TY - GEN
T1 - Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images
AU - Zhao, Yu
AU - Gafita, Andrei
AU - Tetteh, Giles
AU - Haupt, Fabian
AU - Afshar-Oromieh, Ali
AU - Menze, Bjoern
AU - Eiber, Matthias
AU - Rominger, Axel
AU - Shi, Kuangyu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The emerging PSMA-targeted radionuclide therapy provides an effective method for the treatment of advanced metastatic prostate cancer. To optimize the therapeutic effect and maximize the theranostic benefit, there is a need to identify and quantify target lesions prior to treatment. However, this is extremely challenging considering that a high number of lesions of heterogeneous size and uptake may distribute in a variety of anatomical context with different backgrounds. This study proposes an end-to-end deep neural network to characterize the prostate cancer lesions on PSMA imaging automatically. A 68Ga-PSMA-11 PET/CT image dataset including 71 patients with metastatic prostate cancer was collected from three medical centres for training and evaluating the proposed network. For proof-of-concept, we focus on the detection of bone and lymph node lesions in the pelvic area suggestive for metastases of prostate cancer. The preliminary test on pelvic area confirms the potential of deep learning methods. Increasing the amount of training data may further enhance the performance of the proposed deep learning method.
AB - The emerging PSMA-targeted radionuclide therapy provides an effective method for the treatment of advanced metastatic prostate cancer. To optimize the therapeutic effect and maximize the theranostic benefit, there is a need to identify and quantify target lesions prior to treatment. However, this is extremely challenging considering that a high number of lesions of heterogeneous size and uptake may distribute in a variety of anatomical context with different backgrounds. This study proposes an end-to-end deep neural network to characterize the prostate cancer lesions on PSMA imaging automatically. A 68Ga-PSMA-11 PET/CT image dataset including 71 patients with metastatic prostate cancer was collected from three medical centres for training and evaluating the proposed network. For proof-of-concept, we focus on the detection of bone and lymph node lesions in the pelvic area suggestive for metastases of prostate cancer. The preliminary test on pelvic area confirms the potential of deep learning methods. Increasing the amount of training data may further enhance the performance of the proposed deep learning method.
UR - http://www.scopus.com/inward/record.url?scp=85077875820&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857955
DO - 10.1109/EMBC.2019.8857955
M3 - Conference contribution
C2 - 31946051
AN - SCOPUS:85077875820
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 951
EP - 954
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -
