aPROMISE: A Novel Automated-PROMISE platform to Standardize Evaluation of Tumor Burden in 18F-DCFPyL (PSMA) images of Veterans with Prostate Cancer

Enhancing prostate cancer diagnosis and reducing unnecessary biopsies with [18F]DCFPyL PET/CT imaging in PI-RADS 3/4 patients

For patients presenting with prostate imaging reporting and data system (PI-RADS) 3/4 findings on magnetic resonance imaging (MRI) examinations, the standard recommendation typically involves undergoing a biopsy for pathological assessment to ascertain the nature of the lesion. This course of action, though essential for accurate diagnosis, invariably amplifies the psychological distress experienced by patients and introduces a host of potential complications associated with the biopsy procedure. However, [18F]DCFPyL PET/CT imaging emerges as a promising alternative, demonstrating considerable diagnostic efficacy in discerning benign prostate lesions from malignant ones. This study aims to explore the diagnostic value of [18F]DCFPyL PET/CT imaging for prostate cancer in patients with PI-RADS 3/4 lesions, assisting in clinical decision-making to avoid unnecessary biopsies. 30 patients diagnosed with PI-RADS 3/4 lesions through mpMRI underwent [18F]DCFPyL PET/CT imaging, with final biopsy pathology results as the "reference standard". Diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, evaluating the diagnostic efficacy of molecular imaging PSMA (miPSMA) visual analysis and semi-quantitative analysis in [18F]DCFPyL PET/CT imaging. Lesions were assigned miPSMA scores according to the prostate cancer molecular imaging standardized evaluation criteria. Among the 30 patients, 13 were pathologically confirmed to have prostate cancer. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of visual analysis in [18F]DCFPyL PET/CT imaging for diagnosing PI-RADS 3/4 lesions were 61.5%, 88.2%, 80.0%, 75.0%, and 76.5%, respectively. Using SUVmax 4.17 as the optimal threshold, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis were 92.3%, 88.2%, 85.7%, 93.8%, and 90.0%, respectively. The area under the ROC curve (AUC) for semi-quantitative analysis was 0.94, significantly higher than visual analysis at 0.80. [18F]DCFPyL PET/CT imaging accurately diagnosed benign lesions in 15 (50%) of the PI-RADS 3/4 patients. For patients with PI-RADS 4 lesions, the positive predictive value of [18F]DCFPyL PET/CT imaging reached 100%. [18F]DCFPyL PET/CT imaging provides potential preoperative prediction of lesion nature in mpMRI PI-RADS 3/4 patients, which may aid in treatment decision-making and reducing unnecessary biopsies.

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.

Whole-body tumour burden on [18F]DCFPyL PET/CT in biochemical recurrence of prostate cancer: association with tumour biology and PSA kinetics

PURPOSE

The objective was to assess the association between molecular imaging (mi) variables on [18F]DCFPyL-PET/CT with clinical and disease characteristics and prostate specific antigen (PSA) related variables in patients with biochemical recurrence of prostate cancer (BRPC).

MATERIAL AND METHODS

We analysed patients with BRPC after radical treatment. We obtained clinical and PSA variables: International Society of Urology Pathology (ISUP) grade group, European Association of Urology (EAU) risk classification, PSA (PSA≤1ng/ml, 12), PSA doubling time (PSAdt) and PSA velocity (PSAvel). All PET/CT scans were reviewed with the assistance of automated Prostate Molecular Imaging Standardized Evaluation (aPROMISE) software and lesions' segmentation in positive scans was performed using this platform. Standardized uptake value (SUV) derived variables; tumour burden variables [whole-body tumour volume (wbTV), whole-body tumour lesion activity (wbTLA) and whole-body mi PSMA (wbPSMA)] and miTNM staging were obtained. Cut-off of PSA and kinetics able to predict PET/CT results were obtained. Associations between disease and mi variables were analysed using ANOVA, Kruskal-Wallis and Spearman's correlation tests. Multivariate analysis was also performed.

RESULTS

Two hundred and seventy-five patients were studied. [18F]DCFPyL-PET/CT were positive in 165/275 patients. In multivariate analysis, moment of biochemical recurrence, ISUP group, PSA level and PSAvel showed significant association with the detection rate. miTNM showed significant association with PSA level (p<0.001) and kinetics (p<0.001), being higher in patients with metastatic disease. Both PSA and PSAvel showed moderate correlation with wbTV, wbTLA and wbPSMA (p<0.001). A weak correlation with SUVs was found. Mean wbTV, wbTLA and wbPSMA values were significantly higher in PSA > 2ng/ml, PSAdt ≤ 6 months and PSAvel ≥ 0.2ng/ml/month groups. Also, wbTV (p=0.039) and wbPSMA (p=0.020) were significantly higher in patients with ISUP grade group 5. PSA and PSAvel cut-offs (1.15 ng/ml and 0.065 ng/ml/month) were significantly associated with a positive PET/CT.

