ArticleBreast cancer research and treatment2020
Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging.
Article in Breast cancer research and treatment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features.La Radiologia medica · 2023Trial
- Article
- The role of magnetic resonance imaging (MRI) in breast cancer molecular subtypes.Polish journal of radiology · 2026Review
- Prediction of immunotherapy response in nasopharyngeal carcinoma: a comparative study using MRI-based radiomics signature and programmed cell death ligand 1 expression score.European radiology · 2025Article
- Radiomics-based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Physiologically Decomposed Diffusion-weighted MRI.Radiology. Imaging cancer · 2025Article
- Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification.Frontiers in artificial intelligence · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.European radiology · 2024Article
- Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.Journal of imaging informatics in medicine · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Review
- Data Partitioning and Statistical Considerations for Association of Radiomic Features to Biological Underpinnings: What Is Needed.Radiology · 2023Article
- A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based onJapanese journal of radiology · 2023Article
- Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.Academic radiology · 2023Review
- Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy.Medeniyet medical journal · 2022Article
- Radiomics in medical imaging: pitfalls and challenges in clinical management.Japanese journal of radiology · 2022Review
- Review
- PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma.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
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2 authors.
Funding
Abstract
background and purposeMultiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets.
methodsWe developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p < 0.05.
resultsThe mpRad features successfully classified malignant from benign breast lesions with excellent sensitivity and specificity of 82.5% and 80.5%, respectively, with Area Under the receiver operating characteristic Curve (AUC) of 0.87 (0.81-0.93). mpRad provided a 9-28% increase in AUC metrics over single radiomic parameters.
conclusionsWe have introduced the mpRad framework that extends radiomic analysis from single images to multiparametric datasets for better characterization of the underlying tissue biology.
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