The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas.
Radiother Oncol
; 194: 110186, 2024 05.
Article
in En
| MEDLINE
| ID: mdl-38412906
ABSTRACT
BACKGROUND:
Accurate gross tumor volume (GTV) delineation is a critical step in radiation therapy treatment planning. However, it is reader dependent and thus susceptible to intra- and inter-reader variability. GTV delineation of soft tissue sarcoma (STS) often relies on CT and MR images.PURPOSE:
This study investigates the potential role of 18F-FDG PET in reducing intra- and inter-reader variability thereby improving reproducibility of GTV delineation in STS, without incurring additional costs or radiation exposure. MATERIALS ANDMETHODS:
Three readers performed independent GTV delineation of 61 patients with STS using first CT and MR followed by CT, MR, and 18F-FDG PET images. Each reader performed a total of six delineation trials, three trials per imaging modality group. Dice Similarity Coefficient (DSC) score and Hausdorff distance (HD) were used to assess both intra- and inter-reader variability using generated simultaneous truth and performance level estimation (STAPLE) GTVs as ground truth. Statistical analysis was performed using a Wilcoxon signed-ranked test.RESULTS:
There was a statistically significant decrease in both intra- and inter-reader variability in GTV delineation using CT, MR 18F-FDG PET images vs. CT and MR images. This was translated by an increase in the DSC score and a decrease in the HD for GTVs drawn from CT, MR and 18F-FDG PET images vs. GTVs drawn from CT and MR for all readers and across all three trials.CONCLUSION:
Incorporation of 18F-FDG PET into CT and MR images decreased intra- and inter-reader variability and subsequently increased reproducibility of GTV delineation in STS.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Sarcoma
/
Magnetic Resonance Imaging
/
Fluorodeoxyglucose F18
/
Positron-Emission Tomography
/
Tumor Burden
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Year:
2024
Type:
Article