ReviewCancers2025
Unique Molecular Alteration of Lobular Breast Cancer: Association with Pathological Classification, Tumor Biology and Behavior, and Clinical Management.
Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Article
- ESE and Transfer Learning for Breast Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- Development and validation of a ferroptosis-related gene signature for prognostic prediction and therapeutic target identification in invasive lobular carcinoma.Translational cancer research · 2026Article
- Multimodal breast cancer diagnosis using feature fusion and deep learning.Frontiers in medicine · 2026Article
- Invasive lobular breast carcinoma variants; clinicopathological features and patient outcomes.Breast cancer research and treatment · 2025Article
- Single-cell transcriptomics in metastatic breast cancer: mapping tumor evolution and therapeutic resistance.Frontiers in genetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Invasive lobular carcinoma (ILC), accounting for up to 15% of diagnosed breast cancers, has garnered significant attention due to the loss of the epithelial cell-cell adhesion molecule E-cadherin. This loss contributes to its distinct biological, morphological, and clinical characteristics compared to non-lobular breast cancers. The use of immunohistochemistry (IHC) for E-cadherin and/or the associated cadherin-catenin complex, such as p120-catenin and beta-catenin, in morphologically equivocal cases, has been increasingly adopted in pathology practice. This approach has substantially improved diagnostic accuracy, interobserver reproducibility, and the identification of new morphologic variants of ILC. ILCs exhibit unique tumor biology, which presents considerable challenges in clinical management, especially in preoperative imaging evaluation, surgical management, and neoadjuvant treatment. Recent advances in translational and clinical research have enhanced our understanding of ILC and have spurred the development of new clinical trials specifically targeting these cancers. This review highlights recent progress in various aspects of ILC, including its unique molecular alteration, pathological classification and diagnostic approach, tumor biology and behavior, key clinical management challenges, and ongoing clinical trials, as well as the role of artificial intelligence in diagnosing ILC radiologically and pathologically. The goal of this review is to provide an updated understanding of the tumor biology, clinical manifestations, and molecular landscape of ILC and to help refine current tumor classification and diagnosis, subsequently improving management strategies and overall outcomes for lobular carcinoma patients.
Indexed as
Identifiers
What OpenQuestion holds
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.