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Background and AimTo develop and validate radiomic prediction models using contrast‐enhanced computed tomography (CE‐CT) to preoperatively predict Ki‐67 expression in gastrointestinal stromal tumors (GISTs). MethodA total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki‐67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. ResultsThe radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632–0.801], 0.765 (95% CI 0.683–0.847), and 0.754 (95% CI 0.666–0.842) in the prediction of high Ki‐67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726–0.876), 0.828 (95% CI 0.681–0.974), and 0.784 (95% CI 0.701–0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. ConclusionsThe radiomic signature from CE‐CT was significantly associated with Ki‐67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki‐67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.

This work seeks the development and validation of radiomics signatures from nonenhanced computed tomography (CT, NE‐RS) to preoperatively predict the malignancy degree of gastrointestinal stromal tumors (GISTs) and the comparison of these signatures with those from contrast‐enhanced CT. A dataset for 370 GIST patients was collected from four centers. This dataset was divided into cohorts for training, as well as internal and external validation. The minimum‐redundancy maximum‐relevance algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm were used to filter unstable features. (a) NE‐RS and radiomics signature from contrast‐enhanced CT (CE‐RS) were built and compared for the prediction of malignancy potential of GIST based on the area under the receiver operating characteristic curve (AUC). (b) The radiomics model was also developed with both the tumor size and NE‐RS. The AUC values were comparable between NE‐RS and CE‐RS in the training (.965 vs .936; P = .251), internal validation (.967 vs .960; P = .801), and external validation (.941 vs .899; P = .173) cohorts in diagnosis of high malignancy potential of GISTs. We next focused on the NE‐RS. With 0.185 selected as the cutoff of NE‐RS for diagnosis of the malignancy potential of GISTs, accuracy, sensitivity, and specificity for diagnosis high‐malignancy potential GIST was 90.0%, 88.2%, and 92.3%, respectively, in the training cohort. For the internal validation set, the corresponding metrics are 89.1%, 94.9%, and 80.0%, respectively. The corresponding metrics for the external cohort are 84.6%, 76.1%, and 91.0%, respectively. Compared with only NE‐RS, the radiomics model increased the sensitivity in the diagnosis of GIST with high‐malignancy potential by 5.9% ( P = .025), 2.5% ( P = .317), 10.5% ( P = .008) for the training set, internal validation set, and external validation set, respectively. The NE‐RS had comparable prediction efficiency in the diagnosis of high‐risk GISTs to CE‐RS. The NE‐RS and radiomics model both had excellent accuracy in predicting malignancy potential of GISTs.

Background: The relationship between plaque calcification and new ischemic brain lesions after carotid artery stenting (CAS) remains controversial. The purpose of this study was to determine if the circumferential degree of carotid calcification is associated with new ischemic brain lesions on diffusionweighted imaging (DWI) after CAS.Methods: A total of 96 patients with carotid stenosis of ≥50% who underwent CAS were enrolled in the study. All patients underwent preoperative carotid computed tomography (CT), and preoperative and postoperative brain MRI. The brain MRI sequences included T1WI, T2WI, T2-fluid-attenuated inversion recovery (FLAIR), and DWI. The location, circumferential degree, volume, percentage volume, maximum density, mean density, Agatston score of carotid calcification, and total plaque volume were assessed and compared between patients with and without new ischemic brain lesions after CAS. Univariate and multivariate analyses were performed to evaluate predictors of new ischemic brain lesions.Results: All of the 96 patients (67.8±6.8 years of age, 83.3% men) were included in the analysis. New ischemic brain lesions on DWI were observed in 40 patients (41.7%). Patients with new ischemic brain lesions after CAS had a larger circumferential degree of calcification than those without new ischemic brain lesions (P<0.001). There was only a possible trend toward significance for the percentage volume of calcification between the two groups with and without new brain ischemic lesions (P=0.07). No significant differences were found regarding the location (P=0.18), volume (P=0.37), maximum density (P=0.44), mean density (P=0.39), Agatston score (P=0.28), and total plaque volume (P=0.33) of carotid calcification between the DWI+ and DWI-groups. In the multivariate analysis, an increased risk of new ischemic brain lesions was observed in patients with a high score for the circumferential degree of calcification [score 3; odds ratio (OR): 10.7, P<0.001; score 4, OR: 11.7, P=0.038]. Conclusions:The circumferential degree of carotid calcification was associated with new ischemic brain lesions after CAS. CAS should be avoided if possible for carotid stenosis with large circumferential calcified plaques.

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