ArticleCancers2021
Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- A pathology-based surrogate model for chemotherapy decision-making in intermediate-risk luminal breast cancer: validation of histologic grade and Ki67 in a Chinese population.Frontiers in medicine · 2026Article
- Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.Radiology. Imaging cancer · 2024Article
- Prediction of sentinel lymph node metastasis in breast cancer patients based on preoperative features: a deep machine learning approach.Scientific reports · 2024Article
- Predictive factors of axillary lymph node involvement in Tunisian women with early breast cancer.African health sciences · 2023Article
- Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer.Scientific reports · 2023Article
- MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.Cancers · 2023Article
- Impact of the systematic introduction of tomosynthesis on breast biopsies: 10 years of results.La Radiologia medica · 2023Article
- Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.Scientific reports · 2023Article
- State of the Art in 2022 PET/CT in Breast Cancer: A Review.Journal of clinical medicine · 2023Review
- Vision-Transformer-Based Transfer Learning for Mammogram Classification.Diagnostics (Basel, Switzerland) · 2023Article
- An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy.Frontiers in oncology · 2023Article
- Lean Perspectives in an Organizational Change in a Scientific Direction of an Italian Research Institute: Experience of the Cancer Institute of Bari.International journal of environmental research and public health · 2022Article
- HER2-Low Status Is Not Accurate in Breast Cancer Core Needle Biopsy Samples: An Analysis of 5610 Consecutive Patients.Cancers · 2022Article
- Assessing Whether Morphological Changes in Axillary Lymph Node Have Already Occurred Prior to Metastasis in Breast Cancer Patients by Ultrasound.Medicina (Kaunas, Lithuania) · 2022Article
- Article
- A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients.Scientific reports · 2022Article
- Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer.Frontiers in oncology · 2022Article
- A Nomogram Based on Molecular Biomarkers and Radiomics to Predict Lymph Node Metastasis in Breast Cancer.Frontiers in oncology · 2022Article
- Prevalence of Patients Affected by Fibromyalgia in a Cohort of Women Underwent Mammography Screening.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.
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