Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics.

BACKGROUND AND OBJECTIVE: Treatment planning through the diagnostic dimension of theranostics provides insights into predicting the absorbed dose of RPT, with the potential to individualize radiation doses for enhancing treatment efficacy. However, existing studies focusing on dose prediction from diagnostic data often rely on organ-level estimations, overlooking intra-organ variations. This study aims to characterize the intra-organ theranostic heterogeneity and utilize artificial intelligence techniques to localize them, i.e. to predict voxel-wise absorbed dose map based on pre-therapy PET. METHODS: 23 patients with metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA I&T RPT were retrospectively included. 48 treatment cycles with pre-treatment PET imaging and at least 3 post-therapeutic SPECT/CT imaging were selected. The distribution of PET tracer and RPT dose was compared for kidney, liver and spleen, characterizing intra-organ heterogeneity differences. Pharmacokinetic simulations were performed to enhance the understanding of the correlation. Two strategies were explored for pre-therapy voxel-wise dosimetry prediction: (1) organ-dose guided direct projection; (2) deep learning (DL)-based distribution prediction. Physical metrics, dose volume histogram (DVH) analysis, and identity plots were applied to investigate the predicted absorbed dose map. RESULTS: Inconsistent intra-organ patterns emerged between PET imaging and dose map, with moderate correlations existing in the kidney (r = 0.77), liver (r = 0.5), and spleen (r = 0.58) (P < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R2 = 0.92 for kidney). The DL model improved the mean slope of fitting lines in identity plots (199% for liver), when compared to the theoretical optimal results of the organ-dose approach. CONCLUSION: Our results demonstrated the intra-organ heterogeneity of pharmacokinetics may complicate pre-therapy dosimetry prediction. DL has the potential to bridge this gap for pre-therapy prediction of voxel-wise heterogeneous dose map.

Response Evaluation Criteria in PSMA PET/CT (RECIP 1.0) in Metastatic Castration-resistant Prostate Cancer.

Background Response Evaluation Criteria in Prostate-specific Membrane Antigen (PSMA) PET/CT (RECIP 1.0) initially integrated software-based quantitative assessment of PSMA-positive total tumor volume (TTV). Clinical implementation of such software is not expected soon, limiting the use of RECIP in practice. Purpose To assess the agreement of RECIP determined using tumor segmentation software (quantitative RECIP) with RECIP determined by qualitative reads by nuclear medicine physicians (visual RECIP) for response evaluation in metastatic castration-resistant prostate cancer. Materials and Methods This multicenter retrospective study at three academic centers included men who received lutetium 177 (177Lu) PSMA treatment between December 2014 and July 2019. PSMA PET/CT images at baseline and 12 weeks were assessed qualitatively by five readers for changes in TTV and for new lesions. Quantitative changes in TTV were also measured using tumor segmentation software. The status of new lesions was combined with qualitative changes in TTV to determine visual RECIP and with quantitative changes in TTV to determine quantitative RECIP. The primary outcomes were the agreement between visual and quantitative RECIP and the interreader reliability of visual RECIP according to the Fleiss κ. The secondary outcome was the association of visual RECIP with overall survival according to Cox regression. Results A total of 124 men (median age, 73 years [IQR, 67-76 years]) were included. Forty (32%) and 84 (68%) men had quantitative RECIP progressive disease (PD) and non-PD, respectively. Agreement between visual versus quantitative RECIP was excellent (κ = 0.89; 118 of 124 men [95%]). Agreement among readers in classifying visual RECIP PD versus non-PD was excellent (κ = 0.81; 103 of 124 men [83%]). RECIP PD was associated with significantly shorter overall survival compared with non-PD (hazard ratio, 2.6 [95% CI: 1.7, 3.8]; P < .001). Conclusion Qualitatively assessed RECIP demonstrated excellent agreement with quantitative RECIP and excellent interreader reliability and can be readily implemented in clinical practice for response evaluation in men with metastatic castration-resistant prostate cancer undergoing 177Lu-PSMA therapy. © RSNA, 2023 Supplemental material is available for this article.

