Accurate Tumor Delineation Rough Volume of Interest Analysis for F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.

Background: The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (F-FDG PET/CT) images based on a "rough" volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses.

Methods: A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively ( = 87) and prospectively ( = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer™) components.

Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer.

Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular; however, it has been shown that large variations in performance can be obtained by relying on different approaches. The purpose of this study was to evaluate the potential benefit of combining different algorithms into an improved consensus for the final prediction, as it has been shown in other fields. The evaluation was carried out in the context of the use of radiomics from F-FDG PET/CT images for predicting outcome in stage II-III Non-Small Cell Lung Cancer.

A realistic computed tomography simulator for small motion analysis of cerebral aneurysms.

This paper describes a realistic simulator for the Computed Tomography (CT) scan process for motion analysis. In fact, we are currently developing a new framework to find small motion from the CT scan. In order to prove the fidelity of this framework, or potentially any other algorithm, we present in this paper a simulator to simulate the whole CT acquisition process with a priori known parameters.