With the spread of novel coronavirus (2019-nCoV) pneumonia, chest high-resolution computed tomography (HRCT) has been one of the key diagnostic tools. To achieve early and accurate diagnostics, determining the radiological characteristics of the disease is of great importance. In this small scale research we retrospectively reviewed and selected six cases confirmed with 2019-nCoV infection in West China Hospital and investigated their initial and follow-up HRCT features, along with the clinical characteristics. The 2019-nCoV pneumonia basically showed a multifocal or unifocal involvement of ground-glass opacity (GGO), sometimes with consolidation and fibrosis. No pleural effusion or lymphadenopathy was identified in our presented cases. The follow-up CT generally demonstrated mild to moderate progression of the lesion, with only one case showing remission by the reducing extent and density of the airspace opacification.

Background and PurposeAs a third-generation EGFR tyrosine kinase inhibitor (TKI), osimertinib is approved for treating advanced non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation after progression on first- or second-generation EGFR-TKIs such as gefitinib, erlotinib and afatinib. We aim at exploring the feasibility and effectiveness of using radiomic features from chest CT scan to predict the prognosis of metastatic non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation receiving second-line osimertinib therapy.MethodsContrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with EGFR-T790M mutation, respectively. Radiomic features were extracted from the volume of interest. LASSO regression was used to preliminarily evaluate the prognostic values of different radiomic features. We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. Finally, a combined clinical-radiomic model was developed and validated using the concordance index (C-index), decision-curve analysis (DCA) and calibration curve analysis.ResultsDisease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients’ prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660 vs. 0.560 vs. 0.598 vs. 0.590). Adding the SR radiomic model to the clinical model could remarkably strengthen the C-index of the latter from 0.672 to 0.755. DCA and calibration curve analyses also demonstrated good performance of the combined clinical-radiomic model.ConclusionsRadiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients’ prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients’ outcomes.

Background Hypopharyngeal squamous cell carcinoma (HSCC) is a rare type of head and neck cancer with poor prognosis. However, till now, there is still no model predicting the survival outcomes for HSCC patients. We aim to develop a novel nomogram predicting the long-term cancer-specific survival (CSS) for patients with HSCC and establish a prognostic classification system. Methods Data of 2021 eligible HSCC patients were retrieved from the Surveillance, Epidemiology and End Results database between 2010 and 2015. We randomly split the whole cases (ratio: 7:3) into the training and the validation cohort. Cox regression as well as the Least absolute shrinkage and selection operator (LASSO) COX were used to select significant predictors of CSS. Based on the beta-value of these predictors, a novel nomogram was built. The concordance index (C-index), the calibration curve and the decision curve analysis (DCA) were utilized for the model validation and evaluation using the validation cohort. Results In total, cancer-specific death occurred in 974/2021 (48.2%) patients. LASSO COX indicated that age, race, T stage, N stage, M stage, surgery, radiotherapy and chemotherapy are significant prognosticators of CSS. A prognostic model based on these factors was constructed and visually presented as nomogram. The C-index of the model was 0.764, indicating great predictive accuracy. Additionally, DCA and calibration curves also demonstrated that the nomogram had good clinical effect and satisfactory consistency between the predictive CSS and actual observation. Furthermore, we developed a prognostic classification system that divides HSCC patients into three groups with different prognosis. The median CSS for HSCC patients in the favorable, intermediate and poor prognosis group was not reached, 39.0-Mo and 10.0-Mo, respectively (p < 0.001). Conclusions In this study, we constructed the first nomogram as well as a relevant prognostic classification system that predicts CSS for HSCC patients. We believe these tools would be helpful for clinical practice in patients’ consultation and risk group stratification.

Background To investigate and compare the clinical and imaging features among family members infected with COVID-19. Methods We retrospectively collected a total of 34 COVID-19 cases (15 male, 19 female, aged 48 ± 16 years, ranging from 10 to 81 years) from 13 families from January 17, 2020 through February 15, 2020. Patients were divided into two groups: Group 1 - part of the family members (first-generation) who had exposure history and others (second-generation) infected through them, and Group 2 - patients from the same family having identical exposure history. We collected clinical symptoms, laboratory findings, and high-resolution computed tomography (HRCT) features for each patient. Comparison tests were performed between the first- and second-generation patients in Group 1. Results In total there were 21 patients in Group 1 and 20 patients in Group 2. For Group 1, first-generation patients had significantly higher white blood cell count (6.5 × 10 9 /L (interquartile range (IQR): 4.9–9.2 × 10 9 /L) vs 4.5 × 10 9 /L (IQR: 3.7–5.3 × 10 9 /L); P = 0.0265), higher neutrophil count (4.9 × 10 9 /L (IQR: 3.6–7.3 × 10 9 /L) vs 2.9 × 10 9 /L (IQR: 2.1–3.3 × 10 9 /L); P = 0.0111), and higher severity scores on HRCT (3.9 ± 2.4 vs 2.0 ± 1.3, P = 0.0362) than the second-generation patients. Associated underlying diseases (odds ratio, 8.0, 95% confidence interval: 3.4–18.7, P = 0.0013) were significantly correlated with radiologic severity scores in second-generation patients. Conclusion Analysis of the family cluster cases suggests that COVID-19 had no age or sex predominance. Secondarily infected patients in a family tended to develop milder illness, but this was not true for those with existing comorbidities.

