ArticleCancers2022
Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 42 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
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Diagnostic performance ofEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Leveraging immune and clinicopathological profiles with machine learning to predict axillary lymph node metastasis in breast cancer patients.Breast cancer research : BCR · 2026Observational
- Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment.Nature communications · 2026Article
- Integrating PET/CT-derived heterogeneity indices and composite risk scores improves prognostic stratification in breast cancer.European journal of nuclear medicine and molecular imaging · 2026Article
- Prognostic value of FDG PET/CT in special types of breast cancer with non-favorable histology.Discover oncology · 2025Article
- Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model.Scientific reports · 2025Article
- Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics.Scientific reports · 2024Article
- Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied toBiomedicines · 2024Article
- Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.Journal of imaging informatics in medicine · 2024Article
- Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review.World journal of surgical oncology · 2024Review
- Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images.Current oncology (Toronto, Ont.) · 2024Article
- Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.Molecular imaging and biology · 2024Article
- Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development.JMIR cancer · 2023Article
- Application of Machine Learning Analyses Using Clinical and [Molecular imaging and biology · 2023Article
- AI-Enhanced PET and MR Imaging for Patients with Breast Cancer.PET clinics · 2023Review
- Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning.NPJ breast cancer · 2023Article
- Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of the reportAccurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. MATERIALS AND
methodsA total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (
resultsA total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91-0.97) in the training set (
conclusionsThe use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.
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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.