CONCLUSION

Higher PSA values, unfavourable PSA kinetics and ISUP grade group 5 were robust predictive variables of larger tumour burden variables on [18F]DCFPyL PET/CT assessed by aPROMISE platform.

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.

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.

Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR

BACKGROUND

Prostate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC). Unknown features influencing PSMA biodistribution can be explored by analyzing segmented organs at risk (OAR) and lesions. Manual segmentation is time-consuming and labor-intensive, so automated segmentation methods are desirable. Training deep-learning segmentation models is challenging due to the scarcity of high-quality annotated images. Addressing this, we developed shifted windows UNEt TRansformers (Swin UNETR) for fully automated segmentation. Within a self-supervised framework, the model's encoder was pre-trained on unlabeled data. The entire model was fine-tuned, including its decoder, using labeled data.

METHODS

In this work, 752 whole-body [68Ga]Ga-PSMA-11 PET/CT images were collected from two centers. For self-supervised model pre-training, 652 unlabeled images were employed. The remaining 100 images were manually labeled for supervised training. In the supervised training phase, 5-fold cross-validation was used with 64 images for model training and 16 for validation, from one center. For testing, 20 hold-out images, evenly distributed between two centers, were used. Image segmentation and quantification metrics were evaluated on the test set compared to the ground-truth segmentation conducted by a nuclear medicine physician.

RESULTS

The model generates high-quality OARs and lesion segmentation in lesion-positive cases, including mCRPC. The results show that self-supervised pre-training significantly improved the average dice similarity coefficient (DSC) for all classes by about 3%. Compared to nnU-Net, a well-established model in medical image segmentation, our approach outperformed with a 5% higher DSC. This improvement was attributed to our model's combined use of self-supervised pre-training and supervised fine-tuning, specifically when applied to PET/CT input. Our best model had the lowest DSC for lesions at 0.68 and the highest for liver at 0.95.

CONCLUSIONS

We developed a state-of-the-art neural network using self-supervised pre-training on whole-body [68Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry.

Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n = 83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n = 123 subjects). Set1, the data set from Scanner1, was reconstructed with standard clinical parameters (5 min; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-min). Set2, data from Scanner2 representing out-of-domain clinical scans, used standard iterative reconstruction (5 min; OSEM). A deep neural network was trained with each dataset: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50% and 75%) of Set1 for training. Scanner1, list-mode 2-min reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1 = 0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1 = 0.755; p-value = 0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1 = 0.478) to 100% training data (F1 = 0.713; p < 0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumor. Patients with a recent diagnosis of intermediate/high-risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-18F-DCFPyL-PET/CT scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with two softwares. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen's kappa coefficient (k) for the concordance analysis. Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, tumor volume (TV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p < 0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k = 0.733; p < 0.001 and k = 0.812; p < 0.001, respectively) although the concordances with histopathology were moderate (p < 0001). The analytical validation of aPROMISE showed a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.

A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans

Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.

Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.

Modified PROMISE criteria for standardized interpretation of gastrin-releasing peptide receptor (GRPR)-targeted PET

PURPOSE

There are image interpretation criteria to standardize reporting prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET). As up to 10% of prostate cancer (PC) do not express PSMA, other targets such as gastrin-releasing peptide receptor (GRPR) are evaluated. Research on GRPR-targeted imaging has been slowly increasing in usage at staging and biochemical recurrence (BCR) of PC. We therefore propose a modification of the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria (mPROMISE) for GRPR-targeted PET.

METHODS

[68 Ga]Ga-RM2 PET data from initially prospective studies performed at our institution were retrospectively reviewed: 44 patients were imaged for staging and 100 patients for BCR PC. Two nuclear medicine physicians independently evaluated PET according to the mPROMISE criteria. A third expert reader served as standard reference. Interreader reliability was computed for GRPR expression, prostate bed (T), lymph node (N), skeleton (Mb), organ (Mc) metastases, and final judgment of the scan.

RESULTS

The interrater reliability for GRPR PET at staging was moderate for GRPR expression (0.59; 95% confidence interval [CI] 0.40, 0.78), substantial for T-stage (0.78; 95% CI 0.63, 0.94), and almost perfect for N-stage (0.97; 95% CI 0.92, 1.00) and final judgment (0.92; 95% CI 0.82, 1.00). The interreader agreement at BCR showed substantial agreement for GRPR expression (0.70; 95% CI 0.59, 0.81) and final judgment (0.65; 95% CI 0.53, 0.78), while almost perfect agreement was seen across the major categories (T, N, Mb, Mc). Acceptable performance of the mPROMISE criteria was found for all subsets when compared to the standard reference.

