while treatment efficacy is assessed through imaging, biomarker detection, pathological evaluation, and clinical symptom improvement. Among these, biomarker detection and pathological assessments are invasive, time-intensive procedures that may be difficult for patients with severe comorbidities and high complication risks. Thus, there is an urgent need for new, supportive tools in TNBC diagnosis and treatment. Deep learning and radiomics techniques represent advanced machine learning methodologies and are also emerging outcomes in the medical-engineering field in recent years. They are extensions of conventional imaging diagnostic methods and have demonstrated tremendous potential in image segmentation, reconstruction, recognition, and classification. These techniques hold certain application prospects for the diagnosis of TNBC, assessment of treatment response, and long-term prognosis prediction. This article reviews recent progress in the application of deep learning, ultrasound, MRI, and radiomics for TNBC diagnosis and treatment, based on research from both domestic and international scholars.