Comparative Analysis of Morphological and Functional Effects of 225Ac- and 177Lu-PSMA Radioligand Therapies (RLTs) on Salivary Glands.

Most Prostate Specific Membrane Antigens (PSMAs) targeting small molecules accumulate in the salivary glands (SGs), raising concerns about SG toxicity, especially after repeated therapies or therapy with 225Ac-labeled ligands. SG toxicity is assessed clinically by the severity of patient-reported xerostomia, but this parameter can be challenging to objectively quantify. Therefore, we explored the feasibility of using SG volume as a biomarker for toxicity. In 21 patients with late-stage metastatic resistant prostate cancer (mCRPC), the PSMA volume and ligand uptake of SG were analyzed retrospectively before and after two cycles of 177Lu-PSMA (LuPSMA; cohort A) and before and after one cycle of 225Ac-PSMA-617 (AcPSMA, cohort B). Mean Volume-SG in cohort A was 59 ± 13 vs. 54 ± 16 mL (-10%, p = 0.4), and in cohort B, it was 50 ± 13 vs. 40 ± 11 mL (-20%, p = 0.007), respectively. A statistically significant decrease in the activity concentration in the SG was only observed in group B (SUVmean: 9.2 ± 2.8 vs. 5.3 ± 1.8, p < 0.0001; vs. A: SUVmean: 11.2 ± 3.3 vs. 11.1 ± 3.5, p = 0.8). SG volume and PSMA-ligand uptake are promising markers to monitor the SG toxicity after a PSMA RLT.

Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with 177Lu-PSMA I&T therapy.

PURPOSE: Although treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment. In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment. METHODS: Twenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA I&T RLT were included retrospectively. They had available pre-therapy 68 Ga-PSMA-HEBD-CC PET/CT and at least 3 planar and 1 SPECT/CT imaging for dosimetry. Overall, 43 cycles of 177Lu-PSMA I&T RLT were applied. Organ-based standard uptake values (SUVs) were obtained from pre-therapy PET/CT scans. Patient dosimetry was calculated for the kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the planar and SPECT/CT images. Machine learning methods were explored for dose prediction from organ SUVs and laboratory measurements. The uncertainty of these dose predictions was compared with the population-based dosimetry estimates. Mean absolute percentage error (MAPE) was used to assess the prediction uncertainty of estimated dosimetry. RESULTS: An optimal machine learning method achieved a dosimetry prediction MAPE of 15.8 ± 13.2% for the kidney, 29.6% ± 13.7% for the liver, 23.8% ± 13.1% for the salivary glands, and 32.1 ± 31.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (p < 0.01), 25.5 ± 17.3% for the kidney, 139.1% ± 111.5% for the liver, 67.0 ± 58.3% for the salivary glands, and 54.1 ± 215.3% for the spleen. CONCLUSION: The preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.

Measuring response in metastatic castration-resistant prostate cancer using PSMA PET/CT: comparison of RECIST 1.1, aPCWG3, aPERCIST, PPP, and RECIP 1.0 criteria.

