ArticleDiscover oncology2025
Multi-center strain elastography radiomics for breast cancer diagnosis: integrating intratumoral and peritumoral regions.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence-assisted ultrasound elastography in classifying benign and malignant breast tumors: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.Journal of imaging informatics in medicine · 2026Review
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop and validate a novel strain elastography (SE) radiomics nomogram for diagnosing breast cancer (BC) by analyzing intratumoral and peritumoral regions.
methodsA cohort of 322 patients, comprising 217 from hospital #1 (06/2021-05/2023) and 105 from hospital #2 (06/2022-05/2023) with breast lesions, was enrolled. Radiomic features were extracted from intratumoral and peritumoral (0-1 mm, 1-2 mm, 2-3 mm) regions on strain elastography images. Significant features were selected using Mann-Whitney U test, Spearman's correlation coefficient, and LASSO logistic regression. A radiomic model was constructed utilizing these features, followed by the development of a radiomic nomogram integrating optimal features.
resultsThe intratumoral radiomic model exhibited an area under the receiver operating characteristic curve (AUC) of 0.774 (95% CI: 0.626-0.922) in the internal testing set. Combining peritumoral radiomics, the intratumoral & peritumoral_0-1 mm radiomic model emerged as the optimal model with an AUC of 0.884 (95% CI: 0.766-0.998) in the internal testing set, signifying improved BC identification. The optimal model demonstrated an AUC of 0.841 (95% CI: 0.762-0.920) in the external testing set, indicating robustness and generalization.
conclusionsThe radiomic model incorporating intratumoral & peritumoral_0-1 mm radiomic features shows promise in diagnosing BC, aiding in devising effective clinical treatment strategies.
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