PET radiomics in lung cancer: advances and translational challenges

Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medic...

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Bibliographic Details
Published in:EJNMMI physics 2024-10, Vol.11 (1), p.81-29, Article 81
Main Authors: Zhang, Yongbai, Huang, Wenpeng, Jiao, Hao, Kang, Lei
Format: Article
Language:English
Subjects:
Applied and Technical Physics
Automation
Biological activity
Biomarkers
Blood flow
Clinical translation
Computational Mathematics and Numerical Analysis
Computed tomography
Diagnosis
Dimensional analysis
Engineering
Feature extraction
Health services
Image acquisition
Image segmentation
Imaging
Lung cancer
Medical imaging
Medicine
Medicine & Public Health
Nuclear Medicine
Nuclear medicine imaging
Photon emission
Position emission tomography
Positron emission
Prediction models
Prognosis
Radiology
Radiomics
Review
Single photon emission computed tomography
State-of-the-art reviews
Tomography
Workflow
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Summary:Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.
ISSN:2197-7364
2197-7364
DOI:10.1186/s40658-024-00685-5