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Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules. A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve. The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843–0.940) and 0.911 (95% CI: 0.867–0.955), respectively, in the primary group and 0.826 (95% CI: 0.727–0.926) and 0.843 (95% CI: 0.749–0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis. A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules. • A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

To evaluate the performance of low b-value diffusion-weighted imaging (DWI) for detection of inflamed vessels in active Takayasu arteritis (TA).Forty patients with active TA involving the thoracic aorta and its super-aortic branches underwent low b-value (50 s/mmThe overall image quality of DEI, DWI, and T2WI was scored 7.97 ± 1.15, 7.32 ± 1.73, and 6.51 ± 1.69 respectively. The score of DEI and DWI was higher than that of T2WI (p0.001). The quality of blood suppression was rated higher in DWI than T2WI and DEI (p0.001). Both the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the diseased vessel walls measured on DEI and DWI were significantly higher than those on T2WI (p0.001). However, there was no significant difference in SNR and CNR between DEI and DWI (p = 0.283 and 0.063). In detection of mural inflammation, significant advantage was observed when comparing the findings from DEI/DWI to those from T2WI (p0.001). But no significant difference was found between the findings of DWI and DEI (p0.99).Low b-value DWI may be used as a promising alternative to DEI for detecting inflamed vessels in active TA.• Currently, the most widely used imaging modality in detection of mural inflammation is contrast-enhanced MRI. • Low b-value DWI is shown comparable to contrast-enhanced MRI and superior to T2WI in identifying mural inflammation in patients with active Takayasu arteritis. • Low b-value DWI is a fast and unenhanced MRI technique which may potentially replace contrast-enhanced MRI in identifying disease activity of Takayasu arteritis.

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 diffusion-weighted 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

We sought to build a high-risk plaque MRI-based model (HRPMM) using radiomics features and machine learning for differentiating symptomatic from asymptomatic carotid plaques. One hundred sixty-two patients with carotid stenosis were randomly divided into training and test cohorts. Multi-contrast MRI including time of flight (TOF), T1- and T2-weighted imaging, and contrast-enhanced imaging was done. Radiological characteristics of the carotid plaques were recorded and calculated to build a traditional model. After extracting the radiomics features on these images, we constructed HRPMM with least absolute shrinkage and selection operator algorithm in the training cohort and evaluated its performance in the test cohort. A combined model was also built using both the traditional and radiomics features. The performance of all the models in the identification of high-risk carotid plaque was compared. Intraplaque hemorrhage and lipid-rich necrotic core were independently associated with clinical symptoms and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.825 versus 0.804 in the training and test cohorts. The HRPMM and the combined model achieved an AUC of 0.988 versus 0.984 and of 0.989 versus 0.986 respectively in the two cohorts. Both the radiomics model and combined model outperformed the traditional model, whereas the combined model showed no significant difference with the HRPMM. Our MRI-based radiomics model can accurately distinguish symptomatic from asymptomatic carotid plaques. It is superior to the traditional model in the identification of high-risk plaques. • Carotid plaque multi-contrast MRI stores other valuable information to be further exploited by radiomics analysis. • Radiomics analysis can accurately distinguish symptomatic from asymptomatic carotid plaques. • The radiomics model is superior to the traditional model in the identification of high-risk plaques.

Background and Aim To develop and validate radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). Method A 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. Results The 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. Conclusions The 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.