Background The effect of comorbid hypertension and type 2 diabetes mellitus (T2DM) on coronary artery plaques examined by coronary computed tomography angiography (CCTA) is not fully understood. We aimed to comprehensively assess whether comorbid hypertension and T2DM influence coronary artery plaques using CCTA. Materials and methods A total of 1100 T2DM patients, namely, 277 normotensive [T2DM(HTN−)] and 823 hypertensive [T2DM(HTN +)] individuals, and 1048 normotensive patients without T2DM (control group) who had coronary plaques detected on CCTA were retrospectively enrolled. Plaque type, coronary stenosis, diseased vessels, the segment involvement score (SIS) and the segment stenosis score (SSS) based on CCTA data were evaluated and compared among the groups. Results Compared with patients in the control group, the patients in the T2DM(HTN−) and T2DM(HTN +) groups had more partially calcified plaques, noncalcified plaques, segments with obstructive stenosis, and diseased vessels, and a higher SIS and SSS (all P values < 0.001). Compared with the control group, T2DM(HTN +) patients had increased odds of having any calcified and any noncalcified plaque [odds ratio (OR) = 1.669 and 1.278, respectively; both P values < 0.001]; both the T2DM(HTN-) and T2DM(HTN +) groups had increased odds of having any partially calcified plaque (OR = 1.514 and 2.323; P = 0.005 and P < 0.001, respectively), obstructive coronary artery disease (CAD) (OR = 1.629 and 1.992; P = 0.001 and P < 0.001, respectively), multivessel disease (OR = 1.892 and 3.372; both P-values < 0.001), an SIS > 3 (OR = 2.233 and 3.769; both P values < 0.001) and an SSS > 5 (OR = 2.057 and 3.580; both P values < 0.001). Compared to T2DM(HTN−) patients, T2DM(HTN +) patients had an increased risk of any partially calcified plaque (OR = 1.561; P = 0.005), multivessel disease (OR = 1.867; P < 0.001), an SIS > 3 (OR = 1.647; P = 0.001) and an SSS > 5 (OR = 1.625; P = 0.001). Conclusion T2DM is related to the presence of partially calcified plaques, obstructive CAD, and more extensive coronary artery plaques. Comorbid hypertension and diabetes further increase the risk of partially calcified plaques, and more extensive coronary artery plaques.

Background Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features’ value in predicting the TMB status of BLCA patients. Methods Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features. Results The unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model. Conclusions Our work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings.

Background and purposeRadiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown.MethodsComputed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation.ResultsHierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models.ConclusionWe firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

BackgroundThe effect of smoking on coronary artery plaques examined by coronary computed tomography angiography (CCTA) in type 2 diabetes mellitus (DM) patients is not fully understood. This study explored the effect of smoking on coronary artery plaques by comparing the characteristics of plaques between diabetes patients with and without a smoking history and among those with different smoking durations.Materials and MethodsIn total, 1058 DM patients found to have coronary plaques on CCTA were categorized into the smoker (n=448) and nonsmoker groups (n=610). Smokers were stratified by smoking duration [≤20 years (n=115), 20~40 years (n=233) and &gt;40 years (n=100)]. The plaque types, luminal stenosis [obstructive (&lt;50%) or nonobstructive (≥50%) stenosis], segment involvement score (SIS), and segment stenosis score (SSS) of the CCTA data were compared among groups.ResultsCompared to nonsmokers, smokers demonstrated increased odds ratios (ORs) of any noncalcified plaques (OR=1.423; P=0.014), obstructive plaques (OR=1.884; P&lt;0.001), multivessel disease (OR=1.491; P=0.020), SIS≥4 (OR=1.662; P&lt;0.001), and SSS≥7 (OR=1.562; P=0.001). Compared to diabetes patients with a smoking duration ≤20 years, those with a smoking duration of 20~40 years and &gt;40 years had higher OR of any mixed plaques (OR=2.623 and 3.052, respectively; Ps&lt;0.001), obstructive plaques (OR=2.004 and 2.098; P=0.003 and 0.008, respectively), multivessel disease (OR=3.171 and 3.784; P&lt;0.001 and P=0.001, respectively), and SSS≥7 (OR=1.605 and 1.950; P=0.044 and 0.020, respectively). Diabetes with a smoking duration &gt;40 years had a higher OR of SIS≥4 (OR=1.916, P=0.034).ConclusionSmoking is independently associated with the presence of noncalcified, obstructive, and more extensive coronary artery plaques in diabetes patients, and a longer smoking duration is significantly associated with a higher risk of mixed, obstructive, and more extensive plaques.