PURPOSE: To compare the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, the adapted Prostate Cancer Working Group Criteria 3 (aPCWG3), the adapted Positron Emission Tomography Response Criteria in Solid Tumors (aPERCIST), the PSMA PET Progression (PPP), and the Response Evaluation Criteria In PSMA-Imaging (RECIP) 1.0 for response evaluation using prostate-specific membrane antigen (PSMA)-PET/CT in men with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA radioligand therapy. METHODS: A total of 124 patients were included in this multicenter retrospective study. All patients received 177Lu-PSMA and underwent PSMA-PET/CT scans at baseline (bPET) and at 12 weeks (iPET). Imaging responses according to RECIST 1.1, aPCWG3, aPERCIST, PPP, and RECIP 1.0 were interpreted by consensus among three blinded readers. Changes in total tumor burden were obtained using the semi-automatic qPSMA software. The response according to each criterion was classified to progressive disease (PD) vs no-PD. Primary outcome measure was the prognostic value (by Cox regression analysis) for overall survival (OS). Secondary outcome measure was the inter-reader reliability (by Cohen's κ coefficient). RESULTS: A total of 43 (35%) of patients had non-measurable disease according to RECIST 1.1. Sixteen (13%), 66 (52%), 72 (58%), 69 (56%), and 39 (32%) of 124 patients had PD according to RECIST 1.1, aPCWG3, aPERCIST, PPP, and RECIP, respectively. PD vs no-PD had significantly higher risk of death according to aPCWG3 (HR = 2.37; 95%CI, 1.62-3.48; p < 0.001), aPERCIST (HR = 2.48; 95%CI, 1.68-3.66; p < 0.001), PPP (HR = 2.72; 95%CI, 1.85-4.01; p < 0.001), RECIP 1.0 (HR = 4.33; 95%CI, 2.80-6.70; p < 0.001), but not according to RECIST 1.1 (HR = 1.29; 95%CI, 0.73-2.27; p = 0.38). The κ index of RECIST 1.1, aPCWG3, aPERCIST 1.0, PPP, and RECIP 1.0 for identifying PD vs no-PD were 0.50 (95%CI, 0.32-0.76), 0.72 (95%CI, 0.63-0.82), 0.68 (95%CI, 0.63-0.73), 0.73 (95%CI, 0.63-0.83), and 0.83 (95%CI, 0.77-0.88), respectively. CONCLUSION: PSMA-PET-specific criteria for early response evaluation in men with mCRPC treated with 177Lu-PSMA achieved higher prognostic values and inter-reader reliabilities in comparison to conventional CT assessment or to criteria adapted to PSMA-PET from other imaging modalities. RECIP 1.0 identified the fewest patients with PD and achieved the highest risk of death for PD vs. no-PD, suggesting that other classification methods tend to overcall progression. Prospective validation of our findings on an independent patient cohort is warranted.

Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning.

PURPOSE: In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers. METHODS: In 173 subjects imaged with 68Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework. RESULTS: In the development set, including 18F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1-87.8] for identification of suspicious uptake sites, 77% (CI: 70.0-83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage. CONCLUSION: The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.

The added value of PSMA PET/MR radiomics for prostate cancer staging.

PURPOSE: To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients. METHODS: Patients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1-3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student's t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS). RESULTS: All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS. CONCLUSION: All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

Nomograms to predict outcomes after 177Lu-PSMA therapy in men with metastatic castration-resistant prostate cancer: an international, multicentre, retrospective study.

