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Chronic lung allograft dysfunction (CLAD) is a major cause of mortality in lung transplant recipients. CLAD can be sub-divided into at least 2 subtypes with distinct mortality risk characteristics: restrictive allograft syndrome (RAS), which demonstrates increased overall computed tomography (CT) lung density in contrast with bronchiolitis obliterans syndrome (BOS), which demonstrates reduced overall CT lung density. This study aimed to evaluate a reader-independent quantitative density metric (QDM) derived from CT histograms to associate with CLAD survival. A retrospective study evaluated CT scans corresponding to CLAD onset using pulmonary function tests in 74 patients (23 RAS, 51 BOS). Two different QDM values (QDM1 and QDM2) were calculated using CT lung density histograms. Calculation of QDM1 includes the extreme edges of the histogram. Calculation of QDM2 includes the central region of the histogram. Kaplan-Meier analysis and Cox regression analysis were used for CLAD prognosis. Higher QDM values were significantly associated with decreased survival. The hazard ratio for death was 3.2 times higher at the 75th percentile compared to the 25th percentile using QDM1 in a univariate model. QDM may associate with CLAD patient prognosis.

BACKGROUND AND PURPOSE: Recent studies demonstrated superiority of CTP to NCCT/CTA at detecting lacunar infarcts. This study aimed to assess CTP's capability to identify lacunae in different intracranial regions. MATERIALS AND METHODS: Over 5.5 years, 1085 CTP examinations were retrospectively reviewed in patients with acute stroke symptoms with CTP within 12 hours and MRI within 7 days of symptom onset. Patients had infarcts Յ2 cm or no acute infarct on DWI; patients with concomitant infarcts Ͼ2 cm on DWI were excluded. CTP postprocessing was automated by a delay-corrected algorithm. Three blinded reviewers were given patient NIHSS scores and symptoms; infarcts were recorded based on NCCT/CTA, CTP (CBF, CBV, MTT, and TTP), and DWI. RESULTS: One hundred thirteen patients met inclusion criteria (53.1% female). On DWI, lacunar infarcts were present in 37 of 113 (32.7%), and absent in 76 of 113 (67.3%). On CTP, lacunar infarcts typically appeared as abnormalities larger than infarct size on DWI. Interobserver for CTP ranged from 0.38 (CBF) (P Ͻ .0001) to 0.66 (TTP) (P Ͻ .0001); interobserver for DWI was 0.88 (P Ͻ 0.0001). In all intracranial regions, sensitivity of CTP ranged from 18.9% (CBV) to 48.7% (TTP); specificity ranged from 97.4% (CBF and TTP) to 98.7% (CBV and MTT). CTP's sensitivity was highest in the subcortical white matter with or without cortical involvement (21.7%-65.2%) followed by periventricular white matter (12.5%-37.5%); sensitivity in the thalami or basal ganglia was 0%. CONCLUSIONS: CTP has low sensitivity and high specificity in identifying lacunar infarcts. Sensitivity is highest in the subcortical white matter with or without cortical involvement, but limited in the basal ganglia and thalami.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.

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