ArticleJournal of medical imaging (Bellingham, Wash.)2015
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.
Article in Journal of medical imaging (Bellingham, Wash.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 90 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.
Use of PET/MR Radiomics to Evaluate the Clinical Phenotypes, Response Status of Neoadjuvant Chemotherapy and Long-term Prognosis of Breast Cancer: a Preliminary Study
Use of Imaging Markers by Fluorocholine PET/MR to Predict Molecular Subtypes and Clinical Outcomes of Breast Cancer: A Pilot Study
Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.Frontiers in medicine · 2024Pooled it
- TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.International journal of molecular sciences · 2019Pooled it
- Deep learning for identifying radiogenomic associations in breast cancer.Computers in biology and medicine · 2019Trial
- Bridging tumor biology and radiomics through an ultra-large physics-driven vascular model.npj biomedical innovations · 2026Article
- Development and validation of an immune-related molecular subtyping model based on a four-gene prognostic biomarker signature and multi-omics integrative analysis in Luminal B breast cancer.Breast cancer research : BCR · 2026Article
- Article
- DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT.BMC cancer · 2025Article
- Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers.Scientific reports · 2024Article
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- Machine learning with multimodal data for COVID-19.Heliyon · 2023Review
- A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.Radiology. Imaging cancer · 2023Article
- Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches.Journal of personalized medicine · 2023Article
- A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.Sensors (Basel, Switzerland) · 2023Article
- Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer.Biomarker research · 2023Article
- Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.Frontiers in oncology · 2023Review
- Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.Frontiers in oncology · 2023Article
- Predicting transient ischemic attack risk in patients with mild carotid stenosis using machine learning and CT radiomics.Frontiers in neurology · 2023Article
- Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2022Article
- Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning.Journal of clinical and translational hepatology · 2022Article
30 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
12 authors.
Funding
Abstract
Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.
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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.