ReviewFrontiers in oncology2025
Diagnostic dilemma of lobular carcinoma: a mini-review of imaging modalities and the role of artificial intelligence and radiomics.
Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Prospective study onBritish journal of cancer · 2026Trial
- A rare case of synchronous bilateral breast cancer with distinct histological subtypes.International journal of surgery case reports · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.