ArticleBMC cancer2019
Characterizing MRI features of rectal cancers with different KRAS status.
Article in BMC cancer, 2019. 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
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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
19 citing papers in PubMed, 31 citations in OpenAlex.
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- Assessment of prognostic indicators and KRAS mutations in rectal cancer using a fractional-order calculus MR diffusion model: whole tumor histogram analysis.Abdominal radiology (New York) · 2025Article
- Research progress on predicting KRAS gene mutations in colorectal cancer by combining radiomics and multimodal medical imaging.Frontiers in oncology · 2025Review
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features.Diagnostics (Basel, Switzerland) · 2023Article
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- The application of radiomics in predicting gene mutations in cancer.European radiology · 2022Review
- Role of MRI‑based radiomics in locally advanced rectal cancer (Review).Oncology reports · 2022Review
- Multi-Omic Approaches in Colorectal Cancer beyond Genomic Data.Journal of personalized medicine · 2022Review
- Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT.Frontiers in oncology · 2022Article
- Overview of serum and tissue markers in colorectal cancer: a primer for radiologists.Abdominal radiology (New York) · 2021Review
- Radiomics and machine learning applications in rectal cancer: Current update and future perspectives.World journal of gastroenterology · 2021Review
- Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma.World journal of clinical cases · 2021Article
- Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice.Diagnostics (Basel, Switzerland) · 2021Review
- Radiogenomics in Colorectal Cancer.Cancers · 2021Review
- MRI Radiomics Signature as a Potential Biomarker for PredictingFrontiers in oncology · 2021Article
- MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).La Radiologia medica · 2020Article
- Predictive Value of the Prognostic Nutritional Index in Neoadjuvant Chemoradiotherapy for Rectal Cancer.Cancer diagnosis & prognosisArticle
- Morphometric assessment of the mesorectal fat in Chinese Han population: A clinical MRI study.Science progressArticle
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTo investigate whether MRI findings, including texture analysis, can differentiate KRAS mutation status in rectal cancer.
methodsTotally, 158 patients with pathologically proved rectal cancers and preoperative pelvic MRI examinations were enrolled. Patients were stratified into two groups: KRAS wild-type group (KRAS
resultsMean values of six texture parameters (Mean, Variance, Skewness, Entropy, gray-level nonuniformity, run-length nonuniformity) were significantly higher in KRAS
conclusionThe MRI findings of rectal cancer, especially texture features, showed an encouraging value for identifying KRAS 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.