Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies

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Authors: Dinesh Kumar, Neeraj Choudhary, Simranjeet Kaur, Suresh Babu Kondaveeti
Cited by: 3
References: 184

Abstract

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and care for survivors. The understanding of tumour heterogeneity has been greatly improved by the integration of multi-omics technologies, including proteomics, metabolomics, and genomics, which has made it possible to establish individualized treatment strategies. Precision oncology has been transformed by next-generation sequencing, liquid biopsy, and AI-driven predictive modelling, which enable targeted medication selection and real-time illness monitoring. Robotic-assisted mastectomy, intraoperative radiation therapy, and AI-guided adaptive radiotherapy are examples of innovations in radiation and surgical oncology that have increased treatment precision while reducing long-term complications. Advances in immunotherapy, such as TCR-T therapy, CAR-T cell therapy, and immune checkpoint inhibitors, are revolutionizing the treatment of breast cancer, especially aggressive subtypes like triple-negative breast cancer. By improving patient quality of life, developments in wearable biosensors, digital health tools, and mental health AI platforms are tackling survivorship issues. Breast cancer therapies are continually being enhanced by AI-powered drug discovery and personalized therapy algorithms, which optimize treatment effectiveness while minimizing toxicity. Issues, including therapeutic resistance, accessibility, and healthcare disparities, still exist despite these developments. Future studies that combine real-time molecular profiling, adaptive clinical decision-making, and artificial intelligence have the potential to significantly transform the treatment of breast cancer and enhance patient outcomes.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging AI in cancer detection Cancer Genomics and Diagnostics

Publication details

Journal / source
Discover Oncology
Publisher
Springer Science and Business Media LLC
Publication date
24 January 2026
Language
EN