Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT.
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...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 47; no. 3; pp. 603 - 614 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141578521&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141578521 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Mar2020 vid: 47 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141578521 10.1007/s00259-019-04606-y 141578521 ppf: 603 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT. aug: au: Zhao, Yu Gafita, Andrei Vollnberg, Bernd Tetteh, Giles Haupt, Fabian Afshar-Oromieh, Ali Menze, Bjoern Eiber, Matthias Rominger, Axel Shi, Kuangyu affil: Department of Informatics, Technische Universität München, Munich, Germany sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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