Abstract :
The aggressive and diverse subtype of breast cancer known as triple-negative breast cancer (TNBC) is devoid of HER2, progesterone, and oestrogen receptors. Due to the lack of actionable molecular targets, TNBC, which accounts for 15–20% of patients, is linked to a poor prognosis, early metastases, and few therapeutic choices. Although they are still the major therapeutic options, conventional treatments including radiation,chemotherapy, and surgery are limited by toxicity, drug resistance, and high recurrence rates.The management of TNBC now has more options thanks to developments in artificial intelligence (AI) and precision medicine. Targeted treatments like PARP inhibitors and immune checkpoint inhibitors are supported by the discovery of actionable changes, such as BRCA1/2 mutations, made possible by genomic and proteomic analysis. By combining multi-omics, imaging, and clinical data, AI improves diagnosis, tumour subclassification, treatment response prediction, and medication repurposing. AI-driven precision medicine has great promise to enhance tailored treatment plans and clinical results in TNBC, despite obstacles pertaining to data privacy, equality, and legislation.
Keyword :
Triple-Negative Breast Cancer, Precision Medicine, Artificial Intelligence, Targeted therapy, diagnosis of TNBC