PurposePatients with combined pulmonary fibrosis and emphysema (CPFE) have been suggested to have an increased risk of lung cancer. We conducted a systematic review of all published data and performed a meta-analysis to define the characteristics of lung cancer that develops in CPFE.MethodWe searched Pubmed, Embase, and Cochrane to find original articles about lung cancer and CPFE published prior to September 2015. All titles/abstracts were reviewed by two radiologists to identify articles that used predefined selection criteria. Summary estimates were generated using a random-effect model and odds ratios (ORs) to develop squamous cell carcinoma (SqCC) were calculated. Kaplan–Meier survival curves were obtained for the survival of patients with CPFE and non-CPFE.ResultsNine original articles that assessed 620 patients were included in this review. In the pooled data, patients were older age (70.4 years), almost all were heavy smokers (53.5 pack years), and males were predominant (92.6%). SqCC was the most common type (42.3%), followed by adenocarcinoma (34.4%). Compared with lung cancer population with an otherwise normal lung, the OR to develop SqCC in CPFE was 9.06 (95% CI, 6.08–13.5). The ORs in CPFE compared with lung cancers that developed in lungs with fibrosis or emphysema were also higher. The median survival for CPFE patients with lung cancer (19.5 months) was significantly shorter than in non-CPFE (53.1 months).ConclusionsLung cancer in CPFE, most commonly SqCC, presents in elderly heavy smokers with a male predominance. The median survival for CPFE patients with lung cancer is 19.5 months.

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods:We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and falsepositive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion:The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.