Publications by authors named "Jose L L Gongora"
Aim: To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference.
Methods: Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7-75), 46% female.
Automated classification of PET-CT lesions in lung cancer: An independent validation study.
Clin Physiol Funct Imaging
September 2022
Introduction: Recently, a tool called the positron emission tomography (PET)-assisted reporting system (PARS) was developed and presented to classify lesions in PET/computed tomography (CT) studies in patients with lung cancer or lymphoma. The aim of this study was to validate PARS with an independent group of lung-cancer patients using manual lesion segmentations as a reference standard, as well as to evaluate the association between PARS-based measurements and overall survival (OS).
Methods: This study retrospectively included 115 patients who had undergone clinically indicated (18F)-fluorodeoxyglucose (FDG) PET/CT due to suspected or known lung cancer.
Background: Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data.
Purpose: We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer.