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

The BPL reconstruction algorithm may enhance the robustness of PET radiomics features, supporting their use in clinical settings for non-invasive diagnosis and staging. The adoption of BPL towards improved PET radiomics robustness has the potential to transform NSCLC evaluation and management, but still needs standardization.

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Publication Details

Journal
BMC Medical Imaging
Published
2025-07-01
DOI
https://doi.org/10.1186/s12880-025-01774-2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Arman Rahmim, Jalil Pirayesh Islamian, Yunus Soleymani, Neda Gilani et al.
BMC Medical Imaging
Radiomics and Machine Learning in Medical Imaging
article

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

Arman Rahmim, Jalil Pirayesh Islamian, Yunus Soleymani, Neda Gilani, Peyman Sheikhzadeh, Saeed Farzanehfar, Farahnaz Aghahosseini, Zahra Valibeiglou
article en

Abstract

The BPL reconstruction algorithm may enhance the robustness of PET radiomics features, supporting their use in clinical settings for non-invasive diagnosis and staging. The adoption of BPL towards improved PET radiomics robustness has the potential to transform NSCLC evaluation and management, but still needs standardization.

BMC Medical ImagingVol. 25(1)
University of British Columbia (CA), Iran University of Medical Sciences (IR), Tabriz University of Medical Sciences (IR), Tehran University of Medical Sciences (IR)
Tabriz University of Medical Sciences, Tehran University of Medical Sciences and Health Services, University of Tabriz
Openalex Percentile: Top 77%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies — Arman Rahmim, Jalil Pirayesh Islamian, et al. · BMC Medical Imaging (2025) | TGRS Research Map | TGRS