ArticleOncology letters2018
Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast-enhanced and diffusion-weighted MRI.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for breast cancer detection: An update meta-analysis.Thoracic cancer · 2021Pooled it
- Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions.BMC cancer · 2019Pooled it
- MRI Features of Synchronous Masses in Known Breast Cancer Patients in Predicting Benign Versus Malignant Lesions: A Case Based Review at Tertiary Care Cancer Hospital.South Asian journal of cancer · 2023Article
- Efficient System for Delimitation of Benign and Malignant Breast Masses.Entropy (Basel, Switzerland) · 2022Article
- Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Diffusion Breast MRI: Current Standard and Emerging Techniques.Frontiers in oncology · 2022Review
- Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast.Frontiers in oncology · 2022Article
- Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future.Current oncology (Toronto, Ont.) · 2021Review
- Efficacy of high-resolution, 3-D diffusion-weighted imaging in the detection of breast cancer compared to dynamic contrast-enhanced magnetic resonance imaging.Polish journal of radiology · 2021Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Breast MRI texture analysis for prediction of BRCA-associated genetic risk.BMC medical imaging · 2020Article
- Dual-energy computed tomography for evaluation of breast cancer: value of virtual monoenergetic images reconstructed with a noise-reduced monoenergetic reconstruction algorithm.Japanese journal of radiology · 2020Article
- Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters.European radiology · 2019Article
- The Application of Radiomics in Breast MRI: A Review.Technology in cancer research & treatmentReview
- Breast Cancer Classification on Multiparametric MRI - Increased Performance of Boosting Ensemble Methods.Technology in cancer research & treatmentArticle
- Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging exhibits high sensitivity but low specificity for breast cancer. The present study aimed to investigate whether combining morphology, texture features and kinetic features with diffusion-weighted imaging using quantitative analysis improves the accuracy of discriminating malignant from benign breast masses. In total, 104 and 171 malignant lesions in 205 women were included. Additionally, 13 texture and 11 morphology features were computed from each lesion using a semi-automated segmentation method. To increase prediction accuracy, a newly designed classification model, difference-weighted local hyperplane, was used for statistical analysis of the combined effects of the features for predicting lesion type. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated. Diagnostic performances of morphology and texture features, kinetic features and ADC alone and the combination of them were evaluated using receiver operating characteristics analysis. Malignant lesions had lower mean ADCs than benign lesions. By using 10-fold cross validation scheme, combined morphological and kinetic features achieved a diagnostic average accuracy of 0.87. Adding an ADC threshold of 1.37×10
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