Trial reportScientific reports2016
A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.
Trial report in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- The impact of radiogenomics on breast cancer.American journal of cancer research · 2025Review
- Performance of low- and high-temporal-resolution DCE-MRI texture analysis in distinguishing breast lesions from background enhancement.American journal of translational research · 2025Article
- Review
- MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients.Journal of digital imaging · 2023Article
- Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance.Insights into imaging · 2023Article
- Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps.Biomedical engineering online · 2022Article
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review.Exploration of targeted anti-tumor therapy · 2022Review
- AI-enhanced breast imaging: Where are we and where are we heading?European journal of radiology · 2021Review
- [Multimodal, multiparametric and genetic breast imaging].Der Radiologe · 2021Review
- Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables.Journal of biophotonics · 2020Article
- Article
- Combining molecular and imaging metrics in cancer: radiogenomics.Insights into imaging · 2020Review
- [Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Article
- Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.Lung cancer (Amsterdam, Netherlands) · 2019Article
- Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer.European radiology · 2019Article
- Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.Journal of magnetic resonance imaging : JMRI · 2019Article
- Observational
- A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.British journal of cancer · 2018Article
- Diffusion-weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2- breast cancers.Journal of magnetic resonance imaging : JMRI · 2018Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast enhanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positive breast lesions with low (< 18, N = 55) and high (> 30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively characterize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.
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