ArticleOncotarget2021
Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound.
Article in Oncotarget, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 21 citations in OpenAlex.
- Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.The Kaohsiung journal of medical sciences · 2026Article
- Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.Tomography (Ann Arbor, Mich.) · 2025Article
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- Review
- Ultrasound-based radiomics for the evaluation of breast cancer.Frontiers in oncology · 2025Review
- Advancing breast cancer relapse prediction with radiomics and neural networks: a clinically interpretable framework.Frontiers in oncology · 2025Article
- Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence.International journal of clinical oncology · 2024Review
- Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study.Journal of the Egyptian National Cancer Institute · 2024Article
- Review
- Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer.Oncology letters · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.Journal of clinical medicine · 2023Review
- Review
- Article
- Quantitative Ultrasound for Evaluation of Tumour Response to Ultrasound-Microbubbles and Hyperthermia.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC). MATERIALS AND
methodsA prospective study was conducted in patients with LABC (
resultsWith a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (
conclusionsA QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.
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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.