ArticleFrontiers in oncology2021
Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response prediction.Abdominal radiology (New York) · 2026Review
- Interpretable intratumoral and peritumoral radiomics using SHAP for predicting microsatellite status in gastric adenocarcinoma: a dual-center study.Frontiers in oncology · 2026Article
- Radiomics-based prediction of microsatellite instability in colorectal cancer: a non-invasive approach to treatment stratification.La Radiologia medica · 2025Article
- Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.Molecular imaging and radionuclide therapy · 2025Article
- Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer.Frontiers in physiology · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Ensemble learning for predicting microsatellite instability in colorectal cancer using pretreatment colonoscopy images and clinical data.Frontiers in oncology · 2025Article
- Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.European radiology · 2023Article
- Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians.Frontiers in immunology · 2023Review
- The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.Health data science · 2023Review
- Article
- Development of the prediction model based on clinical-imaging omics: molecular typing and sentinel lymph node metastasis of breast cancer.Annals of translational medicine · 2022Article
- Multi-Omic Approaches in Colorectal Cancer beyond Genomic Data.Journal of personalized medicine · 2022Review
- Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer.Frontiers in oncology · 2022Article
- CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo establish and validate a combined radiomics model based on radiomics features and clinical characteristics, and to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients preoperatively.
methodsA total of 368 patients from four hospitals, who underwent preoperative contrast-enhanced CT examination, were included in this study. The data of 226 patients from a single hospital were used as the training dataset. The data of 142 patients from the other three hospitals were used as an independent validation dataset. The regions of interest were drawn on the portal venous phase of contrast-enhanced CT images. The filtered radiomics features and clinical characteristics were combined. A total of 15 different discrimination models were constructed based on a feature selection strategy from a pool of 3 feature selection methods and a classifier from a pool of 5 classification algorithms. The generalization capability of each model was evaluated in an external validation set. The model with high area under the curve (AUC) value from the training set and without a significant decrease in the external validation set was final selected. The Brier score (BS) was used to quantify overall performance of the selected model.
resultsThe logistic regression model using the mutual information (MI) dimensionality reduction method was final selected with an AUC value of 0.79 for the training set and 0.73 for the external validation set to predicting MSI. The BS value of the model was 0.12 in the training set and 0.19 in the validation set.
conclusionThe established combined radiomics model has the potential to predict MSI status in CRC patients preoperatively.
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