当前位置： SCI文献检索 > EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING期刊下所有文献 > Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT.

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

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.

journal_name

Eur J Nucl Med Mol Imaging

journal_title

European journal of nuclear medicine and molecular imaging

authors

Zhao Y,Gafita A,Vollnberg B,Tetteh G,Haupt F,Afshar-Oromieh A,Menze B,Eiber M,Rominger A,Shi K

doi

10.1007/s00259-019-04606-y

subject

Has Abstract

pub_date

2020-03-01 00:00:00

pages

603-613

issue

3

eissn

1619-7070

issn

1619-7089

pii

10.1007/s00259-019-04606-y

journal_volume

47

pub_type

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