ArticleEuropean radiology2022
Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 3 of them syntheses 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
59 citing papers in PubMed, 3 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- ESR Essentials: artificial intelligence in breast imaging-practice recommendations by the European Society of Breast Imaging.European radiology · 2026Guideline
- 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
- Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.Frontiers in medicine · 2024Pooled it
- Habitat-based ultrasound radiomics for preoperative prediction of axillary lymph node metastasis in breast cancer: a two-center study.Gland surgery · 2026Article
- A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.Abdominal radiology (New York) · 2026Article
- Article
- Application of Artificial Intelligence in Breast Ultrasound Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Dual-mode 3D shear wave elastography with qualitative and quantitative assessment improves diagnosis of inguinal lymph node metastasis.Quantitative imaging in medicine and surgery · 2026Article
- DeiT-MLP-mixer for the preoperative prediction of axillary lymph node involvement in breast cancer via ultrasound imaging.BMC medical imaging · 2026Article
- Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020-2025).Journal of clinical monitoring and computing · 2026Article
- Lymph Node Assessment with Multiparametric Ultrasound: Normal Values, Morphologic Patterns, and Diagnostic Algorithms.Cancers · 2026Article
- Augmented Prediction of N Parameter in Breast Cancer: Is It Possible with Shear-Wave Elastography Ultrasound Radiomics?Cancers · 2026Article
- Preoperative assessment of axillary lymph node tumor burden in cT1-2N0 breast cancer patients with a modality-adaptive network based on sentinel lymph node ultrasound images.Scientific reports · 2026Article
- Advances in ultrasound for preoperative molecular subtyping of malignant breast tumors.Frontiers in oncology · 2026Review
- Deep-learning-based radiomics of intratumoral and peritumoral CT images to differentiate benign from malignant parotid tumors.American journal of cancer research · 2026Article
- Predictive value of ABVS and VTIQ parameters for axillary lymph node metastasis in breast cancer.Frontiers in cell and developmental biology · 2026Article
- Random Forest-Based Detection of Metastases in Clinically Scanned Lymph Nodes Using Quantitative Ultrasound Imaging.Ultrasound in medicine & biology · 2025Article
- Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.Scientific reports · 2025Article
- The development and validation of a risk stratification system for assessing axillary status after neoadjuvant therapy in node-positive breast cancer: a multicenter, prospective, observational study.International journal of surgery (London, England) · 2025Observational
- Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study.The oncologist · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesTo develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.
methodsData of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.
resultsSWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort.
conclusionThe radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. KEY POINTS: • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.
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.