Background The 2019 novel coronavirus (COVID-19) presents a major threat to public health and has rapidly spread worldwide since the outbreak in Wuhan, Hubei Province, China in 2019. To date, there have been few reports of the varying degrees of illness caused by the COVID-19. Case presentation A case of 68-year-old female with COVID-19 pneumonia who had constant pain in the right upper quadrant of her abdomen during her hospitalization that was finally diagnosed as acute cholecystitis. Ultrasound-guided percutaneous transhepatic gallbladder drainage (PTGD) was performed, and the real-time fluorescence polymerase chain reaction (RT-PCR) COVID-19 nucleic acid assay of the bile was found to be negative. PTGD, antibacterial and anti-virus combined with interferon inhalation treatment were successful. Conclusion The time course of chest CT findings is typical for COVID-19 pneumonia. PTGD is useful for acute cholecystitis in COVID-19 patients. Acute cholecystitis is likely to be caused by COVID-19 .

In computed tomography, automated detection of pulmonary nodules with a broad spectrum of appearance is still a challenge, especially, in the detection of small nodules. An automated detection system usually contains two major steps: candidate detection and false positive (FP) reduction. We propose a novel strategy for fast candidate detection from volumetric chest CT scans, which can minimize false negatives (FNs) and false positives (FPs). The core of the strategy is a nodule-size-adaptive deep model that can detect nodules of various types, locations, and sizes from 3D images. After candidate detection, each result is located with a bounding cube, which can provide rough size information of the detected objects. Furthermore, we propose a simple yet effective CNNs-based classifier for FP reduction, which benefits from the candidate detection. The performance of the proposed nodule detection was evaluated on both independent and publicly available datasets. Our detection could reach high sensitivity with few FPs and it was comparable with the state-of-the-art systems and manual screenings. The study demonstrated that excellent candidate detection plays an important role in the nodule detection and can simplify the design of the FP reduction. The proposed candidate detection is an independent module, so it can be incorporated with any other FP reduction methods. Besides, it can be used as a potential solution for other similar clinical applications.INDEX TERMS Computed tomography, pulmonary nodule, object detection, deep-learning, convolutional neural networks.

Background Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images. Methods A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities. Results Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81–0.93) and 0.90 (95% CI, 0.83–0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients. Conclusion The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.

Background: Few studies have demonstrated the performance of regional strain by cardiovascular magnetic resonance (CMR) feature tracking in hypertrophic cardiomyopathy (HCM) patients, and the prognostic value of segmental strain remains unknown. This study aimed to explore the prognostic implications of strain parameters generated by CMR feature tracking analysis in HCM patients.Methods: In total, 104 clinically diagnosed HCM patients and 30 healthy volunteers were enrolled in this study, and all patients underwent a standard CMR examination. Global and regional strain was computed by short axis, 2-, 3-, and 4-chamber view cine MR imaging using specialized software. Cardiac structure, function, and myocardial strain were compared between the control group and HCM patients, and the event and event-free groups. Univariate and multivariate Cox regression analyses were performed to evaluate the correlations between clinical and CMR parameters and poor prognosis.Results: During the follow-up time, 8 patients reached the primary end points and 14 patients reached secondary end points. Regional radial strain of hypertrophic segments (RRS) and regional circumferential strain of hypertrophic segments (RCS) were worse in HCM patients with primary and secondary end points.In univariate Cox regression analysis of RRS, RCS were associated with primary and secondary end points.