Diagnostic dilemma of lobular carcinoma: a mini-review of imaging modalities and the role of artificial intelligence and radiomics - Jabbari, Sima; Shadan, Mariam; Eltayeb, Yousef; Salim, Omer El Faroug; Afaq, Nimrah; Haider, Rushda; Shaikh, Sana Sohail (Frontiers in Oncology, 2025)
Lobular carcinoma (LC) presents unique diagnostic challenges due to its subtle imaging characteristics and asymptomatic presentation, often leading to delays in diagnosis and treatment. This mini-review critically examines both traditional and advanced imaging modalities used to detect and manage LC, including mammography, ultrasound, digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), breast magnetic resonance imaging (MRI), and breast-specific gamma imaging (BSGI). Traditional modalities like mammography and ultrasound, while widely used, have limitations, particularly in detecting LC in patients with dense breast tissue. Advanced techniques, such as MRI and BSGI, offer improved sensitivity and specificity but are limited by cost and accessibility. Emerging technologies such as artificial intelligence (AI) and radiomics are reshaping the diagnostic landscape for LC. AI has shown promise in enhancing diagnostic accuracy, predicting treatment outcomes, and improving risk stratification by analyzing large datasets from multiple sources, including imaging, genomic, and clinical data. Radiomics, which extracts quantitative features from medical images, further complements AI by providing detailed insights into tumor characteristics, treatment responses, and molecular subtypes of breast cancer, including LC. Together, AI and radiomics have the potential to revolutionize the detection, characterization, and monitoring of LC, particularly by enhancing the accuracy of traditional imaging methods and supporting personalized treatment strategies. This review also provides actionable recommendations for clinicians, radiologists, and researchers on the integration of advanced imaging techniques and AI into clinical workflows. With continued advancements, AI and radiomics are poised to improve the early detection and management of LC, ultimately contributing to better patient outcomes.