ArticleInternational journal of computer assisted radiology and surgery2019
Breast tumor classification using different features of quantitative ultrasound parametric images.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematized Review of Quantitative Ultrasound Based on First-Order Speckle Statistics.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Pooled it
- Breast tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Random Forest-Based Detection of Metastases in Clinically Scanned Lymph Nodes Using Quantitative Ultrasound Imaging.Ultrasound in medicine & biology · 2025Article
- Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Homodyned K-Distribution Temporal-Based Characterization of Rat Placenta Microstructure Using the Reduced Uterine Perfusion Pressure Model of Preeclampsia.Ultrasound in medicine & biology · 2025Article
- Application and validation of the machine learning-based multimodal radiomics model for preoperative prediction of lateral lymph node metastasis in papillary thyroid carcinoma.Frontiers in endocrinology · 2025Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.La Radiologia medica · 2024Article
- A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography.Biomedical engineering online · 2024Article
- Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features.Frontiers in oncology · 2023Article
- Cystic (including atypical) and solid breast lesion classification using the different features of quantitative ultrasound parametric images.International journal of computer assisted radiology and surgery · 2022Article
- Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.Computational and mathematical methods in medicine · 2022Article
- An Optimized Framework for Breast Cancer Classification Using Machine Learning.BioMed research international · 2022Article
- Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis.Frontiers in oncology · 2022Article
- Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images.Diagnostics (Basel, Switzerland) · 2021Review
- Artificial Intelligence in Medical Imaging of the Breast.Frontiers in oncology · 2021Review
- Synthesis, radiolabelling, and biological assessment of folic acid-conjugated G-3IET nanobiotechnology · 2020Article
- Regularized Estimation of Effective Scatterer Size and Acoustic Concentration Quantitative Ultrasound Parameters Using Dynamic ProgrammingAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
RATIONALE AND
objectivesThe ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors. MATERIALS AND
methodsTo verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson's correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability.
resultsThe best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification.
conclusionTherefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers.
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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.