Prostate-specific membrane antigen (PSMA) PET has a higher accuracy than CT and bone scans to stage patients with prostate cancer. We do not understand how to apply clinical trial data based on conventional imaging to patients staged using PSMA PET. Therefore, we aimed to evaluate the ability of bone scans to detect osseous metastases using PSMA PET as a reference standard. Methods: In this multicenter retrospective diagnostic study, 167 patients with prostate cancer, who were imaged with bone scans and PSMA PET performed within 100 d, were included for analysis. Each study was interpreted by 3 masked readers, and the results of the PSMA PET were used as the reference standard. Endpoints were positive predictive value (PPV), negative predictive value (NPV), and specificity for bone scans. Additionally, interreader reproducibility, positivity rate, uptake on PSMA PET, and the number of lesions were evaluated. Results: In total, 167 patients were included, with 77 at initial staging, 60 in the biochemical recurrence and castration-sensitive prostate cancer setting, and 30 in the castration-resistant prostate cancer setting. In all patients, the PPV, NPV, and specificity for bone scans were 0.73 (95% CI, 0.61-0.82), 0.82 (95% CI, 0.74-0.88), and 0.82 (95% CI, 0.74-0.88), respectively. In patients at initial staging, the PPV, NPV, and specificity for bone scans were 0.43 (95% CI, 0.26-0.63), 0.94 (95% CI, 0.85-0.98), and 0.80 (95% CI, 0.68-0.88), respectively. Interreader agreement for bone disease was moderate for bone scans (Fleiss κ, 0.51) and substantial for the PSMA PET reference standard (Fleiss κ, 0.80). Conclusion: In this multicenter retrospective study, the PPV of bone scans was low in patients at initial staging, with 57% of positive bone scans being false positives. This suggests that a large proportion of patients considered low-volume metastatic by the bone scan actually had localized disease, which is critical when applying clinical data from trials such as the STAMPEDE M1 radiation therapy trial to patients being staged with PSMA PET.
A methodology for determining tau PET thresholds is needed to confidently detect early tau deposition. We compared multiple threshold-determining methods in participants who underwent either 18F-flortaucipir or 18F-MK-6240 PET scans. Methods: 18F-flortaucipir (n = 798) and 18F-MK-6240 (n = 216) scans were processed and sampled to obtain regional SUV ratios. Subsamples of the cohorts were based on participant diagnosis, age, amyloid-β status (positive or negative), and neurodegeneration status (positive or negative), creating older-adult (age ≥ 55 y) cognitively unimpaired (amyloid-β-negative, neurodegeneration-negative) and cognitively impaired (mild cognitive impairment/Alzheimer disease, amyloid-β-positive, neurodegeneration-positive) groups, and then were further subsampled via matching to reduce significant differences in diagnostic prevalence, age, and Mini-Mental State Examination score. We used the biostatistical estimation of tau threshold hallmarks (BETTH) algorithm to determine sensitivity and specificity in 6 composite regions. Results: Parametric double receiver operating characteristic analysis yielded the greatest joint sensitivity in 5 of the 6 regions, whereas hierarchic clustering, gaussian mixture modeling, and k-means clustering all yielded perfect joint specificity (2.00) in all regions. Conclusion: When 18F-flortaucipir and 18F-MK-6240 are used, Alzheimer disease-related tau status is best assessed using 2 thresholds, a sensitivity one based on parametric double receiver operating characteristic analysis and a specificity one based on gaussian mixture modeling, delimiting an uncertainty zone indicating participants who may require further evaluation.
