Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer. Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness. Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732-0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794-0.936) and 0.861 (95% CI: 0.733-0.990), respectively. Decision curve analysis confirmed its clinical usefulness. Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.

Background: Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma (KRAS)] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations. Methods:We propose a multi-channel and multi-task deep learning (MMDL) model for the simultaneous prediction of EGFR and KRAS mutation statuses based on CT images. First, we decomposed each 3D lung nodule into 9 views. Then, we used the pre-trained inception-attention-resnet model for each view to learn the features of the nodules. By combining 9 inception-attention-resnet models to predict the types of gene mutations in lung nodules, the models were adaptively weighted, and the proposed MMDL model could be trained end-to-end. The MMDL model utilized multiple channels to characterize the nodule more comprehensively and integrate patient personal information into our learning process. Results:We trained the proposed MMDL model using a dataset of 363 patients collected by our partner hospital and conducted a multi-center validation on 162 patients in The Cancer Imaging Archive (TCIA) public dataset. The accuracies for the prediction of EGFR and KRAS mutations were, respectively, 79.43% and 72.25% in the training dataset and 75.06% and 69.64% in the validation dataset. Conclusions:The experimental results demonstrated that the proposed MMDL model outperformed the latest methods in predicting EGFR and KRAS mutations in NSCLC.

With 28% contrast medium reduction and reduced radiation dose, CT angiography using spectral imaging and lower concentration contrast agent provided better image quality than conventional CTA.

Vascular calcification is the transformation of arterial wall mesenchymal cells, particularly smooth muscle cells (SMCs), into osteoblast phenotypes by various pathological factors. Additionally, vascular transformation mediates the abnormal deposition of calcium salts in the vascular wall, such as intimal and media calcification. Various pathological types have been described, such as calcification and valve calcification. The incidence of vascular calcification in patients with diabetes is much higher than that in nondiabetic patients, representing a critical cause of cardiovascular events in patients with diabetes. Because basic research on the clinical transformation of vascular calcification has yet to be conducted, this study systematically expounds on the risk factors for vascular calcification, vascular bed differences, sex differences, ethnic differences, diagnosis, severity assessments, and treatments to facilitate the identification of a new entry point for basic research and subsequent clinical transformation regarding vascular calcification and corresponding clinical evaluation strategies.

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The clinical risks and prognosis of diabetic vascular intimal calcification (VIC) and medial calcification (VMC) are different. This study aims to investigate the mechanism of VIC/VMC translocation. Anterior tibial arteries were collected from patients with diabetic foot amputation. The patients were then divided into VIC and VMC groups. There were plaques in all anterior tibial arteries, while the enrichment of galectin-3 in arterial plaques in the VIC group was significantly higher than that in the VMC group. Furthermore, a macrophage/vascular smooth muscle cell (VSMC) coculture system was constructed. VSMC-derived extracellular vesicles (EVs) was labeled with fluorescent probe. After macrophages were pretreated with recombinant galectin-3 protein, the migration of VSMC-derived EVs and VSMC-derived calcification was more pronounced. And anti-galectin-3 antibody can inhibit this process of EVs and calcification translocation. Then, lentivirus (LV)-treated bone marrow cells (BMCs) were transplanted into apolipoprotein E-deficient (ApoE−/−) mice, and a diabetic atherosclerosis mouse model was constructed. After 15 wk of high-fat diet, ApoE−/− mice transplanted with LV-shgalectin-3 BMCs exhibited medial calcification and a concentrated distribution of EVs in the media. In conclusion, upregulation of galectin-3 in macrophages promotes the migration of VSMC-derived EVs to the intima and induces diabetic vascular intimal calcification. NEW & NOTEWORTHY The clinical risk and prognosis of vascular intimal and medial calcification are different. Macrophage galectin-3 regulates the migration of vascular smooth muscle cell-derived extracellular vesicles and mediates diabetic vascular intimal/medial calcification translocation. This study may provide insights into the early intervention in diabetic vascular calcification.

Intraoperative cardiac arrest (IOCA) is a lethal complication of noncardiac surgery. According to several reports, immediate survival after IOCA is approximately 50%. In this study, a retrospective case analysis was performed to determine the incidence of IOCA, the potential causes of cardiac arrest, and the risk factors of no resuscitation in patients undergoing tumorous surgery.The medical records of surgery patients who experienced cardiac arrest during the intraoperative period between 2005 and 2014 were reviewed. The general conditions of the patients with IOCA were compared between the successfully resuscitated group and the unresuscitated group.Fifteen patients with IOCA among 142,853 patients undergoing tumorous surgery were reviewed during the study period. Immediate survival after IOCA was 60%. Hospital survival was 46.7%. The incidence of IOCA decreased during 2010 to 2014 when compared with the rate during 2005 to 2009 (P < .05). The risk factors affecting the success of resuscitation after IOCA included American Society of Anesthesiologists Physical Status (ASA PS) classification ≥ III (P < .05) and preoperative tachycardia (heart rate ≥100/min, P < .05). The methods of anesthesia had no effects on the results of resuscitation.The incidence of IOCA in patients undergoing tumorous surgery was 1.05 per 10,000 anesthesia. The overall mortality of IOCA was 0.56/10,000. The frequency of IOCA decreased within 10 years. There was no cardiac arrest primarily attributable to anesthesia over this study period. The risk factors leading to unsuccessful resuscitation after IOCA were ASA PS classification ≥ III and preoperative tachycardia.

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