Quantitative CT imaging analysis to predict pathology features in patients with a Congenital Pulmonary Airway Malformation

Background: Risk for infection and potential malignant degeneration are the most common arguments for resecting asymptomatic Congenital Pulmonary Airway Malformations (CPAM). We aimed to investigate if CT- imaging characteristics can be used to predict histopathological features, by using an objective quantitative CT scoring method. Methods: Archival CPAM tissue samples were histologically re-assessed and patients who had a pre-operative volumetric CT-scan were included. Lung disease was quantified using the newly-developed congenital lung abnormality quantification(CLAQ) scoring method and obtained percentages were used to predict histopathological signs of inflammation and presence of mucinous proliferation (MP). Because MP is presumed a precursor for mucinous adenocarcinoma in situ (AIS) this method was also used to compare CT-scans of patients with AIS to those with only CPAM. Results: Thirty-three CPAM patients were included of which 13(39%) had histological signs of inflammation and 8(24%) had a MP. Patients with inflammation had a significantly smaller lesion (14% vs 38%) while those with MP had more extensive disease (54%vs17%). Patients with AIS had a significantly smaller lesion compared to CPAM patients (5%vs29%). Significant predictors for inflammation were smaller lesion size and percentage hypodensity within lesions while a larger lesion size and percentage parenchymal hyperdensity (solid lung tissue components) were predictors for MP as well as AIS. Conclusions: Smaller CPAM lesions may be more susceptible to inflammation while larger lesions may be associated with the presence of MP. Parenchymal hyperdensity is found as a predictor for MP as well as AIS and should therefore elicit more extensive gross sampling. Level of evidence: Level III

Genetic, Physiological, and Lifestyle Predictors of Mortality in the General Population

Objectives. We investigated the quality of 162 variables, focusing on the contribution of genetic markers, used solely or in combination with other characteristics, when predicting mortality. Methods. In 5974 participants from the Rotterdam Study, followed for a median of 15.1 years, 7 groups of factors including age and gender, genetics, socioeconomics, lifestyle, physiological characteristics, prevalent diseases, and indicators of general health were related to all-cause mortality. Genetic variables were identified from 8 genome-wide association scans (n = 19 033) and literature review. Results. We observed 3174 deaths during follow-up. The fully adjusted model (C-statistic for 15-year follow-up [C(15y)] = 0.80; 95% confidence interval [CI] = 0.75, 0.77) predicted mortality well. Most of the additional information apart from age and sex stemmed from physiological markers, prevalent diseases, and general health. Socioeconomic factors and lifestyle contributed meaningfully to mortality risk prediction with longer prediction horizon. Although specific genetic factors were independently associated with mortality, jointly they contributed little to mortality prediction (C(15y) = 0.56; 95% CI = 0.55, 0.57). Conclusions. Mortality can be predicted reasonably well over a long period. Genetic factors independently predict mortality, but only modestly more than other risk indicators