The Role of Tumor pH in Breast Cancer Imaging: Biology, Diagnostic Applications, and Emerging Techniques

Breast cancer screening, while vital for reducing mortality, faces significant limitations in sensitivity and specificity, particularly in dense breasts. Current modalities primarily detect anatomical changes, often missing biologically aggressive tumors at their earliest stages. The altered metabolism of cancer cells establishes a characteristic inverted pH gradient that drives tumor invasion, metastasis, and treatment resistance. This makes tumor acidity a compelling, functional biomarker for early detection. This review synthesizes the emerging role of pH as a diagnostic biomarker and provides a critical evaluation of advanced imaging techniques for its non-invasive or minimal measurement. We detail the biological underpinnings of tumor acidosis, emphasizing its regulation through glycolytic reprogramming and dysregulated proton transport. Our analysis encompasses a broad spectrum of pH-sensitive imaging modalities, including magnetic resonance methods such as Chemical Exchange Saturation Transfer (CEST) MRI for extracellular pH mapping and multi-nuclear Magnetic Resonance Spectroscopy (MRS) using ¹H, ³¹P, and ¹⁹F nuclei to probe various cellular compartments. Furthermore, we examine hyperpolarized ¹³C MRI for real-time metabolic flux imaging, where metrics such as the lactate-to-pyruvate ratio demonstrate significant predictive value for treatment response. The review also assesses optical and photoacoustic imaging techniques, which offer high sensitivity but are often constrained to superficial tumors. Imaging tumor pH provides a powerful functional window into the earliest metabolic shifts in breast cancer, far preceding macroscopic anatomical changes. The ongoing development and evidence support the role of the pH-sensitive imaging techniques in diagnosis, lesion characterization, and therapy. Additionally, it holds promise for supplementing breast cancer screening by enabling earlier, more specific detection and personalized risk stratification, ultimately aiming to improve patient outcomes.