ArticleJournal of translational medicine2021
Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.Nature communications · 2026Article
- Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer.Cell death & disease · 2025Article
- Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.BMC medical imaging · 2025Article
- Article
- A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer.Breast cancer (Tokyo, Japan) · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Multimodality MRI radiomics analysis of TP53 mutations in triple negative breast cancer.Frontiers in oncology · 2023Article
- Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer.Journal of translational medicine · 2022Article
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundThis study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions.
methodsThis retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADC
resultsRF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC
conclusionsThe radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.
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Registered trials
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