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OBJECTIVE: This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). METHODS: 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. CONCLUSION: The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
RESUMO
PURPOSE: To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). METHODS: This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. RESULTS: SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798-0.952), 0.859 (95% CI 0.748-0.935), and 0.828 (95% CI 0.731-0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. CONCLUSION: A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.
RESUMO
OBJECTIVE: Spontaneous cervicocerebral artery dissection (sCCD) is an important cause of ischaemic stroke that often occurs in young and middle aged patients. The purpose of this study was to investigate the correlation between tortuosity of the carotid artery and sCCD. METHODS: Patients with confirmed sCCD who underwent computed tomography angiography (CTA) were reviewed retrospectively. Age and sex matched patients having CTA were used as controls. The tortuosity indices of the cervical arteries were measured from the CTA images. The carotid siphon and the extracranial internal carotid artery (ICA) were evaluated according to morphological classification. The carotid siphons were classified into five types. The extracranial ICA was categorised as simple tortuosity, coiling or kinking. Independent risk factors for sCCD were investigated using multivariable analysis. RESULTS: The study included sixty-six patients with sCCD and 66 controls. There were no differences in vascular risk factors between the two groups. The internal carotid tortuosity index (ICTI) (25.24 ± 12.37 vs. 15.90 ± 8.55, respectively; p < .001) and vertebral tortuosity index (VTI) (median 11.28; interquartile range [IQR] 6.88, 18.80 vs. median 8.38; IQR 6.02, 12.20, respectively; p = .008) were higher in the patients with sCCD than in the controls. Type III and Type IV carotid siphons were more common in the patients with sCCD (p = .001 and p < .001, respectively). The prevalence of any vessel tortuosity, coiling and kinking of the extracranial ICA was higher in the patients with sCCD (p < .001, p = .018 and p = .006, respectively). ICTI (odds ratio [OR] 2.964; p = .026), VTI (OR 5.141; p = .009), and Type III carotid siphons (OR 4.654; p = .003) were independently associated with the risk of sCCD. CONCLUSION: Arterial tortuosity is associated with sCCD, and greater tortuosity of the cervical artery may indicate an increased risk of arterial dissection.