ArticleEuropean radiology2024
Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed, 36 citations in OpenAlex.
- Application of Artificial Intelligence in Breast Ultrasound Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- An Integrated Risk Prediction Model for Gout Using Clinical Data, Ultrasound Features, and Deep Learning: A Retrospective Multicenter Study.Journal of inflammation research · 2026Article
- An interpretable machine learning model based on CT imaging for predicting lymphovascular invasion and survival in bladder urothelial carcinoma: a multicenter study.BMC medical imaging · 2025Article
- Ultrasound based radiomics nomogram combined with clinical parameters to predict lymphovascular space invasion in endometrioid adenocarcinoma.Scientific reports · 2025Article
- Radiomics and deep learning model based on X-ray imaging for the assisted diagnosis of early Legg-Calvé-Perthes disease.BMC musculoskeletal disorders · 2025Article
- Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study.BMC cancer · 2025Article
- Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.BMC medical imaging · 2025Article
- Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.Insights into imaging · 2025Article
- Radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma.European journal of medical research · 2025Article
- Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features.BMC medical imaging · 2025Article
- Preoperative Imaging Assessment of Lymphovascular Invasion in Breast Cancer: Current Evidence, Technical Advances, and Future Directions.Breast cancer (Dove Medical Press) · 2025Review
- Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer.Frontiers in oncology · 2025Article
- Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features.BMC medical imaging · 2024Article
- A radiomics model of contrast-enhanced computed tomography for predicting post-acute pancreatitis diabetes mellitus.Quantitative imaging in medicine and surgery · 2024Article
- Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.Breast cancer (Dove Medical Press) · 2024Article
- The role of multiparametric MRI in predicting lymphovascular invasion in breast cancer patients.Future oncology (London, England) · 2024Article
- The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Integrating Peritumoral and Intratumoral Radiomics with Deep Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer Using DCE-MRI.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). MATERIALS AND
methodsIn this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (n = 320) and test (n = 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
resultsThe nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84-0.91), 0.89 (95% CI, 0.84-0.94) and 0.95 (95% CI, 0.92-0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (p < 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84-0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.
conclusionsThe radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI. CLINICAL RELEVANCE STATEMENT: The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer. KEY POINTS: • The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone. • The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets). • A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130).
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.