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.

Recent studies have demonstrated that some chemotherapeutic drugs can enhance antitumor immunity by eliminating and inactivating immunosuppressive cells. Oxaliplatin (OXP) induces immunogenic cell death by increasing the immunogenicity of cancer cells. However, the effects of OXP on the tumor immunosuppressive microenvironment remain unclear. The aim of the present study was to evaluate the antitumor activity of OXP by intraperitoneal (i.p.) administration in an abdominal implantation model of colon cancer and tested the tumor immune microenvironment to observe whether OXP affects the local immune inhibitory cell populations. Abdominal metastasis models were established by inoculation of CT26 cells. The antitumor efficacy of OXP and the tumor immune microenvironment were evaluated. The tumors and spleens of mice were harvested for flow cytometric analysis. Cluster of differentiation (CD)‑8+CD69+ T cells, regulatory T cells (Tregs), CD11b+F4/80high macrophages and myeloid‑derived suppressor cells (MDSCs) were evaluated by flow cytometric analysis. In vivo i.p. administration of OXP inhibited tumor growth in the abdominal metastasis model. Furthermore, OXP was observed to increase tumor‑infiltrating activated CD8+ T cells in tumors, decrease CD11b+F4/80high macrophages in tumors and decrease MDSCs in the spleen. These results suggested that i.p. administration of OXP alone may inhibit tumor cell growth and induce the antitumor immunostimulatory microenvironment by eliminating immunosuppressive cells.

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.

The purpose of this study was to identify risk factors in patients with surgically treated node-positive IB1-IIB cervical cancer and to establish a risk model for disease-free survival (DFS) and overall survival (OS). A total of 170 patients who underwent radical hysterectomy and bilateral pelvic lymphadenectomy as primary treatment for node-positive International Federation of Gynaecology and Obstetrics (FIGO) stage IB1-IIB cervical cancer from January 2002 to December 2008 were retrospectively analyzed. Five published risk models were evaluated in this population. The variables, including common iliac lymph node metastasis and parametrial invasion, were independent predictors of outcome in a multivariate analysis using a Cox regression model. Three distinct prognostic groups (low, intermediate, and high risk) were defined using these variables. Five-year DFS rates for the low-, intermediate-, and high-risk groups were 73.7%, 60.0%, and 25.0%, respectively (P<0.001), and 5-year OS rates were 81.9%, 42.8%, and 25.0%, respectively (P<0.001). The risk model derived in this study provides a novel means for assessing prognosis of patients with node-positive stage IB1-IIB cervical cancer. Future study will focus on external validation of the model and refinement of the risk scoring systems by adding new biologic markers.

Background: The aim of this study was to explore the predictive values of quantitative CT indices of the total lung and lung lobe tissue at discharge for the pulmonary diffusion function of coronavirus disease 2019 (COVID-19) patients at 5 months after symptom onset. Methods: A total of 90 patients with moderate and severe COVID-19 underwent CT scans at discharge, and pulmonary function tests (PFTs) were performed 5 months after symptom onset. The differences in quantitative CT and PFT results between Group 1 (patients with abnormal diffusion function) and Group 2 (patients with normal diffusion function) were compared by the chi-square test, Fisher’s exact test or Mann–Whitney U test. Univariate analysis, stepwise linear regression and logistic regression were used to determine the predictors of diffusion function in convalescent patients. Results: A total of 37.80% (34/90) of patients presented diffusion dysfunction at 5 months after symptom onset. The mean lung density (MLD) of the total lung tissue in Group 1 was higher than that in Group 2, and the percentage of the well-aerated lung (WAL) tissue volume (WAL%) of Group 1 was lower than that of Group 2 (all p < 0.05). Multiple stepwise linear regression identified only WAL and WAL% of the left upper lobe (LUL) as parameters that positively correlated with the percent of the predicted value of diffusion capacity of the lungs for carbon monoxide (WAL: p = 0.002; WAL%: p = 0.004), and multiple stepwise logistic regression identified MLD and MLD_LUL as independent predictors of diffusion dysfunction (MLD: OR (95%CI): 1.011 (1.001, 1.02), p = 0.035; MLD_LUL: OR (95%CI): 1.016 (1.004, 1.027), p = 0.008). Conclusion: At five months after symptom onset, more than one-third of moderate and severe COVID-19 patients presented with diffusion dysfunction. The well-aerated lung and mean lung density quantified by CT at discharge could be predictors of diffusion function in convalesce.