AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of malignancy of pure ground-glass opacities (GGOs), using observations from computed tomography (CT) and 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission...
BACKGROUND: We designed a deep learning model for assessing F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
May 3, 2019
Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTV. Automatic segmentation algorithms applied to F-FDG PET (PET-AS) imaging hav...
BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a mo...
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diag...
International journal of neural systems
Mar 3, 2019
Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeo...
AJR. American journal of roentgenology
Dec 12, 2018
OBJECTIVE: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (SUV) of lymph nodes in patients with cancer using the unenhanced CT images from a PET/CT examination,...
Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to t...
AJR. American journal of roentgenology
Nov 1, 2018
OBJECTIVE: The purpose of this study is to establish the feasibility, safety, diagnostic performance, and clinical impact of real-time intraprocedural F-FDG PET/CT-guided automated robotic arm-assisted biopsy of hypermetabolic marrow or bone lesions.
AIM: To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images.