ArticleInsights into imaging2024
Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Combination of radiomic and clinical characteristics to predict mortality in patients with colorectal perforation.International journal of colorectal disease · 2025Article
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5 authors.
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Abstract
objectivesRadiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival.
methodsFor radiomics texture selection, 3D Slicer 5.0.3 software and the least absolute shrinkage and selection operator (LASSO) algorithm were used. Subsequently, the radiomics model, computed tomography (CT) image, and clinical risk model were compared. The performance of the three models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration plots, and clinical impact curves (CICs).
resultsFor the LNM prediction model, 224 patients with LNM information were used to construct a model that was applied to predict LNM. According to the CT data and clinical characteristics, we constructed a radiomics model, CT imaging model and clinical model. The radiomics model for evaluating LNM status showed excellent calibration and discrimination in the training cohort (AUC = 0.926, 95% CI = 0.869-0.982) and the validation cohort (AUC = 0.872, 95% CI = 0.802-0.941). DeLong's test demonstrated that the difference among the three models was significant. Similarly, DCA and CIC showed that the radiomics model has better clinical utility than the CT imaging model and clinical model. Our model also exhibited good performance in predicting survival-in line with the findings of the model built with clinical risk factors.
conclusionsCT radiomics models exhibited better predictive performance for LNM than models built based on clinical risk characteristics and CT imaging and had comparative clinical utility for predicting patient prognosis. CRITICAL RELEVANCE STATEMENT: The radiomics model showed excellent performance and discrimination for predicting LNM and survival of duodenal papillary carcinoma (DPC). KEY POINTS: LNM status determines the most appropriate treatment for DPC. Our radiomics model for evaluating the LNM status of DPC performed excellently. The radiomics model had high sensitivity and specificity for predicting survival, exhibiting great clinical value.
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