Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66–0.73 (sensitivity of 80.0–85.0% and specificity of 54.2–59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients.

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. To develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), or minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (mean age, 53.2 years; 119 men, 283 women) with a total of 448 pGGNs on noncontrast chest CT that underwent resection from January 2019 to June 2022 and were histologically diagnosed as AAH (n=29), AIS (n=83), MIA (n=235), or IAC (n=101). We developed Lung-PNet, a 3D deep learning model for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules from January 2019 to December 2021 were randomly allocated to training (n=327) and internal test (n=82) sets; nodules from January 2022 to June 22 formed a holdout test set (n=39). Segmentation performance was assessed by Dice coefficients, using radiologists' manual segmentations as reference. Classification performance was assessed by AUCROC and AUC under precision-recall curve (AUCPR), and compared with that of four readers (three radiologists, one surgeon). Code is publicly available: https://github.com/Xiaodong-Zhang-PKUFH/Lung-PNet.git. RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0838, and AUCROC and AUCPR for classification as IAC were 0.911 and 0.842. At threshold probability of ≥ 50.0% for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. Accuracy and F1 score (with p values vs Lung-PNet) were, in the holdout test set: reader 1, 51.3% (p=.02) and 48.7% (p=.008); reader 2, 79.5% (p=.75) and 75.0% (p=.10); reader 3, 66.7% (p=.35) and 68.3% (p<.001); reader 4, 71.8% (p=.48) and 42.1% (p=.18). CONCLUSION. Lung-PNet exhibited robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGNN management.

We assessed the value of the multiplanar reconstruction technique (MRT) for computed tomography‐guided lung biopsy. We evaluated 72 difficult biopsy cases (traditional method = 44; MRT = 28) to compare patient and lesion characteristics, diagnostic accuracy, complications, radiation dose, and procedure duration. Diagnostic accuracy was significantly higher using MRT than the traditional method (100% vs. 84.1%, respectively; P = 0.038). There were no severe complications in the MRT group, but one case each of severe pneumothorax and fatal hemothorax in the traditional method group. The dose‐length product rate was lower and the procedure duration slightly higher in the MRT than in the traditional group (336.83 vs. 479.64 and 33.39 vs. 25.93 minutes, respectively). MRT using computed tomography‐guided lung biopsy could improve diagnostic accuracy and avoid severe complications compared to the traditional method.

The therapeutic efficacy of percutaneous transluminal angioplasty and stenting (PTAS) of post-irradiated stenosis of subclavian artery (PISSA) was not well clarified. This retrospective study was designed to evaluate the technical safety and outcome of the patients of severe symptomatic PISSA accepted PTAS. Between 2000 and 2019, 16 cases with 17 lesions of symptomatic and medically refractory PISSA accepted PTAS were included. We evaluated their technical success, peri-procedural complications and diffusion-weight imaging (DWI) of brain magnetic resonance imaging (MRI), results of symptom relief, and long-term stent patency. The stenosis of the 17 stenotic lesions were 81.2 ± 11.1%. The most common symptom of the 16 patients was dizziness (14/16, 87.5%). All successfully accepted PTAS without neurological complication and had symptom relief after PTAS (17/17, 100%). Of the 12 patients accepted pre-procedural and early post-procedural MRI follow-up, 2 patients had an asymptomatic tiny acute embolic infarct in the territory of vertebrobasilar system. In a 51.9 ± 54.9 months follow-up, all patients had no severe restenosis and no recurrent vertebrobasilar ischemic symptoms. For patients with PISSA and medically refractory ischemic symptoms, PTAS can be an effective alternative management.

Background: Features of pericoronary adipose tissue (PCAT) from coronary computed tomography angiography (CCTA) are associated with inflammation and cardiovascular risk. As PCAT is vascularly connected with coronary vasculature, the presence of iodine is a potential confounding factor on PCAT HU and textures that has not been adequately investigated. We aim to use dynamic cardiac CT perfusion (CCTP) to understand the perfusion of PCAT and determine its effects on PCAT assessment. Methods: From CCTP, we analyzed HU dynamics of territory-specific PCAT, the myocardium, and other adipose depots in patients with coronary artery disease. HU, blood flow, and radiomics were assessed over time. Changes from peak aorta time, P_a, chosen to model the acquisition time of CCTA, were obtained. Results: HU in PCAT increased more than in other adipose depots. Blood flow in PCAT was ~23% of that in the contiguous myocardium. A two-second offset [before, after] P_a resulted in [4 ± 1.1 HU, 3 ± 1.5 HU] differences in PCAT, giving a 7 HU swing. Due to changes in HU, the apparent PCAT volume reduced by ~15% from the first scan (P₁) to P_a using a conventional fat window. Comparing radiomic features over time, 78% of features changed >10% relative to P₁. Distal and proximal to a significant stenosis, we found less enhancement and longer time-to-peak distally in PCAT. Conclusions: CCTP elucidates blood flow in PCAT and enables the analysis of PCAT features over time. PCAT assessments (HU, apparent volume, and radiomics) are sensitive to acquisition timing and obstructive stenosis, which may confound the interpretation of PCAT in CCTA images. Data normalization may be in order.