Trial reportAbdominal radiology (New York)2019
Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation.
Trial report in Abdominal radiology (New York), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.La Radiologia medica · 2023Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study.BioMed research international · 2022Trial
- Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.Cancer medicine · 2021Trial
- Comprehensive machine learning analysis of a radiomics-based model for predicting microsatellite instability in right Colon Cancer.Frontiers in oncology · 2026Article
- Prediction of Microsatellite Instability in Colorectal Cancer Using Two Internally Validated Radiomic Models.Tomography (Ann Arbor, Mich.) · 2025Article
- Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model.Frontiers in medicine · 2025Article
- Radiomics in sporadic microsatellite instable, mismatch repair deficient and Lynch syndrome-associated pancreatic ductal adenocarcinoma: a pilot study.Frontiers in oncology · 2025Article
- Machine learning-based radiomics approach assessing preoperative non-contrast CT for microsatellite instability prediction in colon cancer.Frontiers in physiology · 2025Article
- A two-stage deep learning prediction system for colon cancer microsatellite instability status using CT images.Frontiers in oncology · 2025Article
- Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study.European radiology experimental · 2024Article
- Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer.Abdominal radiology (New York) · 2024Article
- Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.BMC medical imaging · 2024Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis.BMC gastroenterology · 2023Article
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics.Yonsei medical journal · 2023Article
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.Biology · 2023Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
purposeTo predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis.
methodsThis retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II-III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) "combined" clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV.
resultsOf the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%).
conclusionsPreoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.
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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.