This is a PDF file of a peer-reviewed, preliminarily formatted and unedited paper that has been accepted for publication in Diagnostic and Interventional Radiology. Copyediting of the text and figures and proof review of the the paper will be finished before the paper is published in its final form. Please note that errors may be discovered which could affect the content of the paper during the production process. All legal disclaimers apply. u n c o r r e c t e d p r o o f ABSTRACT PURPOSE: This study aimed to develop a diagnostic model combining computed tomography (CT) images and radiomic features to differentiate indeterminate small (5-20 mm) solid pulmonary nodules (SSPNs).METHODS: This study retrospectively enrolled 413 patients with SSPNs surgically removed and histologically confirmed from 2017 to 2019. The SSPNs included solid malignant pulmonary nodules (n=210) and benign pulmonary nodules (n=203). The least absolute shrinkage and selection operator (LASSO) was used for radiomic feature selection, and random forest (RF) algorithms were used for radiomic model construction. The clinical model and nomogram were established using univariate and multivariable logistic regression analyses combined with clinical symptoms, subjective CT findings and radiomic features. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. RESULTS:The AUC for the clinical model was 0.77 in the training cohort (n=289; 95% confidence interval [CI]: 0.71-0.82; p = 0.001) and 0.75 in the validation cohort (n=124; 95% CI: 0.66-0.83; p = 0.016); the AUCs for the nomogram were 0.92 (95% CI: 0.89-0.95;p < 0.001) and 0.85 (95% CI: 0.78-0.91;p < 0.001), respectively. The Radscore, sex, pleural indentation and age were found independent predictors and were used to build the nomogram. CONCLUSIONS: The radiomic nomogram derived from clinical features, subjective CT signs and the radiomic score can potentially identify the risk of indeterminate SSPNs and aid in the patient's preoperative diagnosis.

PURPOSE The stomach is the most common site of gastrointestinal stromal tumors. In this study, clinical model, radiomics models, and nomogram were constructed to compare and assess the clinical value of each model in predicting the preoperative risk stratification of gastric stromal tumors (GSTs). METHODS In total, 180 patients with GSTs confirmed postoperatively pathologically were included. 70% of patients was randomly selected from each category as the training group (n= 126), and the remaining 30% was stratified as the testing group (n = 54). The image features and texture characteristics of each patient were analyzed, and predictive model was constructed. The image features and the rad-score of the optimal radiomics model were used to establish the nomogram. The clinical application value of these models was assessed by the receiver operating characteristic curve and decision curve analysis. The calibration of each model was evaluated by the calibration curve. RESULTS The area under the curve (AUC) value of the nomogram was 0.930 (95% confidence interval [CI]: 0.886-0.973) in the training group and 0.931 (95% CI: 0.869-0.993) in the testing group. The AUC values of the training group and the testing group calculated by the radiomics model were 0.874 (95% CI: 0.814-0.935) and 0.863 (95% CI: 0.76 5-0.960), respectively; the AUC values calculated by the clinical model were 0.871 (95% CI: 0.811-0.931) and 0.854 (95% CI: 0.76 0-0.947). CONCLUSION The proposed nomogram can accurately predict the malignant potential of GSTs and can be used as repeatable imaging markers for decision-making to predict the risk stratification of GSTs non-invasively and effectively before surgery.