Background: Metabolic tumour volume (MTV) measured on fluorodeoxyglucose F18 (FDG) positron emission tomography coupled with computed tomography (PET/CT) is a prognostic factor of advanced non-small cell lung cancer (NSCLC) treated by first-line immunotherapy. However, these tumours are often necrotic and the necrosis, which is hypometabolic in PET FDG, is not included in the MTV. The aim of this study was to evaluate the prognostic value of total tumour volume (TTV), adding necrotic tumour volume (NTV) to metabolic tumour volume (MTV).
View Article and Find Full Text PDFBackground & Aims: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition.
Methods: For model development, one hundred whole-body or torso F-fluorodeoxyglucose PET-CT scans of 100 patients were retrospectively included.
Our aim was to evaluate the performance in clinical research and in clinical routine of a research prototype, called positron emission tomography (PET) Assisted Reporting System (PARS) (Siemens Healthineers) and based on a convolutional neural network (CNN), which is designed to detect suspected cancer sites in fluorine-18 fluorodeoxyglucose (F-FDG) PET/computed tomography (CT). We retrospectively studied two cohorts of patients. The first cohort consisted of research-based patients who underwent PET scans as part of the initial workup for diffuse large B-cell lymphoma (DLBCL).
View Article and Find Full Text PDFThe metabolic tumour volume (MTV) is an independent prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, its measurement is not standardised and is subject to wide variations depending on the method used. This study aimed to compare the reproducibility of MTV measurement as well as the thresholds obtained for each method and their prognostic values.
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