No abstract

No abstract

Bedside chest X-rays (CXR) for catheter position control may add up to a considerable radiation dose for patients in the intensive care unit (ICU). In this study, image quality and dose reduction potentials of a novel X-ray scatter correction software (SkyFlow, Philips Healthcare, Hamburg, Germany) were evaluated. CXRs of a 'LUNGMAN' (Kyoto Kagaku Co., LTD, Kyoto, Japan) thoracic phantom with a portacath system, a central venous line and a dialysis catheter were performed in an experimental set-up with multiple tube voltage and tube current settings without and with an antiscatter grid. Images with diagnostic exposure index (EI) 250-500 were evaluated for the difference in applied mAs with and without antiscatter grid. Three radiologists subjectively assessed the diagnostic image quality of grid and non-grid images. Compared with a non-grid image, usage of an antiscatter grid implied twice as high mAs in order to reach diagnostic EI. SkyFlow significantly improved the image quality of images acquired without grid. CXR with grid provided better image contrast than grid-less imaging with scatter correction.

BackgroundTo develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).MethodsOne hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used.ResultsThe ML algorithm achieved 87% sensitivity (95% IC 67.3–92.7), 80% specificity (95% CI 74.0–86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001).ConclusionML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.Electronic supplementary materialThe online version of this article (10.1186/s41747-019-0119-0) contains supplementary material, which is available to authorized users.