[F18] FDG-PET/CT for manual or semiautomated GTV delineation of the primary tumor for radiation therapy planning in patients with esophageal cancer: is it useful?

Author:

Walter FranziskaORCID,Jell Constanze,Zollner Barbara,Andrae Claudia,Gerum Sabine,Ilhan Harun,Belka Claus,Niyazi Maximilian,Roeder Falk

Abstract

Abstract Background Target volume definition of the primary tumor in esophageal cancer is usually based on computed tomography (CT) supported by endoscopy and/or endoscopic ultrasound and can be difficult given the low soft-tissue contrast of CT resulting in large interobserver variability. We evaluated the value of a dedicated planning [F18] FDG-Positron emission tomography/computer tomography (PET/CT) for harmonization of gross tumor volume (GTV) delineation and the feasibility of semiautomated structures for planning purposes in a large cohort. Methods Patients receiving a dedicated planning [F18] FDG-PET/CT (06/2011–03/2016) were included. GTV was delineated on CT and on PET/CT (GTVCT and GTVPET/CT, respectively) by three independent radiation oncologists. Interobserver variability was evaluated by comparison of mean GTV and mean tumor lengths, and via Sørensen–Dice coefficients (DSC) for spatial overlap. Semiautomated volumes were constructed based on PET/CT using fixed standardized uptake values (SUV) thresholds (SUV30, 35, and 40) or background- and metabolically corrected PERCIST-TLG and Schaefer algorithms, and compared to manually delineated volumes. Results 45 cases were evaluated. Mean GTVCT and GTVPET/CT were 59.2/58.0 ml, 65.4/64.1 ml, and 60.4/59.2 ml for observers A–C. No significant difference between CT- and PET/CT-based delineation was found comparing the mean volumes or lengths. Mean Dice coefficients on CT and PET/CT were 0.79/0.77, 0.81/0.78, and 0.8/0.78 for observer pairs AB, AC, and BC, respectively, with no significant differences. Mean GTV volumes delineated semiautomatically with SUV30/SUV35/SUV40/Schaefer’s and PERCIST-TLG threshold were 69.1/23.9/18.8/18.6 and 70.9 ml. The best concordance of a semiautomatically delineated structure with the manually delineated GTVCT/GTVPET/CT was observed for PERCIST-TLG. Conclusion We were not able to show that the integration of PET/CT for GTV delineation of the primary tumor resulted in reduced interobserver variability. The PERCIST-TLG algorithm seemed most promising compared to other thresholds for further evaluation of semiautomated delineation of esophageal cancer.

Funder

Universitätsklinik München

Publisher

Springer Science and Business Media LLC

Subject

Oncology,Radiology Nuclear Medicine and imaging

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study;International Journal of Radiation Oncology*Biology*Physics;2024-08

2. Esophageal Cancer;Target Volume Definition in Radiation Oncology;2023

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