Since 2020, breast cancer has emerged as the most prevalent cancer globally and a leading cause of cancer-related deaths among women. Affected by various genetic or environmental carcinogenic factors, breast cells undergo irreversible gene mutations, initiating the uncontrolled proliferation of malignant cells that crowd into clusters to form breast tumors. The in-situ tumors induce local tissue hypoxia in their internal and surrounding areas, leading to vascular hyperplasia, which propels the growth of cancer cells and their invasion into normal tissues.

Medical imaging is the primary tool for breast cancer screening, diagnosis, and treatment assessment. Early screening plays important roles in reducing mortality; accurate diagnosis is essential for effective treatment; and treatment assessment is critical to provide timely feedback and prognosis of cancer responses. Conventional imaging methods for breast cancer, such as mammography, ultrasonography, and magnetic resonance imaging, though widely used in clinics, exhibit limitations including low diagnostic specificity, slow imaging speed, ionizing radiation, or the need of contrast agent injection. For instance, more than 75% of patients receive benign biopsy results after ultrasound diagnosis. Furthermore, current imaging modalities lack the capacity to provide real-time monitoring, evaluation, and prognosis of the cancer responses during neoadjuvant therapy. New imaging modalities with complementary advantages are crucial to address the evolving clinical demands.

Photoacoustic imaging (PAI) is an emerging technology in the biomedical imaging field and has garnered significant attention owing to its exceptional performance. In addition to its high imaging speed, high spatiotemporal resolution, ionizing-free radiation, and abundant penetration, PAI can provide rich functional optical contrast to reveal physiological characteristics of the tumor microenvironment underneath the skin.

Multiple research groups in the PAI field have achieved notable technical breakthroughs for breast cancer screening, diagnosis, and treatment assessment. Regarding early screening, advanced PAI devices have been developed based on customized ultrasonic arrays. These devices aim to detect physiological characteristics such as vascular proliferation, increased hemoglobin concentration, and abnormal blood oxygen saturation in breast tumor areas through entire breast scanning. Some teams have explored the integration of PAI and ultrasonography, utilizing the complementary anatomical information. As concerns breast tumor diagnosis, numerous clinical studies have demonstrated that physiological characteristics in the microenvironment of a tumor can improve the distinction between benign and malignant breast tumors, facilitating accurate BI-RADS classification and reducing the chance of benign biopsy. The high imaging contrast of PAI also enables the guidance of breast sentinel lymph node biopsy with better clearance. While the combination of PAI with exogenous contrast agents and molecular probes is still in the preclinical stage, it holds the potential for more specific diagnosis in future. Regarding treatment assessment, PAI proves efficient and safe in recording physiological dynamics of the cancer microenvironment in response to therapy, offering crucial prognostic information and seamless feedback to the treatment. In addition, the label-free nature of ultraviolet PAI also provides H&E-like images without the need for staining, exhibiting early promise for accurate and rapid detection of tumor margins intraoperatively.

Regardless of the numerous advantages and multiple niche applications, PAI still faces several challenges to achieve wide clinical usage. First, the spread of PAI technologies depends on the established standards of system design, operation, and data processing to reduce the significant performance disparities among devices developed by different teams. Second, several feasibility studies have been conducted in the PAI field but large-scale clinical studies are still lacking. The PAI indicators revealed from breast cancer images have not been systematically documented or incorporated into clinical practice. Third, a gap still exists between the technical teams and clinical needs. For instance, while three-dimensional PAI exhibits better image clarity for lesion measurement, clinical practices and diagnostic analyses still heavily rely on real-time two-dimensional sectional imaging. Accordingly, to further establish its clinical value, PAI researchers need to evolve from scattered and small-scale feasibility studies to large-scale clinical trials addressing fundamental medical questions. This involves improving existing diagnostic and treatment methods and ultimately integrating them into the existing clinical framework.