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Background: In the past, many researchers have studied the correlation between quantitative parameters of computed tomography (CT) and parameters of pulmonary function test (PFT) in patients with chronic obstructive pulmonary disease (COPD) with good results. Most of these studies have focused on the whole-lung level. In this study, we analyzed the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes of the lungs of patients with different grades of COPD and assessed their relationship with different lung function indices. Methods: We retrospectively collected patients who underwent PFTs at The First Affiliated Hospital of Guangzhou Medical University from 1 July 2019 to 27 January 2020, and underwent chest respiratory dual-phase CT scans within 1 week, including 112 non-COPD patients and 297 COPD patients. We quantified the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes using a pulmonary image analysis tool. One-way analysis of variance (ANOVA) and Kruskal-Wallis H method were used to compare the quantitative CT parameters of each lung lobe in different groups. The correlation between quantitative CT parameters of different lung lobes and lung function indices was assessed using multiple linear regression. Results: Among the 3 biphasic CT lung volume parameters, only volume change/inspiratory lung volume (∆LV/LVin) in the non-COPD control, mildly to moderately severe, and severe to extremely severe groups had statistical differences in each lobe level (all P<0.05). Correlation was significant between LVin and different lung function indices and between low attenuation areas percent below the threshold of -950 in the inspiratory phase [low attenuation area below -950 in the inspiratory phase (%LAA-950in)] and lung function indices in the left lower lobe (all P<0.05). There was statistically significant correlation between expiratory lung volume and ∆LV/LVin and lung function indices in the right lower lung (all P≤0.001). In the remaining lobes, LVin, expiratory lung volume, ∆LV/LVin, and %LAA-950in correlated with only some of the lung function indices. Conclusions: The percentage of emphysema volume did not differ between lobes in the non-COPD control and severe to extremely severe COPD populations. LVin and %LAA-950in in the left upper lobe, expiratory lung volume and ∆LV/LVin in the right lower lobe were more reflective of the changes in lung function indices of the patients, whereas the correlation of the 3 biphasic CT lung volume parameters and the percentage of emphysema volume in the upper lobes of both lungs and the right middle lung with lung function indices was unclear.
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Background: It is clinically important to accurately assess the pulmonary function of patients with lung cancer, especially before surgery. This knowledge can help clinicians to monitor patients pre- and post-surgery, predict the impact of surgery on pulmonary function, and help to optimize postsurgical recovery. We used a deep learning approach for assessing pulmonary function on computed tomography (CT) scans in patients with lung cancer before they underwent surgery. Methods: A total of 188 patients with lung cancer whose diagnoses had been pathologically confirmed were enrolled in this study. We used a software to automatically delineate regions of interest (ROIs) throughout the airways, lobes, and the whole lungs. We then used AK software to extract radiomics features of the 3 types of ROIs. We randomly separated these cases into a training cohort and a test cohort at a ratio of 7:3. We next constructed a logistic regression model to assess pulmonary function from the radiomics features. The machine learning outcomes were compared with established clinical criteria for pulmonary function. including forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), FVC, and maximum vital capacity (VCmax) to evaluate the accuracy of the machine learning model. Results: In the ROIs of the lobes, our results showed that the machine learning model had good performance in predicting FVC and VCmax, attaining a Spearman correlation r value of 0.714 with P<0.001 for FVC and a r value of 0.687 with P<0.001 for VCmax. Using the airway ROIs, our model achieved a r of 0.603 with P=0.001 for VCmax. Using the whole lung ROIs, our model achieved a r of 0.704 with P<0.001 for FVC and a r of 0.693 with P<0.001 for VCmax. Conclusions: Preoperative CT may provide a means for evaluating pulmonary function in patients with lung cancer. With radiomics features extracted from the airway, lobes, and the whole lung region, and a properly trained machine learning model, it is possible to obtain accurate estimation for metrics used in clinical criteria and to offer clinicians imaging-based indicators for the status of pulmonary functions.
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BACKGROUND: Accurate diagnosis of mediastinal lymph node (LN) metastases is very important for the treatment and prognosis in lung cancer patients. Spectral computed tomography (CT), as a non-invasive approach, has good prospects for detecting mediastinal nodal metastasis. However, the diagnostic criteria of differentiating metastatic and nonmetastatic LNs have not been determined. METHODS: Clinical and imaging data of 64 lung cancer patients (mean age 61.3±10.3 years, 41 men) from April to December 2019 were retrospectively analyzed. The unenhanced scan and contrast enhanced arterial phase (AP) and venous phase (VP) spectral CT scans were performed. The 70 keV monochromatic image and iodine-based image in all phases were analyzed to measure the parameters of LNs. LNs were divided into the metastatic and non-metastatic groups based on confirmative pathological results, and their differences were statistically analyzed. The receiver operating characteristics curve (ROC) was used to evaluate the efficacy of the differential diagnosis. RESULTS: Seventy-four metastatic LNs and 152 non-metastatic LNs were obtained. Compared with non-metastatic LNs, metastatic LNs often had a larger size (P<0.001). In the unenhanced scans, the density of metastatic LNs was lower than that of non-metastatic LNs (P<0.001); however, there was no difference in CT value in AP and VP between metastatic and non-metastatic LNs (P=0.07, P=0.08, respectively). A statistically significant difference was found in iodine concentration (IC), normalized iodine concentration (NIC) and slope of the spectral curve (λHU) in unenhanced scan, IC and λHU in AP, as well as IC, NIC and λHU in VP between metastatic and non-metastatic LNs. There was no difference in NIC in AP between them. CONCLUSIONS: Combined with morphology, spectral CT quantitative parameters demonstrate certain diagnostic efficiency for differential diagnosis between metastatic and non-metastatic LNs in lung cancer patients.
