ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2023
Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on
Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 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
- Diagnostic performance ofEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Interpretable Machine Learning Using Multimodal Ultrasound and MRI Features for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Dual-Center Retrospective Study.Journal of imaging informatics in medicine · 2026Article
- Diagnostic Potential of Apparent Diffusion Coefficient-Based Lymph Node Classification in Breast Cancer Patients Undergoing [Diagnostics (Basel, Switzerland) · 2026Article
- Risk stratification system for sentinel lymph node metastasis in clinically node-negative early breast cancer with sonographically abnormal but cytologically negative axilla.Scientific reports · 2025Article
- PET Molecular Imaging in Breast Cancer: Current Applications and Future Perspectives.Journal of clinical medicine · 2024Review
- Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [European journal of nuclear medicine and molecular imaging · 2024Article
- PET/MRI and Novel Targets for Breast Cancer.Biomedicines · 2024Review
- Article
- AI-Enhanced PET and MR Imaging for Patients with Breast Cancer.PET clinics · 2023Review
- Application of Machine Learning Analyses Using Clinical and [Molecular imaging and biology · 2023Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models.
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.