BACKGROUND: Lutetium-177 (177Lu) prostate-specific membrane antigen (177Lu-PSMA) is a novel targeted treatment for patients with metastatic castration-resistant prostate cancer (mCRPC). Predictors of outcomes after 177Lu-PSMA to enhance its clinical implementation are yet to be identified. We aimed to develop nomograms to predict outcomes after 177Lu-PSMA in patients with mCRPC. METHODS: In this multicentre, retrospective study, we screened patients with mCRPC who had received 177Lu-PSMA between Dec 10, 2014, and July 19, 2019, as part of the previous phase 2 trials (NCT03042312, ACTRN12615000912583) or compassionate access programmes at six hospitals and academic centres in Germany, the USA, and Australia. Eligible patients had received intravenous 6·0-8·5 GBq 177Lu-PSMA once every 6-8 weeks, for a maximum of four to six cycles, and had available baseline [68Ga]Ga-PSMA-11 PET/CT scan, clinical data, and survival outcomes. Putative predictors included 18 pretherapeutic clinicopathological and [68Ga]Ga-PSMA-11 PET/CT variables. Data were collected locally and centralised. Primary outcomes for the nomograms were overall survival and prostate-specific antigen (PSA)-progression-free survival. Nomograms for each outcome were computed from Cox regression models with LASSO penalty for variable selection. Model performance was measured by examining discrimination (Harrell's C-index), calibration (calibration plots), and utility (patient stratification into low-risk vs high-risk groups). Models were validated internally using bootstrapping and externally by calculating their performance on a validation cohort. FINDINGS: Between April 23, 2019, and Jan 13, 2020, 414 patients were screened; 270 (65%) of whom were eligible and were divided into development (n=196) and validation (n=74) cohorts. The median duration of follow-up was 21·5 months (IQR 13·3-30·7). Predictors included in the nomograms were time since initial diagnosis of prostate cancer, chemotherapy status, baseline haemoglobin concentration, and [68Ga]Ga-PSMA-11 PET/CT parameters (molecular imaging TNM classification and tumour burden). The C-index of the overall survival model was 0·71 (95% CI 0·69-0·73). Similar C-indices were achieved at internal validation (0·71 [0·69-0·73]) and external validation (0·72 [0·68-0·76]). The C-index of the PSA-progression-free survival model was 0·70 (95% CI 0·68-0·72). Similar C-indices were achieved at internal validation (0·70 [0·68-0·72]) and external validation (0·71 [0·68-0·74]). Both models were adequately calibrated and their predictions correlated with the observed outcome. Compared with high-risk patients, low-risk patients had significantly longer overall survival in the validation cohort (24·9 months [95% CI 16·8-27·3] vs 7·4 months [4·0-10·8]; p<0·0001) and PSA-progression-free survival (6·6 months [6·0-7·1] vs 2·5 months [1·2-3·8]; p=0·022). INTERPRETATION: These externally validated nomograms that are predictive of outcomes after 177Lu-PSMA in patients with mCRPC might help in clinical trial design and individual clinical decision making, particularly at institutions where 177Lu-PSMA is introduced as a novel therapeutic option. FUNDING: Prostate Cancer Foundation.

Almost 10 years of PET/MR attenuation correction: the effect on lesion quantification with PSMA: clinical evaluation on 200 prostate cancer patients.

PURPOSE: After a decade of PET/MR, the case of attenuation correction (AC) remains open. The initial four-compartment (air, water, fat, soft tissue) Dixon-based AC scheme has since been expanded with several features, the latest being MR field-of-view extension and a bone atlas. As this potentially changes quantification, we evaluated the impact of these features in PET AC in prostate cancer patients. METHODS: Two hundred prostate cancer patients were examined with either 18F- or 68Ga-prostate-specific membrane antigen (PSMA) PET/MR. Qualitative and quantitative analysis (SUVmean, SUVmax, correlation, and statistical significance) was performed on images reconstructed using different AC schemes: Dixon, Dixon+MLAA, Dixon+HUGE, and Dixon+HUGE+bones for 18F-PSMA data; Dixon and Dixon+bones for 68Ga-PSMA data. Uptakes were compared using linear regression against standard Dixon. RESULTS: High correlation and no visually perceivable differences between all evaluated methods (r > 0.996) were found. The mean relative difference in lesion uptake of 18F-PSMA and 68Ga-PSMA remained, respectively, within 4% and 3% in soft tissue, and within 10% and 9% in bones for all evaluated methods. Bone registration errors were detected, causing mean uptake change of 5% in affected lesions. CONCLUSIONS: Based on these results and the encountered bone atlas registration inaccuracy, we deduce that including bones and extending the MR field-of-view did not introduce clinically significant differences in PSMA diagnostic accuracy and tracer uptake quantification in prostate cancer pelvic lesions, facilitating the analysis of serial studies respectively. However, in the absence of ground truth data, we advise against atlas-based methods when comparing serial scans for bone lesions.

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

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.

68Ga-PSMA-11 Positron Emission Tomography Detects Residual Prostate Cancer after Prostatectomy in a Multicenter Retrospective Study.

