Abstract
18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) is commonly used in clinical practice and clinical drug development to identify and quantify metabolically active tumors. Manual or computer-assisted tumor segmentation in FDG-PET images is a common way to assess tumor burden, such approaches are both labor intensive and may suffer from high inter-reader variability. We propose an end-to-end method leveraging 2D and 3D convolutional neural networks to rapidly identify and segment tumors and to extract metabolic information in eyes to thighs (whole body) FDG-PET/CT scans. The developed architecture is computationally efficient and devised to accommodate the size of whole-body scans, the extreme imbalance between tumor burden and the volume of healthy tissue, and the heterogeneous nature of the input images. Our dataset consists of a total of 3664 eyes to thighs FDG-PET/CT scans, from multi-site clinical trials in patients with non-Hodgkin’s lymphoma (NHL) and advanced non-small cell lung cancer (NSCLC). Tumors were segmented and reviewed by board-certified radiologists. We report a mean 3D Dice score of 88.6% on an NHL hold-out set of 1124 scans and a 93% sensitivity on 274 NSCLC hold-out scans. The method is a potential tool for radiologists to rapidly assess eyes to thighs FDG-avid tumor burden.
Highlights
18F-fluorodeoxyglucose positron-emission tomography (FDG-PET) is a widely used imaging modality in oncology, where radiolabeled glucose is intravenously administered and is rapidly taken up by metabolically active tumors
We present a novel memory-efficient NN architecture that enables a robust and rapid automated segmentation of tumors from 3D eyes to thighs FDG-PET/CT scans without need of downsampling
Our experiments show that this model, trained solely on a large dataset of diffuse large B cell lymphoma (DLBCL) patient scans, produces robust results in follicular lymphoma (FL) and non-small cell lung cancer (NSCLC) patient scans
Summary
18F-fluorodeoxyglucose positron-emission tomography (FDG-PET) is a widely used imaging modality in oncology, where radiolabeled glucose is intravenously administered and is rapidly taken up by metabolically active tumors. This imaging technology provides a means to visualize and quantify metabolically active tumor burden in patients, and FDG-PET has been applied to a wide range of cancer types, with differing degrees of FDG uptake. FDG-PET has been found to be Analysis and interpretation of FDG-PET images is performed by trained radiologists or readers who visually inspect the images for tumors and define individual tumor boundaries (region of interest, ROI) manually, or with the use of semi-automated image analysis software. Manually driven analyses will suffer from intra- and inter-reader variability
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
