Background: The body composition of patients with rectal cancer potentially affects postoperative outcomes. This study explored the correlations between skeletal muscle and adipose tissue quantified by computed tomography (CT) with postoperative complications and long‐term prognosis in patients with rectal cancer after surgical resection.Methods: This retrospective cohort study included patients with rectal cancer who underwent surgical resection at the Wuhan Union Hospital between 2014 and 2018. CT images within 3 months prior to the surgery were used to quantify the indices of skeletal muscle and adipose tissue at the levels of the third lumbar vertebra (L3) and umbilicus. Optimal cut‐off values for each index were defined separately for males and females. Associations between body composition and postoperative complications, overall survival (OS), and disease‐free survival (DFS) were evaluated using logistic and Cox proportional hazards models.Results: We included 415 patients (240 males and 175 females; mean age: 57.8 ± 10.5 years). At the L3 level, a high skeletal muscle density (SMD; hazard ratio [HR]: 0.357, 95% confidence interval [CI]: 0.191–0.665, P = 0.001; HR: 0.571, 95% CI: 0.329–0.993, P = 0.047) and a high skeletal muscle index (SMI; HR: 0.435, 95% CI 0.254–0.747, P = 0.003; HR: 0.568, 95% CI: 0.359–0.897, P = 0.015) were independent prognostic factors for better OS and DFS. At the umbilical level, a large intermuscular fat area (IMFA; HR: 1.904, 95% CI: 1.068–3.395, P = 0.029; HR: 2.064, 95% CI: 1.299–3.280, P = 0.002) was an independent predictive factor for worse OS and DFS, and a high SMI (HR: 0.261, 95% CI: 0.132–0.517, P < 0.001; HR: 0.595, 95% CI: 0.387–0.913, P = 0.018) was an independent prognostic factor for better OS and DFS. The models combining body composition and clinical indicators had good predictive abilities for OS. The receiver operating characteristic areas under the curve were 0.848 and 0.860 at the L3 and umbilical levels, respectively (both P < 0.05).Conclusions: No correlations existed between CT‐quantified body composition parameters and postoperative complications. However, a high SMD and high SMI were significantly associated with longer OS and DFS at the L3 level, whereas a large IMFA and low SMI were associated with worse OS and DFS at the umbilical level. Combining CT‐quantified body composition and clinical indicators could help physicians predict the prognosis of patients with rectal cancer after surgery.

Chest computed tomography (CT) has been widely used to assess pulmonary involvement in COVID-19. We aimed to investigate the correlation between chest CT and clinical features in COVID-19 suspected patients with or without fever. We retrospectively enrolled 211 COVID-19 suspected patients who underwent both chest CT and reverse transcription polymerase chain reaction in Wuhan, China. The performance of CT in patients with relevant onset of symptoms, with fever (n = 141) and without fever (n = 70), was assessed respectively. The sensitivity of CT for COVID-19 was 97.3%, with area under the curve (AUC) of 0.71 (95% confidence interval [CI], 0.66–0.76). There were 141 suspected patients with fever and 70 without fever. In the fever group, 4 variables were screened to establish the basic model: age, monocyte, red blood cell, and hypertension. The AUC of the basic model was 0.72 (95% CI, 0.63–0.81), while the AUC of the CT-aided model was 0.77 (95% CI, 0.68–0.85), a significant difference (P < .05). In the nonfever group, only dry cough was screened out to establish the basic model. The AUC was 0.76 (95% CI, 0.64–0.88), which was not significantly different than the CT-aided model (P = .08). Chest CT has a high sensitivity in patients with COVID-19, and it can improve diagnostic accuracy for COVID-19 suspected patients with fever during the initial screen, whereas its value for nonfever patients remains questionable.

