Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies

Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies

Abstract Purpose This study aims to enhance the diagnostic and prognostic capabilities of PET imaging through improved robustness of radiomics features, utilizing the Bayesian penalized likelihood (BPL) reconstruction algorithm. Specifically, we focus on 18F-FDG PET imaging of lung cancer, which, wi...

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Main Authors: Zahra Valibeiglou, Jalil Pirayesh Islamian, Yunus Soleymani, Saeed Farzanehfar, Farahnaz Aghahosseini, Neda Gilani, Arman Rahmim, Peyman Sheikhzadeh
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Imaging
Subjects:
Non-small cell lung carcinoma
NEMA image quality phantom
18F-FDG PET
Reconstruction algorithm
Radiomics features
Online Access:https://doi.org/10.1186/s12880-025-01774-2
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https://doi.org/10.1186/s12880-025-01774-2

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