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Background: Previous studies have reported that the tumor heterogeneity and immune molecular mechanisms of proximal and distal colorectal cancer (CRC) are divergent. Therefore, our study aims to analyze the difference between left-sided CRC (LCC) and right-sided CRC (RCC), and respectively develop the nomograms based on prognostic immune-related genes for LCC and RCC. Methods: We enrolled 443 colon cancer patients (220 LCC patients and 223 patients) from The Cancer Genome Atlas (TCGA) datasets. Firstly, the differential expressed immune-related genes (DE-IRGs), overall survival (OS), and biological functions between LCC and RCC groups were identified. Then, we analyzed the differences between the two groups in the immune microenvironment, immune checkpoint, and tumor mutation burden (TMB). Next, the LCC and RCC data from TCGA dataset are randomly divided into training and internal validation sets at a 7:3 ratio respectively. Additionally, 566 colon cancer patients (342 LCC patients and 224 RCC patients) in the GSE39582 dataset were downloaded from the Gene Expression Omnibus (GEO) database as the external validation set. Then, survival and Lasso Cox regression analyses were applied to identify hub immune-related genes and respectively establish two prognostic gene signatures of LCC and RCC groups. The prognostic signatures were validated by the 10-fold cross-validation, internal validation set, and external validation set. Further, combined with clinical features, we constructed two clinical predictive nomograms and validated them. Results: RCC patients have lower survival than LCC. RCC patients have higher proportions of T cells CD8, T cells follicular helper, and lower macrophages M0, T cells CD4 naive. RCC patients have higher ESTIMATE and immune scores and lower tumor purity. The immune checkpoint expression levels and TMB values are higher in RCC patients than in LCC. We respectively selected 10 immune-related genes for LCC and 7 genes for RCC groups to develop and validate the prognostic model and calculate a risk score for each patient. The AUC values of the risk score for OS in LCC were 0.735 in the training set, 0.711 in the internal validation set, and 0.744 in the external validation set, and in RCC were 0.704 in the training set, 0.738 in the internal validation set, and 0.705 in the external validation set. The AUC values of the 10-fold cross-validation range from 0.564 to 0.808 in LCC and from 0.589 to 0.792 in RCC. The nomogram of LCC of RCC includes risk based on prognostic genes, age, pathological T, N, M, stage, and gender. the AUC values of the LCC nomogram were 0.722 in the training set, 0.696 in the internal validation set, and 0.739 in the external validation set, and of the RCC nomogram were 0.774 in the training set, 0.744 in the internal validation set, and 0.737 in the external validation set. We also found that were significantly different between high- and low-risk patients in the immune score, ESTIMATE score, tumor purity, immune checkpoint expression levels, and TMB values. Conclusions: We found significant differences in the multidimensional insight between LCC and RCC patients in clinical features, DE-IRGs, TMB, immune checkpoint expression levels, and immune microenvironment landscape. Our study respectively established two prognostic nomograms based on DE-IRGs in combination with clinical features to provide a basis for personalized and precise treatment of LCC and RCC patients.

Background Coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have explored the role of chest computed tomography (CT) features and severity scores for prognostic prediction. In this study, we aimed to investigate the role of chest CT severity score and imaging features in the prediction of the prognosis of COVID-19 patients. Methods A total of 134 patients (62 recovered and 72 deceased patients) with confirmed COVID-19 were enrolled. The clinical, laboratory, and chest CT (316 scans) data were retrospectively reviewed. Demographics, symptoms, comorbidities, and temporal changes of laboratory results, CT features, and severity scores were compared between recovered and deceased groups using the Mann-Whitney U test and logistic regression to identify the risk factors for poor prognosis. Results Median age was 48 and 58 years for recovered and deceased patients, respectively. More patients had at least one comorbidity in the deceased group than the recovered group (60% vs. 29%). Leukocytes, neutrophil, high-sensitivity C-reactive protein (hsCRP), prothrombin, D-dimer, serum ferritin, interleukin (IL)-2, and IL-6 were significantly elevated in the deceased group than the recovered group at different stages. The total CT score at the peak stage was significantly greater in the deceased group than the recovered group (20 vs. 11 points). The optimal cutoff value of the total CT scores was 16.5 points, achieving 69.4% sensitivity and 82.2% specificity for the prognostic prediction. The crazy-paving pattern and consolidation were more common in the deceased patients than those in the recovered patients. Linear opacities significantly increased with the disease course in the recovered patients. Sex, age, neutrophil, IL-2, IL-6, and total CT scores were independent risk factors for the prognosis with odds ratios of 3.8 to 8.7. Conclusions Sex (male), older age (>60 years), elevated neutrophil, IL-2, IL-6 level, and total CT scores (≥16) were independent risk factors for poor prognosis in patients with COVID-19. Temporal changes of chest CT features and severity scores could be valuable for early identification of severe cases and eventually reducing the mortality rate of COVID-19.

Objective To evaluate temporal lung changes in coronavirus disease 2019 (COVID-19) in high-resolution computed tomography (HRCT) and to determine the appropriate computed tomographic (CT) follow-up time. Methods Eighty-six patients with two or more HRCT scans who were diagnosed with COVID-19 were included. The CT score and major CT findings were evaluated. Results Eighty-two (95.3%) patients had lesions on the initial HRCT scans. Most scans showed bilateral, multifocal lung lesions, with multiple lobes involved and diffuse distribution. For fifty-seven patients with type I (progress compared with the initial CT score), the CT score reached a peak at 12 days and the nadir at 36 days. For twenty-nine patients with type II (no progress compared with the initial CT score), the lowest CT score was reached at 23 days. On the final HRCT scans (>21 days), patients with a reticular pattern were older than those without a reticular pattern. Conclusion The appropriate follow-up time of CT scans is during the second week (approximately 12 days) and the fourth to fifth weeks (approximately 23–36 days) from the onset of illness. These times could help reduce the CT radiation dose and show timely changes in the course of the disease by CT.

Background: The coronavirus disease 2019 (COVID-19) has become a worldwide pandemic since mid-December 2019, which greatly challenge public medical systems. With limited medical resources, it is a natural strategy, while adopted, to access the severity of patients then determine the treatment priority. However, our work observes the fact that the condition of many mild outpatients quickly worsens in a short time, i.e. deteriorate into severe/critical cases. Hence, it has been crucial to early identify those cases and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression. Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients at admission were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at severe/critical stage of the two groups were compared with Chi-square test, Fisher’s exact test, and t test. Both traditional logistic regression and deep learning-based methods were used to build the prediction models. The area under ROC curve (AUC) was used to evaluate the models. Findings: The deep learning-based method significantly outperformed logistic regression (AUC 0·954 vs. 0·893). The deep learning-based method achieved a prediction AUC of 0·938 by combining the clinical data and the CT data, significantly outperforming its counterpart trained with clinical data only by 0.141. By further considering the temporal information of the CT sequence, our model achieved the best AUC of 0·954. Interpretation: The proposed model can be effectively used for finding out the mild patients who are easy to deteriorate into severe/critical cases so that such patients get timely treatments while alleviating the limitations of medical resources. Funding Statement:This work was supported by National Key R&D Program of China (No. 2018YFB1004600), HUST COVID-19 Rapid Response Call (No. 2020kfyXGYJ093, No. 2020kfyXGYJ094), National Key R&D Program of China (No.2017YFC1309100), National Science Fund for Distinguished Young Scholars (No.81925023), National Natural Science Foundation of China (No.61703049, No. 81771912, No. 81401390) Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The authors stated this was an IRB-approved study.