ArticleTomography (Ann Arbor, Mich.)2019
Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps.
Article in Tomography (Ann Arbor, Mich.), 2019. 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.
- The biomechanical signature of tumor invasion.Genes & diseases · 2026Review
- K-means clustering-based analysis of quantitative ultrafast DCE-MRI for predicting breast cancer response to neoadjuvant chemotherapy.Journal of applied clinical medical physics · 2026Article
- Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study.Breast cancer research : BCR · 2025Article
- New progress in imaging diagnosis and immunotherapy of breast cancer.Frontiers in immunology · 2025Review
- Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer.Journal of imaging informatics in medicine · 2024Article
- Review
- Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis.ACS omega · 2023Review
- Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study.BMC medical imaging · 2022Article
- Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients.World journal of clinical cases · 2022Article
- Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.La Radiologia medica · 2022Review
- Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma.Frontiers in oncology · 2022Article
- Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.BMC bioinformatics · 2021Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint.Tomography (Ann Arbor, Mich.) · 2020Article
- Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer.Frontiers in medicine · 2020Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (K
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