ArticleEuropean radiology experimental2017
Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes.
Article in European radiology experimental, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A deep learning model based on multiphase DCE-MRI for preoperative prediction of Ki-67 expression in breast cancer.Frontiers in oncology · 2026Article
- Heterogeneity Assessment of Breast Cancer Tumor Microenvironment: Multiparametric Quantitative Analysis with DCE-MRI and Discovery of Radiomics Biomarkers.Breast cancer (Dove Medical Press) · 2025Review
- Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.BMC medical imaging · 2024Article
- A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data.Quantitative imaging in medicine and surgery · 2023Article
- Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance.Insights into imaging · 2023Article
- Article
- Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Frontiers in oncology · 2022Review
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics-A Pilot Reader Study.Diagnostics (Basel, Switzerland) · 2021Article
- Machine learning analysis of TCGA cancer data.PeerJ. Computer science · 2021Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Background parenchymal enhancement on breast MRI: A comprehensive review.Journal of magnetic resonance imaging : JMRI · 2020Review
- Precision diagnostics based on machine learning-derived imaging signatures.Magnetic resonance imaging · 2019Review
- The future of radiology is now: the first 100 articles published in European Radiology Experimental.European radiology experimental · 2019Article
- Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.AJR. American journal of roentgenology · 2019Review
- Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data.Computational and mathematical methods in medicine · 2019Article
- Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.European radiology experimental · 2018Review
- Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis.Computational and mathematical methods in medicine · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundIn this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute.
methodsOur retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff's α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman's rank correlation coefficients were used to compare HEIP and CEIP.
resultsInter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with
conclusionsQuantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.
Indexed as
Identifiers
What OpenQuestion holds
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.