Objective:To explore the pattern of pulmonary vascular remodeling in patients with chronic obstructive pulmonary disease (COPD) using artificial intelligence technology based on chest CT images.Methods:This was a cross-sectional study. The clinical and imaging data of 257 patients with stable COPD who underwent chest high resolution CT (HRCT) and pulmonary function tests (PFT) from January 2018 to October 2022 at Shanxi Bethune Hospital were retrospectively analyzed. In addition, 28 healthy individuals with normal HRCT and PFT were collected in the same period as a control group. According to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grading criteria, COPD patients were classified into 31 cases of GOLD 1, 116 cases of GOLD 2, 82 cases of GOLD 3, and 28 cases of GOLD 4. FACT digital lung software was used to automatically segment the pulmonary arteries and pulmonary veins of all the cases, and to calculate the relevant pulmonary vascular parameters, including total lung volume (TLV), vessel volumes at all levels [cross-sectional area less than 5 mm 2 (CSA <5), between 5 and 10 mm 2 (CSA 5-10), and more than 10 mm 2 (CSA >10)], number of vascular branches, and vascular density (pulmonary vascular volume/TLV). Percentage of emphysema (%LAA) and pulmonary artery diameter/aortic diameter (PAD/AD) were calculated for all cases. ANOVA or Kruskal-Wallis H test was used for multiple intergroup comparisons, and LSD test or Bonferroni correction was used for within-group pairwise comparisons. Spearman correlation test was conducted to examine the relationship between CT pulmonary vascular parameters and pulmonary function parameters, as well as %LAA, in both the control group and the COPD group. Results:Differences in age, body mass index, pulmonary function parameters, %LAA and PAD/AD were statistically significant among the 5 groups ( P<0.001). Differences in overall pulmonary vascular density parameters were statistically significant among the 5 groups ( P<0.05). Differences in pulmonary arterial density parameters among the 5 groups with CSA <5, CSA 5-10, and CSA >10 were statistically significant ( P<0.05). The pulmonary arterial density values of GOLD 1 CSA <5, CSA 5-10 and CSA >10 were higher than those of the control group, and then showed a decreasing trend with the increase of COPD severity. The differences in pulmonary venous density parameters among the 5 groups with CSA< 5, CSA 5-10, and CSA >10 were statistically significant ( P<0.001), and the CSA 5-10 pulmonary venous density was higher in GOLD 1 patients than in the control group, and the remaining pulmonary venous densities showed a gradual decreasing trend with the increase in the severity of COPD. The number of arterial and venous vascular branches/TLV tended to decrease in the control group, GOLD 1, GOLD 2, GOLD 3, and GOLD 4 patients ( P<0.001). Pulmonary vascular density parameters were positively correlated with all PFT parameters ( r=0.138-0.510, P<0.05), and negatively correlated with %LAA ( r=-0.340--0.671, P<0.001); PAD/AD was negatively correlated with PFT parameters ( r=-0.208--0.286, P<0.001) and positively correlated with %LAA ( r=0.131, P<0.05). Conclusion:Various pulmonary vascular density parameters can be quantitatively analyzed by artificial intelligence technology based on chest CT images, which can reveal the changing pattern of pulmonary vascular remodeling in COPD patients.

Objective:To investigate the CT features and dynamic changes of new developed lung cancer in patients with connective tissue disease-related interstitial lung disease (CTD-ILD).Methods:A series of chest CT images of 58 CTD-ILD patients during follow-up were collected. The CT features of interstitial lung disease, the initial appearance time of lung cancer, the time of diagnosis of lung cancer, the morphological characteristics (location, shape, size) of lung cancer lesions and the dynamic changes of CT features were analyzed.Results:Among 58 patients, rheumatoid arthritis was the most common (31 cases). Chest CT images showed coexistence of two or more interstitial CT signs. During the follow-up, a total of 59 lung cancer lesions were found. The median time of lung cancer lesion occurred was 289 days. The median delay in diagnosis was 43 days. There were 44 cases of non-small cell lung cancer (including 23 cases of squamous cell carcinoma and 19 cases of adenocarcinoma), 12 cases of small cell lung cancer. Forty-three (72.9%) lesions were located in the lower lobes and 41 (69.5%) lesions were located in the area of pulmonary interstitial fibrosis. According to CT morphological characteristics of lung cancer, nodular type (37 cases), inflammatory consolidation (12 cases) and intra-honeycomb type (10 cases) were identified.Conclusions:The chest CT features of patients with CTD-ILD are complex. New developed lung cancer is easily missed or misdiagnosed in the early stage. Pay attention to the special CT characteristics of CTD-ILD with lung cancer is helpful for early diagnosis.

