ArticleAmerican journal of cancer research2020
Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features.
Article in American journal of cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Prognostic and predictive value of radiomics-based imaging features in patients with colorectal liver metastasis receiving radioembolisation in first-line setting.European journal of radiology open · 2026Article
- Clinical and prognostic implications of RAS mutations in metastatic colorectal cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Multi-sequence MRI deep learning and habitat radiomics for predicting mismatch repair status and prognosis in colorectal liver metastasis: a multicenter study.La Radiologia medica · 2026Article
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Integrated radiomics and machine learning approach for ras mutation status prediction in colorectal liver metastases.La Radiologia medica · 2026Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Prediction of Microsatellite Instability in Colorectal Cancer Using Two Internally Validated Radiomic Models.Tomography (Ann Arbor, Mich.) · 2025Article
- Radiomics and radiogenomics in ovarian cancer: a review with a focus on ultrasound applications.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Therapeutic Potentials of MiRNA for Colorectal Cancer Liver Metastasis Treatment: A Narrative Review.Iranian journal of medical sciences · 2025Review
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- Article
- Research progress on predicting KRAS gene mutations in colorectal cancer by combining radiomics and multimodal medical imaging.Frontiers in oncology · 2025Review
- MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.BMC medical imaging · 2024Article
- Review
- Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.La Radiologia medica · 2024Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a critical need for development of improved methods capable of accurately predicting the RAS (KRAS and NRAS) and BRAF gene mutation status in patients with advanced colorectal cancer (CRC). The purpose of this study was to investigate whether radiomics and/or semantic features could improve the detection accuracy of RAS/BRAF gene mutation status in patients with colorectal liver metastasis (CRLM). In this retrospective study, 159 patients who had been diagnosed with CRLM in two hospitals were enrolled. All patients received lung and abdominal contrast-enhanced CT (CECT) scans prior to radiation therapy and chemotherapy. Semantic features were independently assessed by two radiologists. Radiomics features were extracted from the portal venous phase (PVP) of the CT scan for each patient. Seven machine learning algorithms were used to establish three scores based on the semantic, radiomics and the combination of both features. Two semantic and 851 radiomics features were used to predict the mutation status of RAS and BRAF using an artificial neural network method (ANN). This approach performed best out of the seven tested algorithms. We constructed three scores which were based on radiomics, semantic features and the combined scores. The combined score could distinguish between wild-type and mutant patients with an AUC of 0.95 in the primary cohort and 0.79 in the validation cohort. This study proved that the application of radiomics together with semantic features can improve non-invasive assessment of the gene mutation status of RAS (KRAS and NRAS) and BRAF in CRLM.
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33415015PMC7783758W3119127838What OpenQuestion holds
Registered trials
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