Tumors in von Hippel–Lindau Syndrome: From Head to Toe—Comprehensive State-of-the-Art Review

Von Hippel–Lindau syndrome (VHL) is an autosomal-dominant hereditary tumor disease that arises owing to germline mutations in the VHL gene, located on the short arm of chromosome 3. Patients with VHL may develop multiple benign and malignant tumors involving various organ systems, including retinal hemangioblastomas (HBs), central nervous system (CNS) HBs, endolymphatic sac tumors, pancreatic neuroendocrine tumors, pancreatic cystadenomas, pancreatic cysts, clear cell renal cell carcinomas, renal cysts, pheochromocytomas, paragangliomas, and epididymal and broad ligament cystadenomas. The VHL/hypoxia-inducible factor pathway is believed to play a key role in the pathogenesis of VHL-related tumors. The diagnosis of VHL can be made clinically when the characteristic clinical history and findings have manifested, such as the presence of two or more CNS HBs. Genetic testing for heterozygous germline VHL mutation may also be used to confirm the diagnosis of VHL. Imaging plays an important role in the diagnosis and surveillance of patients with VHL. Familiarity with the clinical and imaging manifestations of the various VHL-related tumors is important for early detection and guiding appropriate management. The purpose of this article is to discuss the molecular cytogenetics and clinical manifestations of VHL, review the characteristic multimodality imaging features of the various VHL-related tumors affecting multiple organ systems, and discuss the latest advances in management of VHL, including current recommendations for surveillance and screening

Imaging features of rare mesenychmal liver tumours: beyond haemangiomas.

Tumours arising from mesenchymal tissue components such as vascular, fibrous and adipose tissue can manifest in the liver. Although histopathology is often necessary for definitive diagnosis, many of these lesions exhibit characteristic imaging features. The radiologist plays an important role in suggesting the diagnosis, which can direct appropriate immunohistochemical staining at histology. The aim of this review is to present clinical and imaging findings of a spectrum of mesenchymal liver tumours such as haemangioma, epithelioid haemangioendothelioma, lipoma, PEComa, angiosarcoma, inflammatory myofibroblastic tumour, solitary fibrous tumour, leiomyoma, leiomyosarcoma, Kaposi sarcoma, mesenchymal hamartoma, undifferentiated embryonal sarcoma, rhabdomyosarcoma and hepatic metastases. Knowledge of the characteristic features of these tumours will aid in guiding the radiologic diagnosis and appropriate patient management

Primary Care Provider Perceptions of Colorectal Cancer Screening Barriers: Implications for Designing Quality Improvement Interventions

Aims. Colorectal cancer (CRC) screening is underutilized. Increasing CRC screening rates requires interventions targeting multiple barriers at each level of the healthcare organization (patient, provider, and system). We examined groups of primary care providers (PCPs) based on perceptions of screening barriers and the relationship to CRC screening rates to inform approaches for conducting barrier assessments prior to designing and implementing quality improvement interventions. Methods. We conducted a retrospective cohort study linking EHR and survey data. PCPs with complete survey responses for questions addressing CRC screening barriers were included (N=166 PCPs; 39,430 patients eligible for CRC screening). Cluster analysis identified groups of PCPs. Multivariate logistic regression estimated odds ratios and 95% confidence intervals for predictors of membership in one of the PCP groups. Results. We found two distinct groups: (1) PCPs identifying multiple barriers to CRC screening at patient, provider, and system levels (N=75) and (2) PCPs identifying no major barriers to screening (N=91). PCPs in the top half of CRC screening performance were more likely to identify multiple barriers than the bottom performers (OR, 4.14; 95% CI, 2.43–7.08). Conclusions. High-performing PCPs can more effectively identify CRC screening barriers. Targeting high-performers when conducting a barrier assessment is a novel approach to assist in designing quality improvement interventions for CRC screening

Opportunistic Detection of Type 2 Diabetes Using Deep Learning From Frontal Chest Radiographs

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs\u27 potential for enhanced T2D screening