Purpose To investigate the predictive value of chest CT-based radiomics for spread through air spaces in stage T1 peripheral type lung cancer.Materials and Methods A total of 173 patients with surgically pathologically confirmed stage T1 non-small cell lung cancer were retrospectively collected and divided into positive group(49 cases)and negative group(124 cases)according to the presence or absence of spread through air spaces.All lesions were randomly divided into training set(122 cases)and validation set(51 cases)according to the ratio of 7∶3.The primary area of lung cancer(the main body of the lesion),the peripheral infiltrative area(a 5-mm annular area expanding outward along the edge of the lesion)and the tumor margin area(a 5-mm annular area retracting inward along the edge of the lesion)were used as areas of interest to extract imaging histological features.Three imaging histological models were established for the primary area of lung cancer,the peripheral infiltrative area and the tumor margin area,and combined with the morphological features of CT to establish three combined models.The efficacy of each model was evaluated and the optimal model was selected.Results The lobulation signs of positive group was significantly more than that of negative group(χ2=9.946,P=0.002).The area under the curve(AUC)of the imaging histological model based on the three regions of interest were 0.899,0.825,0.840 for the training group and 0.876,0.811 and 0.832 for the validation group,respectively.The model with the highest AUC was the primary tumor imaging model(P=0.043,P<0.001,P=0.017),the AUC of the combined model established by adding the lobar sign were 0.917,0.835 and 0.851,respectively.The AUC of the three regions of interest in the validation group were 0.912,0.832,and 0.845 and the highest AUC was found in the primary tumor area(P<0.001,P=0.017,P=0.049).Conclusion It is feasible to study lung cancer with airway metastasis via CT-based radiomics,taken lobulation signs as the risk predictive factor.

Objective: To investigate the value of computed tomography (CT) texture analysis in differential diagnosis of inflammatory and malignant pulmonary nodules. Methods: The image data of 54 patients with lung cancer and 36 patients with pulmonary inflammatory nodules were retrospectively collected in our hospital. All the patients received chest CT scan. CT texture analysis of entropy, correlation degree and contrast ratio were performed by the MaZda software. The receiver operating characteristic curve (ROC) was established and the area under the curve (AUC) was calculated to evaluate the value of CT texture analysis in differential diagnosis of inflammatory and malignant pulmonary nodules. Results: In the lung cancer group, the value of entropy, correlation degree and contrast ratio were 1.58±0.07, 0.02±0.17 and 8.79±2.59, respectively. In the inflammatory nodules group, the value of entropy, correlation degree and contrast ratio were 1.51±0.04, 0.22±0.16 and 12.53±2.24, respectively. The differences were all statistically significant (P values were 0.008, 0.027, and 0.006, respectively) between two groups. There was not statistically significant difference (P>0.05) in the CT values between the lung cancer group and the inflammatory nodule group based on the non-contrast enhanced CT scan. Meanwhile, there was no statistically significant difference (P>0.05) in the value of entropy, correlation degree or contrast ratio between two groups based on arterial phase or venous phase of contrast enhanced CT. The ROC analysis showed that the AUC in differentiating the lung cancer and inflammatory nodules was 0.821, 0.778 and 0.875, respectively. The AUC of combination of three phases was 0.931, which was higher than the AUC of entropy, correlation degree and contrast ratio respectively (P<0.01). The sensitivity was 88.9%, and the specificity was 87.5%. Conclusion CT texture analysis is a high-potential image analysis method, which can provide more information for the differential diagnosis of benign and malignant pulmonary nodules.

Objective:To investigate the CT features and dynamic changes of new developed lung cancer in patients with connective tissue disease-related interstitial lung disease (CTD-ILD).Methods:A series of chest CT images of 58 CTD-ILD patients during follow-up were collected. The CT features of interstitial lung disease, the initial appearance time of lung cancer, the time of diagnosis of lung cancer, the morphological characteristics (location, shape, size) of lung cancer lesions and the dynamic changes of CT features were analyzed.Results:Among 58 patients, rheumatoid arthritis was the most common (31 cases). Chest CT images showed coexistence of two or more interstitial CT signs. During the follow-up, a total of 59 lung cancer lesions were found. The median time of lung cancer lesion occurred was 289 days. The median delay in diagnosis was 43 days. There were 44 cases of non-small cell lung cancer (including 23 cases of squamous cell carcinoma and 19 cases of adenocarcinoma), 12 cases of small cell lung cancer. Forty-three (72.9%) lesions were located in the lower lobes and 41 (69.5%) lesions were located in the area of pulmonary interstitial fibrosis. According to CT morphological characteristics of lung cancer, nodular type (37 cases), inflammatory consolidation (12 cases) and intra-honeycomb type (10 cases) were identified.Conclusions:The chest CT features of patients with CTD-ILD are complex. New developed lung cancer is easily missed or misdiagnosed in the early stage. Pay attention to the special CT characteristics of CTD-ILD with lung cancer is helpful for early diagnosis.