| Literature DB >> 35022844 |
Sofia C Vaz1,2, Judit A Adam3, Roberto C Delgado Bolton4, Pierre Vera5, Wouter van Elmpt6, Ken Herrmann7, Rodney J Hicks8, Yolande Lievens9, Andrea Santos10, Heiko Schöder11, Bernard Dubray12,13, Dimitris Visvikis14, Esther G C Troost15,16,17,18,19, Lioe-Fee de Geus-Oei2.
Abstract
PURPOSE: 2-[18F]FDG PET/CT is of utmost importance for radiation treatment (RT) planning and response monitoring in lung cancer patients, in both non-small and small cell lung cancer (NSCLC and SCLC). This topic has been addressed in guidelines composed by experts within the field of radiation oncology. However, up to present, there is no procedural guideline on this subject, with involvement of the nuclear medicine societies.Entities:
Keywords: CT; ESTRO, 2-[18F]FDG PET; Lung cancer; Planning; Radiation therapy; Radiotherapy, EANM; SNMMI
Mesh:
Substances:
Year: 2022 PMID: 35022844 PMCID: PMC8921015 DOI: 10.1007/s00259-021-05624-5
Source DB: PubMed Journal: Eur J Nucl Med Mol Imaging ISSN: 1619-7070 Impact factor: 10.057
Figure. 1GTV, CTV, PTV, and ITV schematic definitions (based on the International Commission on Radiation Units and Measurements, report 62 - ICRU-62)
2-[18F]FDG PET metrics and segmentation methods summary
| Segmentation methods | |
|---|---|
| Manual method | • Manual delineation slice by slice |
| Threshold-based | • SUVmax > 2.5 • >40% SUVmax within the lesion |
| Advanced image segmentation approaches | • Gradient-based • Hybrid • Deformable contours • Model-based • Statistical • Multimodality-based • Machine learning/deep learning |
| Consensus algorithms | • Majority vote (MJV) • Simultaneous Truth and Performance Level Estimate (STAPLE) • Automatic decision Tree-based Learning Algorithm for Advanced Segmentation (ATLAAS) |