What Is Breast Cancer?

Breast cancer is a malignant disease in which cells of the breast tissue proliferate uncontrollably, forming tumors that can invade adjacent tissue and spread to distant organs through the lymphatic and circulatory systems. It is the most commonly diagnosed cancer worldwide, accounting for roughly 2.3 million new cases annually according to the World Health Organization. In biomedical engineering and clinical research, breast cancer is studied as a target for imaging, detection algorithms, therapeutic device design, and computational modeling, making it one of the most intensively engineered disease areas in medicine.

The disease originates in the ductal or lobular epithelium of the breast and is classified by histological type, grade, and molecular subtype. Molecular subtyping, based on expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), divides tumors into biologically distinct groups that respond differently to systemic therapy, guiding treatment selection in clinical practice.

Pathology and Molecular Subtypes

Breast cancers are broadly divided into luminal (hormone receptor-positive), HER2-enriched, and triple-negative subtypes. Luminal A tumors are low-grade and highly hormone-responsive, with favorable prognosis and sensitivity to endocrine therapy. Luminal B tumors have higher proliferation rates and may also overexpress HER2. HER2-enriched cancers benefit from HER2-targeted agents such as trastuzumab. Triple-negative breast cancer (TNBC), which lacks expression of ER, PR, and HER2, carries the worst prognosis and is treated primarily with chemotherapy, though immune checkpoint inhibitors have shown efficacy in a subset of TNBC patients. The National Cancer Institute's cancer information on breast cancer biology covers these molecular classifications and their clinical implications in detail.

Detection and Screening Technology

Engineering contributions to breast cancer detection center on imaging systems and computational analysis pipelines. X-ray mammography is the established population screening tool, with digital mammography largely replacing film-based systems since the 2000s. Deep learning models trained on large mammography datasets have demonstrated diagnostic accuracy matching or exceeding radiologist performance in controlled studies. A review in PMC of mammography with deep learning for breast cancer detection found that convolutional neural networks achieved an accuracy of 0.88, above the radiologist benchmark of 0.83 in comparable test conditions. Digital breast tomosynthesis (DBT), approved by the FDA in 2011, further improves sensitivity in dense breast tissue by reconstructing three-dimensional image volumes from multi-angle mammographic acquisitions.

Treatment and Therapeutic Engineering

Biomedical engineering plays a central role in breast cancer treatment as well as detection. Radiation therapy, delivered using linear accelerators and multi-leaf collimators, applies precisely shaped radiation fields to the tumor volume while sparing surrounding tissue. Intensity-modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) achieve geometric conformality through computer-optimized treatment planning. In surgical treatment, image-guided lumpectomy systems use intraoperative ultrasound or pre-placed markers to help surgeons achieve clear margins around the tumor. Targeted drug delivery systems, including antibody-drug conjugates and liposomal nanoparticles, are engineered to concentrate cytotoxic agents at tumor sites while limiting systemic toxicity, a research area reviewed in IEEE Transactions on Biomedical Engineering and related publications.

Applications

Research and engineering related to breast cancer spans a range of clinical and translational domains, including:

• Population mammography screening and computer-aided detection (CAD) systems
• Surgical planning and intraoperative navigation for tumor margin assessment
• Radiation therapy delivery systems and treatment planning software
• Liquid biopsy and blood-based biomarker detection for early recurrence monitoring
• Preclinical tumor model development for drug candidate evaluation