B one tumors include benign, intermediate, and malignant lesions, according to the classification system of the World Health Organization (1). Malignant neoplasms can be further divided into primary and secondary bone tumors or metastases (2,3). Radiography is the suggested primary imaging modality for the diagnosis of bone tumors because it can enable visualization of the location, destruction pattern, and periosteal reaction pattern of bone lesions (4,5). These destruction patterns reflect the biologic activity of bone lesions, through which they can be categorized as aggressive or nonaggressive (6). As demonstrated by Lodwick's well-established grading system, the destruction patterns of bone tumors observed on radiographs allow for evaluation of biologic activity and subsequently allow for risk assessment of malignancy (7,8). Primary bone tumors are uncommon. Thus, many radiologists may not be able to develop sufficient expertise to reliably identify and assess these lesions on radiographs (9). However, early detection and correct diagnosis are crucial for adequate and successful treatment (10). To improve the rates of early detection and correct assessment, an artificial intelligence model that could detect and accurately categorize bone lesions into malignant or benign bone lesions on radiographs may be beneficial. Recently, studies have shown that deep learning (DL) models reliably assess and detect a variety of diseases based on medical imaging data (11,12). Clinical implementation of these models may improve the reliability and accuracy of radiologic assessment, thus potentially leading to improved diagnostics and better patient outcomes (13). Recently, a preliminary study used DL to classify primary bone tumors on radiographs ( 14), but Background: An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow.Purpose: To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods:This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results:Radiographs from 934 patients (mean age, 33 years 6 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred f...

Background Since the outbreak of the COVID-19 pandemic, a number of risk factors for a poor outcome have been identified. Thereby, cardiovascular comorbidity has a major impact on mortality. We investigated whether coronary calcification as a marker for coronary artery disease (CAD) is appropriate for risk prediction in COVID-19. Methods Hospitalized patients with COVID-19 (n = 109) were analyzed regarding clinical outcome after native computed tomography (CT) imaging for COVID-19 screening. CAC (coronary calcium score) and clinical outcome (need for intensive care treatment or death) data were calculated following a standardized protocol. We defined three endpoints: critical COVID-19 and transfer to ICU, fatal COVID-19 and death, composite endpoint critical and fatal COVID-19, a composite of ICU treatment and death. We evaluated the association of clinical outcome with the CAC. Patients were dichotomized by the median of CAC. Hazard ratios and odds ratios were calculated for the events death or ICU or a composite of death and ICU. Results We observed significantly more events for patients with CAC above the group’s median of 31 for critical outcome (HR: 1.97[1.09,3.57], p = 0.026), for fatal outcome (HR: 4.95[1.07,22.9], p = 0.041) and the composite endpoint (HR: 2.31[1.28,4.17], p = 0.0056. Also, odds ratio was significantly increased for critical outcome (OR: 3.01 [1.37, 6.61], p = 0.01) and for fatal outcome (OR: 5.3 [1.09, 25.8], p = 0.02). Conclusion The results indicate a significant association between CAC and clinical outcome in COVID-19. Our data therefore suggest that CAC might be useful in risk prediction in patients with COVID-19.

Purpose Correct differentiation between malignant and benign incidentally found cystic renal lesions has critical implications for patient management. In several studies contrast-enhanced ultrasound (CEUS) showed higher sensitivity with respect to the accurate characterization of these lesions compared to MRI, but the cost-effectiveness of CEUS has yet to be investigated. The aim of this study was to analyze the cost-effectiveness of CEUS as an alternative imaging method to MRI for the characterization of incidentally found cystic renal lesions. Materials and Methods A decision model including the diagnostic modalities MRI and CEUS was created based on Markov simulations estimating lifetime costs and quality-adjusted life years (QALYs). The recent literature was reviewed to obtain model input parameters. The deterministic sensitivity of diagnostic parameters and costs was determined and probabilistic sensitivity analysis using Monte-Carlo Modelling was applied. Willingness-to-pay (WTP) was assumed to be $ 100 000/QALY. Results In the base-case scenario, the total costs for CEUS were $9654.43, whereas the total costs for MRI were $9675.03. CEUS resulted in an expected effectiveness of 8.06 QALYs versus 8.06 QALYs for MRI. Therefore, from an economic point of view, CEUS was identified as an adequate diagnostic alternative to MRI. Sensitivity analysis showed that results may vary if CEUS costs increase or those of MRI decrease. Conclusion Based on the results of the analysis, the use of CEUS was identified as a cost-effective diagnostic strategy for the characterization of incidentally found cystic renal lesions.