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Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging 3/2020

07.12.2019 | Original Article

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

verfasst von: Yu Zhao, Andrei Gafita, Bernd Vollnberg, Giles Tetteh, Fabian Haupt, Ali Afshar-Oromieh, Bjoern Menze, Matthias Eiber, Axel Rominger, Kuangyu Shi

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 3/2020

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Abstract

Purpose

This study proposes an automated prostate cancer (PC) lesion characterization method based on the deep neural network to determine tumor burden on 68Ga-PSMA-11 PET/CT to potentially facilitate the optimization of PSMA-directed radionuclide therapy.

Methods

We collected 68Ga-PSMA-11 PET/CT images from 193 patients with metastatic PC at three medical centers. For proof-of-concept, we focused on the detection of pelvis bone and lymph node lesions. A deep neural network (triple-combining 2.5D U-Net) was developed for the automated characterization of these lesions. The proposed method simultaneously extracts features from axial, coronal, and sagittal planes, which mimics the workflow of physicians and reduces computational and memory requirements.

Results

Among all the labeled lesions, the network achieved 99% precision, 99% recall, and an F1 score of 99% on bone lesion detection and 94%, precision 89% recall, and an F1 score of 92% on lymph node lesion detection. The segmentation accuracy is lower than the detection. The performance of the network was correlated with the amount of training data.

Conclusion

We developed a deep neural network to characterize automatically the PC lesions on 68Ga-PSMA-11 PET/CT. The preliminary test within the pelvic area confirms the potential of deep learning methods. Increasing the amount of training data should further enhance the performance of the proposed method and may ultimately allow whole-body assessments.
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Metadaten
Titel
Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT
verfasst von
Yu Zhao
Andrei Gafita
Bernd Vollnberg
Giles Tetteh
Fabian Haupt
Ali Afshar-Oromieh
Bjoern Menze
Matthias Eiber
Axel Rominger
Kuangyu Shi
Publikationsdatum
07.12.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 3/2020
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-019-04606-y

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