Objective: To provide the quantitative volumetric data of the total lung and lobes in inspiration and expiration from healthy adults, and to explore the value of paired inspiratory–expiratory chest CT scan in pulmonary ventilatory function and further explore the influence of each lobe on ventilation. Methods: A total of 65 adults (29 males and 36 females) with normal clinical pulmonary function test (PFT) and paired inspiratory–expiratory chest CT scan were retrospectively enrolled. The inspiratory and expiratory volumetric indexes of the total lung (TL) and 5 lobes (left upper lobe [LUL], left lower lobe [LLL], right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]) were obtained by Philips IntelliSpace Portal image postprocessing workstation, including inspiratory lung volume (LV_in), expiratory lung volume (LV_ex), volume change (∆LV), and well-aerated lung volume (WAL, lung tissue with CT threshold between −950 and −750 HU in inspiratory scan). Spearman correlation analysis was used to explore the correlation between CT quantitative indexes of the total lung and ventilatory function indexes (including total lung capacity [TLC], residual volume [RV], and force vital capacity [FVC]). Multiple stepwise regression analysis was used to explore the influence of each lobe on ventilation. Results: At end-inspiratory phase, the LV_in-TL was 4664.6 (4282.7, 5916.2) mL, the WAL_TL was 4173 (3639.6, 5250.9) mL; both showed excellent correlation with TLC (LV_in-TL: r = 0.890, p < 0.001; WAL_TL: r = 0.879, p < 0.001). From multiple linear regression analysis with lobar CT indexes as variables, the LV_in and WAL of these two lobes, LLL and RUL, showed a significant relationship with TLC. At end-expiratory phase, the LV_ex-TL was 2325.2 (1969.7, 2722.5) mL with good correlation with RV (r = 0.811, p < 0.001), of which the LV_ex of RUL and RML had a significant relationship with RV. For the volumetric change within breathing, the ∆LV_TL was 2485.6 (2169.8, 3078.1) mL with good correlation with FVC (r = 0.719, p < 0.001), moreover, WAL_TL showed a better correlation with FVC (r = 0.817, p < 0.001) than that of ∆LV_TL. Likewise, there was also a strong association between ∆LV, WAL of these two lobes (LLL and RUL), and FVC. Conclusions: The quantitative indexes derived from paired inspiratory–expiratory chest CT could reflect the clinical pulmonary ventilatory function, LLL, and RUL give greater impact on ventilation. Thus, the pulmonary functional evaluation needs to be more precise and not limited to the total lung level.

Background: In this study, our focus was on pulmonary sequelae of coronavirus disease 2019 (COVID-19). We aimed to develop and validate CT-based radiomic models for predicting the presence of residual lung lesions in COVID-19 survivors at three months after discharge. Methods: We retrospectively enrolled 162 COVID-19 confirmed patients in our hospital (84 patients with residual lung lesions and 78 patients without residual lung lesions, at three months after discharge). The patients were all randomly allocated to a training set (n = 114) or a test set (n = 48). Radiomic features were extracted from chest CT images in different regions (entire lung or lesion) and at different time points (at hospital admission or at discharge) to build different models, sequentially, or in combination, as follows: (1) Lesion_A model (based on the lesion region at admission CT); (2) Lesion_D model (based on the lesion region at discharge CT); (3) Δlesion model (based on the lesion region at admission CT and discharge CT); (4) Lung_A model (based on the lung region at admission CT); (5) Lung_D model (based on the lung region at discharge CT); (6) Δlung model (based on the lung region at admission CT and discharge CT). The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the predictive performances of the radiomic models. Results: Among the six models, the Lesion_D and the Δlesion models achieved better predictive efficacy, with AUCs of 0.907 and 0.927, sensitivity of 0.898 and 0.763, and specificity of 0.855 and 0.964 in the training set, and AUCs of 0.875 and 0.837, sensitivity of 0.920 and 0.680, and specificity of 0.826 and 0.913 in the test set, respectively. Conclusions: The CT-based radiomic models showed good predictive effects on the presence of residual lung lesions in COVID-19 survivors at three months after discharge, which may help doctors to plan follow-up work and to reduce the psychological burden of COVID-19 survivors.