PURPOSE: Prostate specific antigen persistence after radical prostatectomy is associated with adverse outcomes in patients with prostate cancer. We sought to define regions at risk for residual disease as well as the accuracy of prostate specific membrane antigen ligand positron emission tomography in patients with prostate specific antigen persistence. MATERIALS AND METHODS: At 6 participating centers a total of 191 patients who underwent 68Ga-prostate specific membrane antigen-11 positron emission tomography/computerized tomography or positron emission tomography/magnetic resonance imaging for persistently elevated postoperative prostate specific antigen (0.1 ng/ml or greater) were retrospectively included in study. The detection rate and the positive predictive value were determined. In 33 patients with additional prostate specific membrane antigen ligand positron emission tomography before prostatectomy we also determined the rate of positron emission tomography based persistence and recurrence. RESULTS: Prostate specific membrane antigen ligand positron emission tomography localized prostate cancer in 130 of 191 patients (68%) with prostate specific antigen persistence at a median prostate specific antigen of 1.1 ng/ml. The detection rate significantly increased with prostate specific antigen (p <0.001). Regarding prostate specific membrane antigen positron emission tomography/computerized tomography only 61 of 173 patients (35%) had disease confined to the pelvis while 57 of 173 (33%) had distant lesions. The most frequently affected nodal regions were the obturator in 42% and the presacral/mesorectal region in 40%. In 15 of the 33 patients (45%) with prostate specific membrane antigen ligand positron emission tomography before and after surgery at least 1 lesion was detected at baseline (positron emission tomography persistence), 8 (24%) had new lesions (positron emission tomography recurrence) and 10 (30%) had negative positron emission tomography findings. The positive predictive value of prostate specific membrane antigen ligand positron emission tomography was 91%. Systemic therapy initiation was significantly associated with distant lesions on prostate specific membrane antigen ligand positron emission tomography. CONCLUSIONS: Prostate specific membrane antigen ligand positron emission tomography localized prostate cancer in more than two-thirds of patients with high risk features and prostate specific antigen persistence after prostatectomy. Obturator and presacral/mesorectal nodes are at high risk for persistent metastasis.

Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning.

Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.

Treatment Response Imaging in Prostate Cancer.

Objective criteria for measuring treatment response in prostate cancer are critical to clinical research and practice. The Prostate Cancer Working Group 3 criteria are widely accepted relying only on conventional imaging for radiographic treatment response. Prostate-specific membrane antigen PET/computed tomography was proven to be superior to conventional imaging in initial diagnosis and biochemical recurrence of prostate cancer. Moreover, there is growing evidence of its role in treatment response assessment in prostate cancer. This study will review the different criteria for imaging treatment response on conventional and advanced molecular imaging for different therapies, and the future perspective in posttherapy imaging.

PSMA-Targeted Radiopharmaceuticals for Prostate Cancer Diagnosis and Therapy.

ABSTRACT: Prostate cancer (PCa) is the most common noncutaneous malignancy in men. Until recent years, accurate imaging of men with newly diagnosed PCa, or recurrent or low-volume metastatic disease, was limited. Further, therapeutic options for men with advanced, metastatic, castration-resistant disease were increasingly limited as a result of increasing numbers of systemic therapies being combined in the upfront metastatic setting. The advent of urea-based, small-molecule inhibitors of prostate-specific membrane antigen (PSMA) has partially addressed those shortcomings in diagnosis and therapy of PCa. On the diagnostic side, there are multiple pivotal phase III trials with several different agents having demonstrated utility in the initial staging setting, with generally modest sensitivity but very high specificity for determining otherwise-occult pelvic nodal involvement. That latter statistic drives the utility of the scan by allowing imaging interpreters to read with very high sensitivity while maintaining a robust specificity. Other pivotal phase III trials have demonstrated high detection efficiency in patients with biochemical failure, with high positive predictive value at the lesion level, opening up possible new avenues of therapy such as metastasis-directed therapy. Beyond the diagnostic aspects of PSMA-targeted radiotracers, the same urea-based chemical scaffolds can be altered to deliver therapeutic isotopes to PCa cells that express PSMA. To date, one such agent, when combined with best standard-of-care therapy, has demonstrated an ability to improve overall survival, progression-free survival, and freedom from skeletal events relative to best standard-of-care therapy alone in men with metastatic, castration-resistant PCa who are post chemotherapy. Within the current milieu, there are a number of important future directions including the use of artificial intelligence to better leverage diagnostic findings, further medicinal chemistry refinements to the urea-based structure that may allow improved tumor targeting and decreased toxicities, and the incorporation of new radionuclides that may better balance efficacy with toxicities than those nuclides that are available.

