成像体模
医学
迭代重建
无线电技术
稳健性(进化)
核医学
再现性
人工智能
重建算法
正电子发射断层摄影术
算法
模式识别(心理学)
数学
放射科
计算机科学
统计
生物化学
化学
基因
作者
Masoomeh Fooladi,Yunus Soleymani,Arman Rahmim,Saeed Farzanefar,Farahnaz Aghahosseini,Negisa Seyyedi,Peyman Sh.Zadeh
标识
DOI:10.1016/j.ejrad.2024.111349
摘要
Abstract
Purpose
Radiomics analysis of oncologic positron emission tomography (PET) images is an area of significant activity and potential. The reproducibility of radiomics features is an important consideration for routine clinical use. This preliminary study investigates the robustness of radiomics features in PSMA-PET images across penalized-likelihood (Q.Clear) and standard ordered subset expectation maximization (OSEM) reconstruction algorithms and their setting parameters in phantom and prostate cancer (PCa) patients. Method
A NEMA image quality (IQ) phantom and 8 PCa patients were selected for phantom and patient analyses, respectively. PET images were reconstructed using Q.Clear (reconstruction β-value: 100–700, at intervals of 100 for both NEMA IQ phantom and patients) and OSEM (duration: 15sec, 30sec, 1 min, 2 min, 3 min, 4 min and 5 min for NEMA phantom and duration: 30 s, 1 min and 2 min for patients) reconstruction methods. Subsequently, 129 radiomic features were extracted from the reconstructed images. The coefficient of variation (COV) of each feature across reconstruction methods and their parameters was calculated to determine feature robustness. Results
The extracted radiomics features showed a different range of variability, depending on the reconstruction algorithms and setting parameters. Specifically, 23.0 % and 53.5 % of features were found as robust against β-value variations in Q.Clear and different durations in OSEM reconstruction algorithms, respectively. Taking into account the two algorithms and their parameters, eleven features (8.5 %) showed COV ≤ 5 % and eighteen (14 %) showed 5 % 20 %. The mean COVs of the extracted radiomics features were significantly different between the two reconstruction methods (p < 0.05) except for the phantom morphological features. Conclusions
All radiomics features were affected by reconstruction methods and parameters, but features with small or very small variations are considered better candidates for reproducible quantification of either tumor or metastatic tissues in clinical trials. There is a need for standardization before the implementation of PET radiomics in clinical practice.
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