ArticleScientific reports2015
Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05466760 (Use of PET/MR Radiomics to Evaluate the Clinical Phenotypes, Response Status of Neoadjuvant Chemotherapy and Long-term Prognosis of Breast Cancer), which is not on this map. Cited by 93 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
Who cites it
93 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.International journal of molecular sciences · 2019Pooled it
- Evaluating the Performance of Mammogram-Based AI Risk Model in Predicting Subsequent Breast Cancer in Women with a Prior History of Breast Cancer.Journal of clinical medicine · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.NPJ digital medicine · 2025Review
- Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization.Medical physics · 2025Article
- A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death.NPJ breast cancer · 2025Article
- DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT.BMC cancer · 2025Article
- The impact of radiogenomics on breast cancer.American journal of cancer research · 2025Review
- Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.Scientific reports · 2024Article
- The RNA world: from experimental laboratory to "in silico" approach. Part 1: User friendly RNA expression databases portals.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2024Review
- Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.The British journal of radiology · 2023Review
- Artificial intelligence in breast imaging: Current situation and clinical challenges.Exploration (Beijing, China) · 2023Review
- Tales from the future-nuclear cardio-oncology, from prediction to diagnosis and monitoring.European heart journal. Cardiovascular Imaging · 2023Review
- HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer.Radiation oncology (London, England) · 2023Article
- Machine learning with multimodal data for COVID-19.Heliyon · 2023Review
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer.International journal of molecular sciences · 2023Review
- How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.Journal of clinical medicine · 2023Review
- Criteria for the translation of radiomics into clinically useful tests.Nature reviews. Clinical oncology · 2023Review
33 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
7 authors.
Funding
Abstract
Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas. Quantitative MRI phenotypes of tumors (such as tumor size, shape, margin, and blood flow kinetics) were associated with their corresponding molecular profiles (including DNA mutation, miRNA expression, protein expression, pathway gene expression and copy number variation). We found that transcriptional activities of various genetic pathways were positively associated with tumor size, blurred tumor margin, and irregular tumor shape and that miRNA expressions were associated with the tumor size and enhancement texture, but not with other types of radiomic phenotypes. We provide all the association findings as a resource for the research community (available at http://compgenome.org/Radiogenomics/). These findings pave potential paths for the discovery of genetic mechanisms regulating specific tumor phenotypes and for improving MRI techniques as potential non-invasive approaches to probe the cancer molecular status.
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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.