ArticleNPJ breast cancer2017
Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI.
Article in NPJ breast cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
87 citing papers in PubMed, 2 syntheses or guidelines pooled it, 154 citations in OpenAlex.
- Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis.Acta neurochirurgica · 2026Pooled it
- Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled 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
- Radiomics and Back Pain.Global spine journal · 2026Review
- MRI-based texture analysis for breast cancer subtype classification in a multi-ethnic population.Magma (New York, N.Y.) · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Multiparametric MRI-based radiomics for differentiating HER2-negative, HER2-low, and HER2-positive breast cancer.Frontiers in medicine · 2026Article
- Nomogram for reducing unnecessary biopsies of breast lesions based on MRI and clinical features: a multi-center retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Preoperative Breast MRI and Histopathology in Breast Cancer: Concordance, Challenges and Emerging Role of CEM and mpMRI.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Diagnostic accuracy of a machine learning model using radiomics features from breast synthetic MRI.BMC medical imaging · 2025Article
- Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter study.EClinicalMedicine · 2025Article
- Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.Scientific reports · 2025Article
- Generative Adversarial Networks With Radiomics Supervision for Lung Lesion Generation.IEEE transactions on bio-medical engineering · 2025Article
- Prediction of HER2 expression in breast cancer patients based on multi-parametric MRI intratumoral and peritumoral radiomics features combined with clinical and imaging indicators.Frontiers in oncology · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Radiomics for Predicting Prognostic Factors in Breast Cancer: Insights from Contrast-Enhanced Mammography (CEM).Journal of clinical medicine · 2024Article
- Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review.Cureus · 2024Review
- Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.The British journal of radiology · 2024Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors. We tested our radiomic feature mapping framework on a retrospective cohort of 124 patients (26 benign and 98 malignant) who underwent multiparametric breast MR imaging at 3 T. The MRI parameters used were T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI). The RFMs were computed by convolving MRI images with statistical filters based on first order statistics and gray level co-occurrence matrix features. Malignant lesions demonstrated significantly higher entropy on both post contrast DCE-MRI (Benign-DCE entropy: 5.72 ± 0.12, Malignant-DCE entropy: 6.29 ± 0.06,
Identifiers
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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.