ArticleYonsei medical journal2023
Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics.
Article in Yonsei medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 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
- Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Proteomic Profiling of Human Extracellular Vesicles Reveals Diagnostic Biomarkers for Colon Adenocarcinoma.Journal of extracellular vesicles · 2026Article
- Fusing data from CT deep learning, CT radiomics and peripheral blood immune profiles to diagnose lung cancer in a cohort of patients experiencing symptoms.EBioMedicine · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Organ-based tumor distribution for predicting prognosis in small-cell lung cancer using fluorodeoxyglucose positron emission tomography/computed tomography.Scientific reports · 2025Article
- Radiomics in sporadic microsatellite instable, mismatch repair deficient and Lynch syndrome-associated pancreatic ductal adenocarcinoma: a pilot study.Frontiers in oncology · 2025Article
- Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study.European radiology experimental · 2024Article
- A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.Insights into imaging · 2024Article
- A Comprehensive Understanding of Post-Translational Modification of Sox2 via Acetylation andCancers · 2024Article
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe investigated the feasibility of preoperative MATERIALS AND
methodsAltogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters.
resultsThe incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively;
conclusionOur predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.
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