Abstract

The aim of this study was to evaluate the role of histopathological and biochemical parameters in the prediction of the presence and number of PSMA positive lesions consistent with the metastatic spread of prostate cancer on 68 Ga-PSMA PET images. Biochemical, histopathological and imaging data of 302 prostate cancer patients who underwent 68 Ga-PSMA-11 PET/CT or PET/MR imaging for primary staging were retrospectively analyzed. Patients were divided into two groups as "PET positive" and "PET negative" according to the presence of pathologic extraprostatic PSMA involvement. "PET positive" patients were additionally divided into two groups: oligometastatic (1-3 metastatic lesion) and multimetastatic (>3 metastatic lesions). The mean age of patients was 66.8 ± 7.6 years. Imaging modality was PET/MR in 223 (73.8%) and PET/CT in 79 (26.2%) of patients. Total PSA, PSA density (PSAD), ALP, and tumor ratio in biopsy specimens were found to be significantly higher in "PET positive" group compared to "PET negative" group and in multimetastatic group compared to oligometastatic group. PET positivity was observed in 3.8% of the low-intermediate risk groups (ISUP 1-3 and total PSA ≤ 20 ng/ml and PSAD < 0.15 ng/ml/cc). This ratio was 46% in the high-risk group (ISUP 4-5 or total PSA > 20 ng/ml or PSAD ≥ 0.15 ng/ml/cc) with a relative risk of 12 (p < .001). The prediction models to predict the PET positivity and the presence of distant metastasis had AUCs of 0.901 and 0.925, respectively; with ALP, total PSA, and tumor ratio in needle biopsy specimen as significant independent predictors (p < .05). In this study, 68 Ga-PSMA-11 PET positivity was significantly higher in the high-risk patient group than in the low-intermediate risk groups. The prediction models used for predicting the PET positivity and the presence of distant metastasis on PET imaging were successful with high discriminatory powers. In addition to total PSA and ISUP GG, ALP and tumor ratio in biopsy specimens can be used to identify high-risk patients.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.