Our objective was to evaluate the prognostic value of end-of-treatment prostate-specific membrane antigen (PSMA) PET/CT (PSMA-PET) in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA radioligand therapy (PSMA-RLT). Methods: This was a single-center retrospective study. mCRPC patients who underwent PSMA-RLT with available baseline PSMA-PET (bPET) and end-of-treatment PSMA-PET (ePET) within 6 mo of the last PSMA-RLT cycle were eligible. Overall survival (OS) and prostate-specific antigen (PSA) progression status at the time of ePET (by Prostate Cancer Clinical Trials Working Group 3 criteria) were collected. PSMA-PET tumor segmentation was performed to obtain whole-body PSMA tumor volume (PSMA-VOL) and define progressive (≥20% increase) versus nonprogressive disease. Pairs of bPET and ePET were interpreted for appearance of new lesions. Response Evaluation Criteria in PSMA-PET/CT (RECIP) 1.0 were also applied to define progressive versus nonprogressive disease. The associations between changes in PSMA-VOL, new lesions, RECIP 1.0, and PSA progression status at the time of ePET with OS were evaluated by Kaplan-Meier analysis. Results: Twenty mCRPC patients were included. The median number of treatment cycles was 3.5 (interquartile range [IQR], 2-4). The median time between bPET and cycle 1 of PSMA-RLT was 1.0 mo (IQR, 0.7-1.8 mo). The median time between the last cycle of PSMA-RLT and ePET was 1.9 mo (IQR, 1.2-3.5 mo). Twelve of 20 patients (60%) had died at the last follow-up. The median follow-up time from ePET for survivors was 31.2 mo (IQR, 6.8-40.7 mo). The median OS from ePET was 11.4 mo (IQR, 6.8-30.7 mo). Patients with new lesions on ePET had shorter OS than those without new lesions (median OS, 10.7 mo [95% CI, 9.2-12.2] vs. not reached; P = 0.002). Patients with progressive PSMA-VOL had shorter OS than those with nonprogressive PSMA-VOL (median OS, 10.7 mo [95% CI: 9.7-11.7 mo] vs. not reached; P = 0.007). Patients with progressive RECIP had shorter OS than those with nonprogressive RECIP (median OS, 10.7 mo [95% CI, 9.7-11.7 mo] vs. not reached; P = 0.007). PSA progression at the time of ePET was associated with shorter OS (median, 10.9 mo [95% CI, 9.4-12.4 mo] vs. not reached; P = 0.028). Conclusion: In this retrospective study of 20 mCRPC patients treated with PSMA-RLT, progression on ePET by the appearance of new lesions, changes in PSMA-VOL, and RECIP 1.0 was prognostic for OS. Validation in larger, prospective multicentric clinical trials is warranted.
Evaluation of metabolic tumor volume (MTV) changes using amino acid PET has become an important tool for response assessment in brain tumor patients. MTV is usually determined by manual or semiautomatic delineation, which is laborious and may be prone to intra- and interobserver variability. The goal of our study was to develop a method for automated MTV segmentation and to evaluate its performance for response assessment in patients with gliomas. Methods: In total, 699 amino acid PET scans using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) from 555 brain tumor patients at initial diagnosis or during follow-up were retrospectively evaluated (mainly glioma patients, 76%). 18F-FET PET MTVs were segmented semiautomatically by experienced readers. An artificial neural network (no new U-Net) was configured on 476 scans from 399 patients, and the network performance was evaluated on a test dataset including 223 scans from 156 patients. Surface and volumetric Dice similarity coefficients (DSCs) were used to evaluate segmentation quality. Finally, the network was applied to a recently published 18F-FET PET study on response assessment in glioblastoma patients treated with adjuvant temozolomide chemotherapy for a fully automated response assessment in comparison to an experienced physician. Results: In the test dataset, 92% of lesions with increased uptake (n = 189) and 85% of lesions with iso- or hypometabolic uptake (n = 33) were correctly identified (F1 score, 92%). Single lesions with a contiguous uptake had the highest DSC, followed by lesions with heterogeneous, noncontiguous uptake and multifocal lesions (surface DSC: 0.96, 0.93, and 0.81 respectively; volume DSC: 0.83, 0.77, and 0.67, respectively). Change in MTV, as detected by the automated segmentation, was a significant determinant of disease-free and overall survival, in agreement with the physician's assessment. Conclusion: Our deep learning-based 18F-FET PET segmentation allows reliable, robust, and fully automated evaluation of MTV in brain tumor patients and demonstrates clinical value for automated response assessment.
Our previous study found that the prostate-specific membrane antigen (PSMA) PET/CT response of primary prostate cancer (PCa) to neoadjuvant therapy can predict the pathologic response. This study was designed to investigate the association between [68Ga]PSMA PET/CT changes and biochemical progression-free survival (bPFS) in high-risk patients who underwent neoadjuvant therapy before radical prostatectomy (RP). Methods: Seventy-five patients with high-risk PCa in 2 phase II clinical trials who received neoadjuvant therapy before RP were included. The patients received androgen deprivation therapy plus docetaxel (n = 33) or androgen deprivation therapy plus abiraterone (n = 42) as neoadjuvant treatment. All patients had serial [68Ga]PSMA PET/CT scans before and after neoadjuvant therapy. Age, initial prostate-specific antigen level, nadir prostate-specific antigen level before RP, tumor grade at biopsy, treatment regimen, clinical T stage, PET imaging features, pathologic N stage, and pathologic response on final pathology were included for univariate and multivariate Cox regression analyses to identify independent predictors of bPFS. Results: With a median follow-up of 30 mo, 18 patients (24%) experienced biochemical progression. Multivariate Cox regression analyses revealed that only SUVmax derived from posttreatment [68Ga]PSMA PET/CT and pathologic response on final pathology were independent factors for the prediction of bPFS, with hazard ratios of 1.02 (95% CI, 1.00-1.04; P = 0.02) and 0.12 (95% CI, 0.02-0.98; P = 0.048), respectively. Kaplan-Meier analysis revealed that patients with a favorable [68Ga]PSMA PET/CT response (posttreatment SUVmax < 8.5) or a favorable pathologic response (pathologic complete response or minimal residual disease) had a significantly lower rate of 3-y biochemical progression. Conclusion: Our results indicated that [68Ga]PSMA PET/CT response was an independent risk factor for the prediction of bPFS in patients with high-risk PCa receiving neoadjuvant therapy and RP, suggesting [68Ga]PSMA PET/CT to be an ideal tool to monitor response to neoadjuvant therapy.
We report the dosimetric evaluation of prostate-specific membrane antigen-based radioligand therapy (RLT) for metastatic prostate cancer in a patient with autosomal-dominant polycystic kidney disease. Methods: The patient received hemodialysis during each of 6 RLT cycles while staying as an inpatient. We used voxel dosimetry and blood sampling for the dose calculation. Results: The patient responded well to the RLT, as indicated by the prostate-specific antigen level decreasing from 298 to 7.1 ng/mL. The doses per cycle ranged from 0.19 to 0.4 Gy/GBq for the parotid gland, 0.14 to 0.28 Gy/GBq for the submandibular gland, 0.03 to 0.11 Gy/GBq per kidney, and 0.10 to 0.15 Gy/GBq for the red bone marrow. Conclusion: This case suggests that [177Lu]Lu-PSMA-based RLT can be applied successfully and safely to a patient with chronic kidney disease undergoing hemodialysis.
Dosimetry after 177Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) enables estimation of radiation doses absorbed by normal organs and target lesions. This process is time-consuming and requires multiple posttreatment studies on several subsequent days. In a previous study, we described a newly developed multiple-linear-regression model to predict absorbed doses (ADs) from a single-time-point (STP) posttreatment study acquired 168 h after the first infusion and 24 h after the following ones, with similar results to the standard multiple-time-point (MTP) protocol. The present study aimed to validate this model in a large patient cohort and to assess whether STP dosimetry affects patient management decisions compared with our MTP protocol. Methods: Quantitative 177Lu-DOTATATE SPECT/CT post-PRRT data from 159 consecutive patients (172 therapies, 477 therapy cycles) were retrospectively analyzed. ADs obtained from an STP model were compared with those obtained using an MTP model. We evaluated the impact of the STP model on the decision on whether PRRT should be stopped because of an expected kidney AD exceeding the safety threshold. We hypothesized that patient management based on the STP model does not differ from that based on the MTP model in at least 90% of the cases. Results: There was no difference in management decisions between the MTP and STP models in 170 of 172 therapies (98.8%). A Fisher χ2 test for combined probabilities produced a composite P value of 0.0003. Mean cumulative AD relative differences between the STP and MTP models were 0.8% ± 8.0%, -7.7% ± 4.8%, 0.0% ± 11.4%, -2.8% ± 6.3%, and -2.1% ± 18.4% for kidneys, bone marrow, liver, spleen, and tumors, respectively (Pearson r = 0.99 for all), for patients who underwent 4 therapy cycles. Similar results were obtained with fewer therapy cycles. Conclusion: Estimated radiation ADs and patient management decisions were similar with the STP and MTP models. The STP model can simplify the dosimetry process while also reducing scanner and staff time and improving patient comfort.
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