ArticleJournal of applied clinical medical physics2022
Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology.Cancers · 2026Article
- Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.European journal of nuclear medicine and molecular imaging · 2025Article
- Peptide PET Imaging: A Review of Recent Developments and a Look at the Future of Radiometal-Labeled Peptides in Medicine.Chemical & biomedical imaging · 2024Review
- Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.Journal of applied clinical medical physics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
purposeTo investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy.
methodsThirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTV
resultsEntropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTV
conclusionsFDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.
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Registered trials
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