ArticleBiomedical engineering online2024
Differentiation between invasive ductal carcinoma and ductal carcinoma in situ by combining intratumoral and peritumoral ultrasound radiomics.
Article in Biomedical engineering online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the radiologic assessment of ductal carcinomaFrontiers in oncology · 2026Pooled it
- Integrated ultrasound-based radiomics and deep learning models in screening breast intraductal high-risk lesions or carcinoma: a multicenter retrospective study.Frontiers in oncology · 2026Article
- An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy.Biomedical engineering online · 2025Article
- AI-Based Ultrasound Nomogram for Differentiating Invasive from Non-Invasive Breast Cancer Masses.Cancers · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundThis study aimed to develop and validate an ultrasound radiomics model for distinguishing invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS) by combining intratumoral and peritumoral features.
methodsRetrospective analysis was performed on 454 patients from Chengzhong Hospital. The patients were randomly divided in accordance with a ratio of 8:2 into a training group (363 cases) and validation group (91 cases). In addition, 175 patients from Yanghu Hospital were used as the external test group. The peritumoral ranges were set to 2, 4, 6, 8, and 10 mm. Mann-Whitney U-test, recursive feature elimination, and a least absolute shrinkage and selection operator were used to in the dimension reduction of the radiomics features and clinical knowledge, and machine learning logistic regression classifiers were utilized to construct the diagnostic model. The area under the curve (AUC) of the receiver operating characteristics, accuracy, sensitivity, and specificity were used to evaluate the model performance.
resultsBy combining peritumoral features of different ranges, the AUC of the radiomics model was improved in the validation and test groups. In the validation group, the maximum increase in AUC was 9.7% (P = 0.031, AUC = 0.803) when the peritumoral range was 8 mm. Similarly, when the peritumoral range was only 8 mm in the test group, the maximum increase in AUC was 4.9% (P = 0.005, AUC = 0.770). In this study, the best prediction performance was achieved when the peritumoral range was only 8 mm.
conclusionsThe ultrasound-based radiomics model that combined intratumoral and peritumoral features exhibits good ability to distinguish between IDC and DCIS. The selection of peritumoral range size exerts an important effect on the prediction performance of the radiomics model.
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