ArticleFrontiers in oncology2021
Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy.
Article in Frontiers in oncology, 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.
- 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
- Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features.Breast cancer research : BCR · 2026Article
- Development and validation of a novel nomogram predicting axillary lymph node metastasis among breast cancer patients in Egypt.Scientific reports · 2026Article
- A clinicopathological and spatial feature-based nomogram for individualized prediction of axillary lymph node involvement in breast cancer.World journal of surgical oncology · 2026Article
- Predicting preoperative axillary lymph node metastasis to guide surgical decisions in invasive breast cancer.Frontiers in oncology · 2026Article
- Construction of a prediction model for axillary lymph node metastasis in stage cN0 hormone receptor-positive breast cancer: based on interpretable machine learning methods.Frontiers in oncology · 2026Article
- Management of the Axilla in Older Patients with Breast Cancer: Reassessing the Role of Sentinel Lymph Node Biopsy.Cancers · 2025Article
- Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging, ultrasound, and clinical parameters.Breast cancer research : BCR · 2025Article
- Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.Scientific reports · 2025Article
- Prediction of axillary lymph node metastasis in T1 breast cancer using diffuse optical tomography, strain elastography and molecular markers.Quantitative imaging in medicine and surgery · 2025Article
- New progress in imaging diagnosis and immunotherapy of breast cancer.Frontiers in immunology · 2025Review
- SLC25A19 drives colorectal cancer progression by regulating p53.Cancer medicine · 2024Article
- New Insight for Axillary De-Escalation in Breast Cancer Surgery: "SoFT Study" Retrospective Analysis.Current oncology (Toronto, Ont.) · 2024Article
- Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer.NPJ breast cancer · 2024Article
- Prediction of sentinel lymph node metastasis in breast cancer patients based on preoperative features: a deep machine learning approach.Scientific reports · 2024Article
- Pathological characteristics predicting sentinel lymph node metastasis in early breast cancer patients.Caspian journal of internal medicine · 2024Article
- Cryoablation Allows the Ultimate De-escalation of Surgical Therapy for Select Breast Cancer Patients.Annals of surgical oncology · 2023Article
- Photothermally responsive theranostic nanocomposites for near-infrared light triggered drug release and enhanced synergism of photothermo-chemotherapy for gastric cancer.Bioengineering & translational medicine · 2023Article
- Prevalence of Pathologic N2/N3 Disease in Postmenopausal Women with Clinical N0 ER+/HER2- Breast Cancer.Annals of surgical oncology · 2022Article
- Internal mammary lymph node metastasis in breast cancer patients based on anatomical imaging and functional imaging.Breast cancer (Tokyo, Japan) · 2022Review
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10 authors.
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Abstract
backgroundSentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied on postoperative data, thus limiting their application in the preoperative setting. Therefore, it is necessary to find a new model for preoperative risk prediction for SLN metastasis to help clinicians facilitate individualized clinical decisions. MATERIALS AND
methodsBC patients who underwent SLN biopsy in two different institutions were included in the training and validation cohorts. Demographic characteristics, preoperative tumor pathological features, and ultrasound findings were evaluated. Multivariate logistic regression was used to develop the nomogram. The discrimination, accuracy, and clinical usefulness of the nomogram were assessed using Harrell's C-statistic and ROC analysis, the calibration curve, and the decision curve analysis, respectively.
resultsA total of 624 patients who met the inclusion criteria were enrolled, including 444 in the training cohort and 180 in the validation cohort. Young age, high BMI, high Ki67, large tumor size, indistinct tumor margins, calcifications, and an aspect ratio ≥1 were independent predictive factors for SLN metastasis of BC. Incorporating these parameters, the nomogram achieved a robust predictive performance with a C-index and accuracy of 0.92 and 0.85, and 0.82 and 0.80 in the training and validation cohorts, respectively. The calibration curves also fit well, and the decision curve analysis revealed that the nomogram was clinically useful.
conclusionsWe established a nomogram to preoperatively predict the risk of SLN metastasis in BC patients, providing a non-invasive approach in clinical practice and serving as a potential tool to identify BC patients who may omit unnecessary SLN biopsy.
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