Feasibility of different lymph node metastases delineation approaches in [18F]SiTATE PET/CT imaging in NET patients
Year: 2025
Type: article
Abstract: Background Accurate tumor delineation in somatostatin receptor (SSTR) PET/CT is imperative for quantifying tumor burden, therapy monitoring, and response assessment in gastroenteropancreatic neuroendocrine tumors (GEPNETs). This study aimed to evaluate different SSTR PET-based tumor delineation approaches, based on a lymph node metastasis as reference standard, and determine the most robust method for volume assessment. Singular, non-bulky lymph node metastases with a short axis diameter ≥ 1.0 cm from [¹⁸F]SiTATE PET/CT scans were included. Different tumor delineation methods were applied: fixed SUV thresholds, isocontours relative to SUV max , and individual backwards thresholding. The correlation between PET-derived volumes and CT-derived volumes (reference standard) were investigated. Results An isocontour approach using 45% of SUV max demonstrated the strongest correlation with CT-derived volumes ( r = 0.874; r² = 0.764; p < 0.001), outperforming fixed SUV thresholds, e.g. SUV 4.0 ( r = 0.727; r 2 = 0.529; p < 0.001). The application of a backwards threshold approach resulted in the identification of significant variability (CoV: 59.6% for fixed SUV, 31.3% for isocontour). Conclusion The 45% isocontour-thresholding relative to lesional SUV max approach constitutes a robust and clinically applicable method for SSTR PET-based tumor delineation in GEPNET patients, irrespective of changes of SSTR avidity of reference tissues (e.g. liver). Further validation is required to establish its role in total tumor volume assessment and therapy monitoring.
Source: EJNMMI Reports
Authors: Sophie Carina Siegmund, Magdalena Schöll, Vera U. Wenter, Gabriel T. Sheikh, Maximilian Scheifele, Franz Josef Gildehaus, Simon Lindner, Lalith K. Shiyam Sundar, Christine Spitzweg, Christoph J. Auernhammer, Rudolf A. Werner, Mathias J. Zacherl
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W4415678624
