ArticleJournal of cancer research and clinical oncology2020
Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning.
Article in Journal of cancer research and clinical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 6 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 6 syntheses or guidelines pooled it, 45 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
- Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled 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
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.The British journal of radiology · 2022Trial
- Identification of KRAS mutation in rectal cancer based on a 2.5D deep learning model.Frontiers in oncology · 2026Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Machine Learning-Based Pathomics Signature for Perineural Invasion in Colorectal Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Molecular Basis, Diagnostic Approaches, and Therapeutic Strategies in Colorectal Cancer-Comprehensive Review.International journal of molecular sciences · 2025Review
- Combination of Irreversible Electroporation andCancers · 2025Review
- Research progress on predicting KRAS gene mutations in colorectal cancer by combining radiomics and multimodal medical imaging.Frontiers in oncology · 2025Review
- A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography.BMC cancer · 2024Article
- The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study.Insights into imaging · 2024Article
- Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.Japanese journal of radiology · 2024Article
- Prognostic DNA mutation and mRNA expression analysis of perineural invasion in oral squamous cell carcinoma.Scientific reports · 2024Article
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
- Correlation betweenGenes · 2023Article
- CT-Based Radiomics to PredictBiomedicines · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
purposePreoperative prediction of perineural invasion (PNI) and Kirsten RAS (KRAS) mutation in colon cancer is critical for treatment planning and patient management. We developed machine learning models for diagnosis of PNI and KRAS mutation in colon cancer patients by interpreting preoperative CT.
methodsThis retrospective study included 207 patients who received surgical resection in our institution. The underlying tumor characteristics were described by analyzing CT image texture quantitatively. The key radiomics features were determined with similarity analysis followed by RELIEFF method among 306 CT imaging features. Eight kernel-based support vector machines classifiers were constructed using individual (II, III, or IV) or multi-stage (II + III + IV) patient cohorts for predicting PNI and KRAS mutation. The model performances were evaluated using accuracy, receiver operating curve, and decision curve analyses.
resultsMulti-stage classifiers obtained AUC of 0.793 and 0.862 for detecting PNI and KRAS mutation for test cohort. Moreover, individual-stage classifiers demonstrated significantly improved diagnostic performance at all stages (II
conclusionMachine learning models developed using preoperative CT data can predict PNI and KRAS mutation in colon cancer patients with satisfactory performance. Individual-stage models better-characterized the relationship between CT features and PNI or KRAS mutation than multi-stage models and demonstrated good prediction scores.
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