T he Bosniak classification of cystic renal masses (CRMs) has contributed substantially to the stratification of malignancy risk in the 3 decades since it was proposed (1). As a living system, refinements were made in 1993 and 2005 (version 2005) (2-4). With the current version of the Bosniak classification (version 2005), several shortcomings in both clinical practice and scientific research have been noted. A systematic review has suggested that interreader variability for the Bosniak classification is large, ranging from 6% to 75% (5), especially for Bosniak classes II, IIF, and III. This variability is partly explained by relatively subjective classification criteria. Moreover, the reported risk for malignancy of each class of CRM varies widely. For example, the likelihood for Bosniak classes II, IIF, III, and IV is, respectively, 9% (range, 5%-14%), 18% (range, 12%-26%), 51% (range, 42%-61%), and 86% (range, 81%-89%) (5). The high prevalence of a benign finding among Bosniak class III CRMs (approximately 49%) (6) is also a concern because unnecessary surgery may cause potential harm and present no clinical benefit.
Background Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). Purpose To develop and validate an MRI‐based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC. Study Type Retrospective. Population In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned. Field Strength/Sequence Pretreatment 3.0T renal MRI. Imaging sequences were fat‐suppressed T2WI, contrast‐enhanced T1WI, and diffusion weighted imaging. Assessment Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high‐grade ccRCC. Statistical Tests The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics. Results The radiomic signature showed good performance in discriminating high‐grade (grades 3 and 4) from low‐grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high‐grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05). Data Conclusion Multiparametric MRI‐based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions. Level of Evidence 3 Technical Efficacy Stage 2
Background Bladder paraganglioma (BPG) is a rare extra-adrenal pheochromocytoma with variable symptoms and easy to be misdiagnosed and mishandled. The aim of the study was to document the imaging features of BPG using computed tomography (CT) and magnetic resonance imaging (MRI). Patients and methods We retrospectively enrolled consecutive patients with pathology-proven BPG, who underwent CT or MRI examinations before surgery between October 2009 and October 2017. The clinical characteristics, CT, and MRI features of the patients were described and analysed. Results A total of 16 patients with 16 bladder tumours (median age 51 years, 9 females) were included. Among them, 13 patients underwent CT examinations and eight patients underwent MRI examinations preoperatively. Tumour diameters ranged from 1.6−5.4 cm. Most of the tumours grew into the bladder cavity (n = 11) with oval shapes (n = 10) and well-defined margins (n = 14). Intratumour cystic degeneration or necrosis (n = 2) was observed. Two lesions showed peripheral tissue invasion, suggesting malignant BPGs. All 13 lesions imaged with CT exhibited slight hypoattenuation and moderate to marked enhancement. Compared to the gluteus maximus, all lesions showed slight h yperintensity in T2-weighted images, hyperintensity on diffusion-weighted images (DWI), hypointensity on apparent diffusion coefficient maps, hyperintensity on T1-weighted images and a “fast in and slow out” enhanced pattern on contrast-enhanced MRI images. Conclusions BPGs are mostly oval-shaped, broadly-based and hypervascular bladder tumours with hypoattenuation on non-contrast CT, T2 hyperintensity, slight T1 hyperintensity compared to the muscle, marked restricted diffusion on DWI. Peripheral tissue invasion can suggest malignancy of the BPGs. All of these features contribute to preoperative decision-making.
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