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Proceedings Paper

Automated threshold selection on whole-body 18F-FDG PET/CT for assessing tumor metabolic response
Author(s): Ine Dirks; Marleen Keyaerts; Bart Neyns; Jef Vandemeulebroucke
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Paper Abstract

PET/CT is widely used in oncology. Yet the identification of lesions, as described by the PET response criteria in solid tumors (PERCIST), still relies on manual identification of a volume of interest (VOI), typically in the liver, for determining the optimal threshold. The process requires expert knowledge and is prone to errors and inter-observer variability. A fully automated procedure for the application of the PERCIST criteria for whole- body images is proposed. The method relies on automated localization of the liver on whole-body CT using a dense V-net trained on large field-of-view images. Inside the liver, a spherical VOI is determined which exhibits the lowest product of the coefficients of variation (defined as the standard deviation over the mean) in PET and CT. The liver segmentation achieved a median dice score of 0.87 ± 0.12 in 10-fold cross-validation, which proved to be sufficient for reliable identification of a VOI. The full pipeline was evaluated on an external PET/CT dataset of 18 patients. To assess reproducibility, geometric and intensity variations were applied, simulating potential image differences when scanning the same person under different conditions. The variability of the resulting threshold was evaluated and compared to the manual approach performed by three observers. The proposed method exhibited superior reproducibility with a mean threshold of 4.01 ± 0.02 SUVbw, compared to 4.11 ± 0.16 SUVbw for the manual method. The automated procedure renders the analysis of large amounts of PET/CT data feasible or could be used to detect anomalies in the manual approach.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131R (10 March 2020); doi: 10.1117/12.2549796
Show Author Affiliations
Ine Dirks, Vrije Univ. Brussel (Belgium)
IMEC (Belgium)
Marleen Keyaerts, Vrije Univ. Brussel (Belgium)
Univ. Ziekenhuis Brussel (Belgium)
Bart Neyns, Vrije Univ. Brussel (Belgium)
Univ. Ziekenhuis Brussel (Belgium)
Jef Vandemeulebroucke, Vrije Univ. Brussel (Belgium)
IMEC (Belgium)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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