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A multi-label CNN model for the automatic detection and segmentation of gliomas using [18F]FET PET imaging

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

The aim of this study was to develop a convolutional neural network (CNN) for the automatic detection and segmentation of gliomas using [18F]fluoroethyl-L-tyrosine ([18F]FET) PET.

Methods

Ninety-three patients (84 in-house/7 external) who underwent a 20–40-min static [18F]FET PET scan were retrospectively included. Lesions and background regions were defined by two nuclear medicine physicians using the MIM software, such that delineations by one expert reader served as ground truth for training and testing the CNN model, while delineations by the second expert reader were used to evaluate inter-reader agreement. A multi-label CNN was developed to segment the lesion and background region while a single-label CNN was implemented for a lesion-only segmentation. Lesion detectability was evaluated by classifying [18F]FET PET scans as negative when no tumor was segmented and vice versa, while segmentation performance was assessed using the dice similarity coefficient (DSC) and segmented tumor volume. The quantitative accuracy was evaluated using the maximal and mean tumor to mean background uptake ratio (TBRmax/TBRmean). CNN models were trained and tested by a threefold cross-validation (CV) using the in-house data, while the external data was used for an independent evaluation to assess the generalizability of the two CNN models.

Results

Based on the threefold CV, the multi-label CNN model achieved 88.9% sensitivity and 96.5% precision for discriminating between positive and negative [18F]FET PET scans compared to a 35.3% sensitivity and 83.1% precision obtained with the single-label CNN model. In addition, the multi-label CNN allowed an accurate estimation of the maximal/mean lesion and mean background uptake, resulting in an accurate TBRmax/TBRmean estimation compared to a semi-automatic approach. In terms of lesion segmentation, the multi-label CNN model (DSC = 74.6 ± 23.1%) demonstrated equal performance as the single-label CNN model (DSC = 73.7 ± 23.2%) with tumor volumes estimated by the single-label and multi-label model (22.9 ± 23.6 ml and 23.1 ± 24.3 ml, respectively) closely approximating the tumor volumes estimated by the expert reader (24.1 ± 24.4 ml). DSCs of both CNN models were in line with the DSCs by the second expert reader compared with the lesion segmentations by the first expert reader, while detection and segmentation performance of both CNN models as determined with the in-house data were confirmed by the independent evaluation using external data.

Conclusion

The proposed multi-label CNN model detected positive [18F]FET PET scans with high sensitivity and precision. Once detected, an accurate tumor segmentation and estimation of background activity was achieved resulting in an automatic and accurate TBRmax/TBRmean estimation, such that user interaction and potential inter-reader variability can be minimized.

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Abbreviations

BGmean :

Mean background uptake

CNN:

Convolutional neural network

DSC:

Dice similarity coefficient

3D:

Three-dimensional

[18F]FET :

[18F]fluoroethyl-L-tyrosine

[18F]FDG:

[18F]fluoro-deoxy-glucose

IDH1:

Isocitrate dehydrogenase 1

LAT2:

L-type amino acid transporter 2

Lesionmax :

Maximal lesion uptake

MTV:

Metabolic tumor volume

OSEM:

Ordered-subset expectation maximization

PSF:

Point spread function modeling

ReLU:

Rectified linear unit

TAC:

Time activity curve

TBR:

Tumor to background ratio

TTM:

Total tumor metabolism

TTP:

Time to peak

U-Nettumor :

U-Net for tumor segmentation

U-Nettumor ,BG :

U-Net for tumor and background segmentation

VOI:

Volume of interest

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Acknowledgements

The authors would like to thank NVIDIA Corporation for donating a Titan X GPU.

Funding

This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant 764458. The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government.

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Authors and Affiliations

  1. Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, UZ, KU Leuven, Herestraat 49 - Box 7003, 3000, Leuven, Belgium

    Masoomeh Rahimpour, Karolien Goffin & Michel Koole

  2. Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands

    Ronald Boellaard

  3. Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium

    Sander Jentjens, Wies Deckers & Karolien Goffin

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  1. Masoomeh Rahimpour
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Correspondence to Masoomeh Rahimpour.

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Ethical approval

The study was approved by the local research ethics committee of UZ/KU Leuven (EC Research – study number: S63157). The external data are from the study on quantifying [18F]FET PET kinetics in gliomas (registered in the Netherlands National Trial Register (www.trialregister.nl), unique identifier NTR5354, registration date 4th of August 2015).

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Rahimpour, M., Boellaard, R., Jentjens, S. et al. A multi-label CNN model for the automatic detection and segmentation of gliomas using [18F]FET PET imaging. Eur J Nucl Med Mol Imaging 50, 2441–2452 (2023). https://doi.org/10.1007/s00259-023-06193-5

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