RECIP 1.0 Predicts Progression-Free Survival After [177Lu]Lu-PSMA Radiopharmaceutical Therapy in Patients with Metastatic Castration-Resistant Prostate Cancer.

Response Evaluation Criteria in Prostate-Specific Membrane Antigen Imaging (RECIP) 1.0 is an evidence-based framework to evaluate therapeutic efficacy in metastatic prostate cancer using prostate-specific membrane antigen (PSMA) PET/CT. This study aimed to evaluate the associations of interim PSMA PET/CT by RECIP 1.0 with short-term outcome after radiopharmaceutical treatment. Methods: This multicenter retrospective study included patients with metastatic castration-resistant prostate cancer who underwent [177Lu]Lu-PSMA radiopharmaceutical therapy at 3 academic centers and received PSMA PET/CT at baseline and at 12 wk. Pairs of PSMA PET/CT images were assessed by 5 readers for visual RECIP 1.0. The primary outcome was the association of RECIP with prostate-specific antigen progression-free survival (PSA-PFS) by Kaplan-Meier analysis. Results: In total, 124 of 287 screened patients met the inclusion criteria, with 0 (0%), 29 (23%), 54 (44%), and 41 (33%) of those 124 patients having complete response, partial response, stable disease, or progressive disease (PD) by visual RECIP 1.0, respectively. Patients with visual RECIP PD had a significantly shorter PSA-PFS than those with RECIP stable disease or with RECIP partial response (2.6 vs. 6.4 vs. 8.4 mo; P < 0.001). The median PSA-PFS among patients with RECIP PD versus those with non-RECIP PD was 2.6 versus 7.2 mo (hazard ratio, 13.0; 95% CI, 7.0-24.1; P < 0.001). Conclusion: PSMA PET/CT by RECIP 1.0 after 2 cycles of [177Lu]Lu-PSMA is prognostic for PSA-PFS. PSMA PET/CT by RECIP 1.0 may be used in earlier stages of prostate cancer to evaluate drug efficacy and to predict progression-free survival.

Validation of Prognostic and Predictive Models for Therapeutic Response in Patients Treated with [177Lu]Lu-PSMA-617 Versus Cabazitaxel for Metastatic Castration-resistant Prostate Cancer (TheraP): A Post Hoc Analysis from a Randomised, Open-label, Phase 2 Trial.

BACKGROUND: Prognostic models have been developed using data from a multicentre noncomparative study to forecast the likelihood of a 50% reduction in prostate-specific antigen (PSA50), longer prostate-specific antigen (PSA) progression-free survival (PFS), and longer overall survival (OS) in patients with metastatic castration-resistant prostate cancer receiving [177Lu]Lu-PSMA radioligand therapy. The predictive utility of the models to identify patients likely to benefit most from [177Lu]Lu-PSMA compared with standard chemotherapy has not been established. OBJECTIVE: To determine the predictive value of the models using data from the randomised, open-label, phase 2, TheraP trial (primary objective) and to evaluate the clinical net benefit of the PSA50 model (secondary objective). DESIGN, SETTING, AND PARTICIPANTS: All 200 patients were randomised in the TheraP trial to receive [177Lu]Lu-PSMA-617 (n = 99) or cabazitaxel (n = 101) between February 2018 and September 2019. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Predictive performance was investigated by testing whether the association between the modelled outcome classifications (favourable vs unfavourable outcome) was different for patients randomised to [177Lu]Lu-PSMA versus cabazitaxel. The clinical benefit of the PSA50 model was evaluated using a decision curve analysis. RESULTS AND LIMITATIONS: The probability of PSA50 in patients classified as having a favourable outcome was greater in the [177Lu]Lu-PSMA-617 group than in the cabazitaxel group (odds ratio 6.36 [95% confidence interval {CI} 1.69-30.80] vs 0.96 [95% CI 0.32-3.05]; p = 0.038 for treatment-by-model interaction). The PSA50 rate in patients with a favourable outcome for [177Lu]Lu-PSMA-617 versus cabazitaxel was 62/88 (70%) versus 31/85 (36%). The decision curve analysis indicated that the use of the PSA50 model had a clinical net benefit when the probability of a PSA response was ≥30%. The predictive performance of the models for PSA PFS and OS was not established (treatment-by-model interaction: p = 0.36 and p = 0.41, respectively). CONCLUSIONS: A previously developed outcome classification model for PSA50 was demonstrated to be both predictive and prognostic for the outcome after [177Lu]Lu-PSMA-617 versus cabazitaxel, while the PSA PFS and OS models had purely prognostic value. The models may aid clinicians in defining strategies for patients with metastatic castration-resistant prostate cancer who failed first-line chemotherapy and are eligible for [177Lu]Lu-PSMA-617 and cabazitaxel. PATIENT SUMMARY: In this report, we validated previously developed statistical models that can predict a response to Lu-PSMA radioligand therapy in patients with advanced prostate cancer. We found that the statistical models can predict patient survival, and aid in determining whether Lu-PSMA therapy or cabazitaxel yields a higher probability to achieve a serum prostate-specific antigen response.

