Sensitivity of an AI method for [¹⁸F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols.

ABSTRACT:

Background

Convolutional neural networks (CNNs), applied to baseline [¹⁸F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols. Baseline [¹⁸F]FDG PET/CT scans were collected from 20 DLBCL patients. EARL1, EARL2 and high-resolution (HR) protocols were applied per scan, generating three images with different image qualities. Image-based transformation was applied by blurring EARL2 and HR images to generate EARL1 compliant images using a Gaussian filter of 5 and 7 mm, respectively. MIPs were generated for each of the reconstructions, before and after image trans

SUBMITTER: Ferrandez MC 

PROVIDER: S-EPMC10533444 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature