ArticleEuropean radiology2022
Prediction of epithelial-to-mesenchymal transition molecular subtype using CT in gastric cancer.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 11 citations in OpenAlex.
- Dual-energy computed tomography-derived extracellular volume fraction and spectral quantitative parameters for predicting early recurrence after gastrectomy: insights from a multicenter study.Abdominal radiology (New York) · 2026Article
- Combined multidetector computed tomography and gastrointestinal endoscopy for gastric cancer screening, preoperative staging, and lymph node metastasis detection.World journal of gastrointestinal oncology · 2026Article
- Clinical calculator based on CT and clinicopathologic characteristics predicts short-term prognosis following resection of microsatellite-stabilized diffuse gastric cancer.Abdominal radiology (New York) · 2024Article
- The Catheter Method in Sclerotherapy in the Management of Ovarian Endometriomas.Cardiovascular and interventional radiology · 2024Article
- Prediction of occult peritoneal metastases or positive cytology using CT in gastric cancer.European radiology · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
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Abstract
objectivesTo develop a prediction model with computed tomography (CT) images and to build a nomogram incorporating known clinicopathologic variables for individualized estimation of epithelial-to-mesenchymal transition (EMT) subtype gastric cancer.
methodsPatients who underwent primary resection of gastric cancer (GC) and molecular subgroup analysis (n = 451) were reviewed. Multivariable analysis using a stepwise variable selection method was performed to build a predictive model for EMT subtype GC. A nomogram using the results of the multivariable analysis was constructed. An optimal cutoff value of total prognostic points of the nomogram for the prediction of EMT subtype was determined. The predictive model for the EMT subtype was internally validated by bootstrap resampling method.
resultsThere were 88 patients with EMT subtype and 363 patients with non-EMT subtype based on transcriptome analysis. The patient's age, Lauren classification, and mural stratification on CT were variables selected for the predictive model. The area under the curve (AUC) of the model was 0.865, and the validated AUC of the bootstrap sample was 0.860. The optimal cutoff value of total prognostic points for the prediction of EMT subtype was 94.622, with 90.9% sensitivity, 67.2% specificity, and 71.8% accuracy.
conclusionA predictive model using patient's age, Lauren classification, and mural stratification on CT for EMT molecular subtype GC was made. A nomogram was built which would serve as a useful screening tool for an individualized estimate of EMT subtype. KEY POINTS: • A predictive model for epithelial-to-mesenchymal transition (EMT) subtype incorporating patient's age, Lauren classification, and mural stratification on CT was built. • The predictive model had high diagnostic accuracy (area under the curve (AUC) = 0.865) and was validated (bootstrap AUC = 0.860). • Adding CT findings to clinicopathologic variables increases the accuracy of the predictive model than using only.
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