Prognostic Performance of RECIP 1.0 Based on [18F]PSMA-1007 PET in Prostate Cancer Patients Treated with [177Lu]Lu-PSMA I&T.

In metastatic castration-resistant prostate cancer (mCRPC) patients treated with prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT), the recently proposed criteria for evaluating response to PSMA PET (RECIP 1.0) based on 68Ga- and 18F-labeled PET agents provided prognostic information in addition to changes in prostate-specific antigen (PSA) levels. Our aim was to evaluate the prognostic performance of this framework for overall survival (OS) in patients undergoing RLT and imaged with [18F]PSMA-1007 PET/CT and compare the prognostic performance with the PSA-based response assessment. Methods: In total, 73 patients with mCRPC who were scanned with [18F]PSMA-1007 PET/CT before and after 2 cycles of RLT were retrospectively analyzed. We calculated the changes in serum PSA levels (ΔPSA) and quantitative PET parameters for the whole-body tumor burden (SUVmean, SUVmax, PSMA tumor volume, and total lesion PSMA). Men were also classified following the Prostate Cancer Working Group 3 (PCWG3) criteria for ΔPSA and RECIP 1.0 for PET imaging response. We performed univariable Cox regression analysis, followed by multivariable and Kaplan-Meier analyses. Results: Median OS was 15 mo with a median follow-up time of 14 mo. Univariable Cox regression analysis provided significant associations with OS for ΔPSA (per percentage, hazard ratio [HR], 1.004; 95% CI, 1.002-1.007; P < 0.001) and PSMA tumor volume (per unit, HR, 1.003; 95% CI, 1.000-1.005; P = 0.03). Multivariable Cox regression analysis confirmed ΔPSA (per percentage, HR, 1.004; 95% CI, 1.001-1.006; P = 0.006) as an independent prognosticator for OS. Kaplan-Meier analyses provided significant segregation between individuals with versus those without any PSA response (19 mo vs. 14 mo; HR, 2.00; 95% CI, 0.95-4.18; P = 0.04). Differentiation between patients with or without progressive disease (PD) was also feasible when applying PSA-based PCWG3 (19 mo vs. 9 mo for non-PD and PD, respectively; HR, 2.29; 95% CI, 1.03-5.09; P = 0.01) but slightly failed when applying RECIP 1.0 (P = 0.08). A combination of both response systems (PCWG3 and RECIP 1.0), however, yielded the best discrimination between individuals without versus those with PD (19 mo vs. 8 mo; HR, 2.78; 95% CI, 1.32-5.86; P = 0.002). Conclusion: In patients with mCRPC treated with RLT and imaged with [18F]PSMA-1007, frameworks integrating both the biochemical (PCWG3) and PET-based response (RECIP 1.0) may best assist in identifying subjects prone to disease progression.