TY - JOUR
T1 - 68Ga-PSMA-617 PET/CT
T2 - a promising new technique for predicting risk stratification and metastatic risk of prostate cancer patients
AU - Liu, Chen
AU - Liu, Teli
AU - Zhang, Ning
AU - Liu, Yiqiang
AU - Li, Nan
AU - Du, Peng
AU - Yang, Yong
AU - Liu, Ming
AU - Gong, Kan
AU - Yang, Xing
AU - Zhu, Hua
AU - Yan, Kun
AU - Yang, Zhi
N1 - Funding Information:
Funding This study was funded by the National Natural Science Foundation of China projects (81571705), Natural Science Foundation of Beijing Municipality (7171002), Beijing Municipal Commission of Health and Family Planning (2015-3-072), Beijing Nova Program (Z171100001117020), and Interdisciplinary Medicine Seed Fund of Peking University (BMU2017MX007).
Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Purpose: The purpose of this study was to investigate the performance of 68Ga-PSMA-617 PET/CT in predicting risk stratification and metastatic risk of prostate cancer. Methods: Fifty newly diagnosed patients with prostate cancer as confirmed by needle biopsy were continuously included, 40 in a train set and ten in a test set. 68Ga-PSMA-617 PET/CT and clinical data of all patients were retrospectively analyzed. Semi-quantitative analysis of PET images provided maximum standardized uptake (SUVmax) of primary prostate cancer and volumetric parameters including intraprostatic PSMA-derived tumor volume (iPSMA-TV) and intraprostatic total lesion PSMA (iTL-PSMA). According to prostate cancer risk stratification criteria of the NCCN Guideline, all patients were simplified into a low-intermediate risk group or a high-risk group. The semi-quantitative parameters of 68Ga-PSMA-617 PET/CT were used to establish a univariate logistic regression model for high-risk prostate cancer and its metastatic risk, and to evaluate the diagnostic efficacy of the predictive model. Results: In the train set, 30/40 (75%) patients had high-risk prostate cancer and 10/40 (25%) patients had low-to-moderate-risk prostate cancer; in the test set, 8/10 (80%) patients had high-risk prostate cancer while 2/10 (20%) had low-intermediate risk prostate cancer. The univariate logistic regression model established with SUVmax, iPSMA-TV and iTL-PSMA could all effectively predict high-risk prostate cancer; the AUC of ROC were 0.843, 0.802 and 0.900, respectively. Based on the test set, the sensitivity and specificity of each model were 87.5% and 50% for SUVmax, 62.5% and 100% for iPSMA-TV, and 87.5% and 100% for iTL-PSMA, respectively. The iPSMA-TV and iTL-PSMA-based predictive model could predict the metastatic risk of prostate cancer, the AUC of ROC was 0.863 and 0.848, respectively, but the SUVmax-based prediction model could not predict metastatic risk. Conclusions: Semi-quantitative analysis indexes of 68Ga-PSMA-617 PET/CT imaging can be used as “imaging biomarkers” to predict risk stratification and metastatic risk of prostate cancer.
AB - Purpose: The purpose of this study was to investigate the performance of 68Ga-PSMA-617 PET/CT in predicting risk stratification and metastatic risk of prostate cancer. Methods: Fifty newly diagnosed patients with prostate cancer as confirmed by needle biopsy were continuously included, 40 in a train set and ten in a test set. 68Ga-PSMA-617 PET/CT and clinical data of all patients were retrospectively analyzed. Semi-quantitative analysis of PET images provided maximum standardized uptake (SUVmax) of primary prostate cancer and volumetric parameters including intraprostatic PSMA-derived tumor volume (iPSMA-TV) and intraprostatic total lesion PSMA (iTL-PSMA). According to prostate cancer risk stratification criteria of the NCCN Guideline, all patients were simplified into a low-intermediate risk group or a high-risk group. The semi-quantitative parameters of 68Ga-PSMA-617 PET/CT were used to establish a univariate logistic regression model for high-risk prostate cancer and its metastatic risk, and to evaluate the diagnostic efficacy of the predictive model. Results: In the train set, 30/40 (75%) patients had high-risk prostate cancer and 10/40 (25%) patients had low-to-moderate-risk prostate cancer; in the test set, 8/10 (80%) patients had high-risk prostate cancer while 2/10 (20%) had low-intermediate risk prostate cancer. The univariate logistic regression model established with SUVmax, iPSMA-TV and iTL-PSMA could all effectively predict high-risk prostate cancer; the AUC of ROC were 0.843, 0.802 and 0.900, respectively. Based on the test set, the sensitivity and specificity of each model were 87.5% and 50% for SUVmax, 62.5% and 100% for iPSMA-TV, and 87.5% and 100% for iTL-PSMA, respectively. The iPSMA-TV and iTL-PSMA-based predictive model could predict the metastatic risk of prostate cancer, the AUC of ROC was 0.863 and 0.848, respectively, but the SUVmax-based prediction model could not predict metastatic risk. Conclusions: Semi-quantitative analysis indexes of 68Ga-PSMA-617 PET/CT imaging can be used as “imaging biomarkers” to predict risk stratification and metastatic risk of prostate cancer.
KW - PET/CT
KW - PSMA
KW - Prostate caner
KW - Volume-based parameters
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U2 - 10.1007/s00259-018-4037-9
DO - 10.1007/s00259-018-4037-9
M3 - Article
C2 - 29717333
AN - SCOPUS:85046131686
SN - 1619-7070
VL - 45
SP - 1852
EP - 1861
JO - European Journal of Nuclear Medicine and Molecular Imaging
JF - European Journal of Nuclear Medicine and Molecular Imaging
IS - 11
ER -