ReviewDiagnostics (Basel, Switzerland)2024
Update on Sentinel Lymph Node Methods and Pathology in Breast Cancer.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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
10 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- Targeted axillary dissection in breast cancer patients: a systematic review and meta-analysis.NPJ breast cancer · 2026Article
- The Evaluation of Combined Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion Diffusion Weighted Imaging in Preoperatively Predicting Sentinel Lymph Node Metastasis of Breast Cancer.International journal of general medicine · 2026Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- The Five-Year Outcomes of Breast Cancer Surgical Management at the Colentina Surgical Clinic, Bucharest, Romania: A Descriptive Retrospective Analysis Between 2019 and 2023.Life (Basel, Switzerland) · 2025Article
- Predictive and Prognostic Values of Glycoprotein 96, Androgen Receptors, and Extranodal Extension in Sentinel Lymph Node-Positive Breast Cancer: An Immunohistochemical Retrospective Study.Journal of clinical medicine · 2024Article
- Review
- Article
- Melanin-Based Nanoparticles for Lymph Node Tattooing: Experimental, Histopathological and Ultrastructural Study.Nanomaterials (Basel, Switzerland) · 2024Article
- Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer.Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer stands out as the most commonly diagnosed cancer among women globally. Precise lymph node staging holds critical significance for both predicting outcomes in early-stage disease and formulating effective treatment strategies to control regional disease progression in breast cancer patients. No imaging technique possesses sufficient accuracy to identify lymph node metastases in the early stages (I or II) of primary breast cancer. However, the sentinel node procedure emerges as a valuable approach for identifying metastatic axillary nodes. The sentinel lymph node is the hypothetical first lymph node or group of nodes draining a cancer. In case of established cancerous dissemination, it is postulated that the sentinel lymph nodes are the target organs primarily reached by metastasizing cancer cells from the tumor. The utilization of the sentinel node technique has brought about changes in the assessment of lymph nodes. It involves evaluating the sentinel node during surgery, enabling prompt lymph node dissection when the sentinel node procedure is positive. Additionally, histological ultra-stratification is employed to uncover occult metastases. This review aims to provide an update of this valuable technique, with focus on the practical aspects of the procedure and the different histological protocols of sentinel node evaluation in breast cancer.
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.