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Background: Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity. Methods: CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed. Results: All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81–0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, "kurtosis" had a high predictive value of early death (AUC at first year: 0.70–0.75 in two independent cohorts), negative association with histopathological grade (Spearman's r: − 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915–11.561) than histopathological staging and grading. Conclusions: We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to investigate whether ultra-low-dose chest computed tomography (CT) can be used for visual assessment of CT features in patients with pulmonary alveolar proteinosis (PAP) and to evaluate the relationship between the quantitative analysis of the ultra-low-dose CT scans and the pulmonary function tests (PFTs). Methods: Thirty-eight patients (mean [SD] age, 44.47 [12.28] years; 29 males, 9 females) with PAP were enrolled and subjected to two scans each with low-dose CT (reference parameters: 120 kV and 50 mAs) and ultra-low-dose CT (reference parameters, 80 kV, 25 mAs). Images were reconstructed via filtered back projection (FBP) for low-dose CT and iterative reconstruction (IR) for ultra-low-dose CT. All patients underwent PFT. The Visual analysis for ground glass opacity (GGO) is performed. The quantitative CT and PFT results were analyzed by canonical correlations. Results: The mean body mass index (BMI) was 25.37±3.26 kg/m2. The effective radiation doses were 2.30±0.46 and 0.24±0.05 mSv for low-dose and ultra-low-dose CT, respectively. The size-specific dose estimates were 5.81±0.81 and 0.62±0.09 mSv for low-dose and ultra-low-dose CT. GGOs and interlobular septal thickening were observed bilaterally in all patients. The average visual GGO score was lower in the upper field (2.67±1.24) but higher in the middle and lower fields (3.08±1.32 and 3.08±0.97, respectively). The average score for the whole lung was 2.94±1.19. There is a significant correlation between PFTs and quantitative of ultra-low-dose CT (canonical loading = 0.78). Conclusions: Ultra-low-dose CT has the potential to quantify the lung parenchyma changes of PAP. This technique could provide a sensitive and objective assessment of PAP and has good relation with PFTs. In addition, the radiation dose of ultra-low-dose CT was very low. [ABSTRACT FROM AUTHOR]

Objectives: To portray the stage characteristics of lung cancers detected in CT screenings, and explore whether there’s universal stage superiority over other methods for various pathological types using available data worldwide in a meta-analysis approach. Materials and Methods: EMBASE and MEDLINE were searched for studies on lung cancer CT screening in natural populations through July 2015 without language or other filters. Twenty-four studies (8 trials and 16 cohorts) involving 1875 CT-detected lung cancer patients were enrolled and assessed by QUADAS-2. Pathology-confirmed stage information was carefully extracted by two reviewers. Stage I or limited stage proportions were pooled by random effect model with Freeman-Tukey double arcsine transformation. Results: Pooled stage I cancer proportion in CT screenings was 73.2% (95% confidence interval: 68.6%, 77.5%), with a significant rising trend (Ptrend<0.05) from baseline (64.7%) to ≥5 repeat rounds (87.1%). Relative to chest radiograph and usual care, the increased stage I proportions in CT were 12.2% (P>0.05), and 46.5% (P<0.05), respectively. Pathology-specifically, adenocarcinomas (66%) and squamous cell lung cancers (17%) composed the majority of CT-detected lung cancers, and had significantly higher stage I proportions relative to chest radiograph (bronchioloalveolar adenocarcinomas, 80.9% vs 51.4%; other adenocarcinomas, 58.8% vs 38.3%; squamous cell lung cancers, 52.3% vs 38.3%; all P<0.05). However, the percentage of small cell lung cancer was lower using CT than other detection routes, and no significant difference in limited stage proportion was observed (6.8% vs 10.8%, P>0.05). Conclusion: CT screening can detect more early stage non-small cell lung cancers, but not all of them could be beneficial as there are a considerable number of indolent ones such as bronchioloalveolar adenocarcinomas. Still, current evidence is lacking regarding small cell lung cancers. [ABSTRACT FROM AUTHOR]

Background: In this study, the accuracy of ultra-low-dose computed tomography (CT) with iterative reconstruction (IR) for detection and measurement of pulmonary nodules was evaluated.Methods: Eighty-four individuals referred for lung cancer screening (mean age: 54.5±10.8 years) underwent low-dose computed tomography (LDCT) and ultra-low-dose CT. CT examinations were performed with attenuation-based tube current modulation. Reference tube voltage and current were set to 120kV/25mÅs for LDCT and 80kV/4mÅs for ultra-low-dose CT. CT images were reconstructed with filtered back projection (FBP) for LDCT, and with FBP and IR for ultra-low-dose CT datasets. A reference standard was established by a consensus panel of 2 different radiologists on LDCT. Volume and diameter of the solid nodules were measured on LDCT with FBP and ultra-low dose CT with FBP and IR. Interobserver and interscan variability were analyzed and compared by the Bland-Altman method.Results: A total of 127 nodules were identified, including 105 solid nodules, 15 part solid nodules, 7 ground glass nodules. On ultra-low-dose CT scans, the effective radiation dose was 0.13±0.11mSv. A total of 113 (88.9%) and 110 (86.6%) true-positive nodules with FBP versus 117 (92.1%) and 118(92.9%) with IR were detected by two observers, respectively. The volume and size of the 105 solid nodules were measured, with mean volume/diameter of 46.5±46.6 mm(3)/5.1±1.6mm. There was no significant difference in nodule volume or diameter measurements between ultra-low-dose CT and LDCT protocols for solid nodules.Conclusions: Ultra-low-dose CT with iterative reconstruction has high sensitivity for lung nodule detection without significant difference in nodule size and volume measurement compared to LDCT. [ABSTRACT FROM AUTHOR]