CONCLUSION

Interpreting GRPR-targeted PET using the mPROMISE criteria showed its reliability with substantial or almost perfect interrater agreement across all major categories. The proposed modification of the PROMISE criteria will aid clinicians in decreasing the level of uncertainty, and clinical trials to achieve uniform evaluation, reporting, and comparability of GRPR-targeted PET.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier: NCT03113617 and NCT02624518.

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.

PSMA PET Tumor-to-Salivary Gland Ratio to Predict Response to [177Lu]PSMA Radioligand Therapy: An International Multicenter Retrospective Study

Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy can improve the outcome of patients with advanced metastatic castration-resistant prostate cancer, but patients do not respond uniformly. We hypothesized that using the salivary glands as a reference organ can enable selective patient stratification. We aimed to establish a PSMA PET tumor-to-salivary gland ratio (PSG score) to predict outcomes after [177Lu]PSMA. Methods: In total, 237 men with metastatic castration-resistant prostate cancer treated with [177Lu]PSMA were included. A quantitative PSG (qPSG) score (SUVmean ratio of whole-body tumor to parotid glands) was semiautomatically calculated on baseline [68Ga]PSMA-11 PET images. Patients were divided into 3 groups: high (qPSG > 1.5), intermediate (qPSG = 0.5-1.5), and low (qPSG < 0.5) scores. Ten readers interpreted the 3-dimensional maximum-intensity-projection baseline [68Ga]PSMA-11 PET images and classified patients into 3 groups based on visual PSG (vPSG) score: high (most of the lesions showed higher uptake than the parotid glands) intermediate (neither low nor high), and low (most of the lesions showed lower uptake than the parotid glands). Outcome data included a more than 50% prostate-specific antigen decline, prostate-specific antigen (PSA) progression-free survival, and overall survival (OS). Results: Of the 237 patients, the numbers in the high, intermediate, and low groups were 56 (23.6%), 163 (68.8%), and 18 (7.6%), respectively, for qPSG score and 106 (44.7%), 96 (40.5%), and 35 (14.8%), respectively, for vPSG score. The interreader reproducibility of the vPSG score was substantial (Fleiss weighted κ, 0.68). The more than 50% prostate-specific antigen decline was better in patients with a higher PSG score (high vs. intermediate vs. low, 69.6% vs. 38.7% vs. 16.7%, respectively, for qPSG [P < 0.001] and 63.2% vs 33.3% vs 16.1%, respectively, for vPSG [P < 0.001]). The median PSA progression-free survival of the high, intermediate, and low groups by qPSG score was 7.2, 4.0, and 1.9 mo (P < 0.001), respectively, by qPSG score and 6.7, 3.8, and 1.9 mo (P < 0.001), respectively, by vPSG score. The median OS of the high, intermediate, and low groups was 15.0, 11.2, and 13.9 mo (P = 0.017), respectively, by qPSG score and 14.3, 9.6, and 12.9 mo (P = 0.018), respectively, by vPSG score. Conclusion: The PSG score was prognostic for PSA response and OS after [177Lu]PSMA. The visual PSG score assessed on 3-dimensional maximum-intensity-projection PET images yielded substantial reproducibility and comparable prognostic value to the quantitative score.

Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging

Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.

Second Version of the Prostate Cancer Molecular Imaging Standardized Evaluation Framework Including Response Evaluation for Clinical Trials (PROMISE V2)

CONTEXT

Prostate-specific membrane antigen (PSMA) targeting positron emission tomography (PET) is emerging to become a reference imaging tool for the staging and restaging of patients with prostate cancer for both clinical routine and trials. The prostate cancer molecular imaging standardized evaluation (PROMISE) criteria have been proposed as a framework for whole-body staging (molecular imaging TNM staging, denoted miTNM staging) to describe the prostate cancer disease extent on PSMA-PET.

OBJECTIVE

To create a comprehensive and integrated framework for PSMA-PET image interpretation and reporting.

EVIDENCE ACQUISITION

We propose the PROMISE V2 framework, which integrates an updated miTNM system, improved assessment of local disease, and a slightly modified PSMA-expression score for clinical routine. We have added a response monitoring framework defining qualitative and quantitative parameters to be recorded for a longitudinal assessment in clinical trials.

EVIDENCE SYNTHESIS

We provide a comprehensive literature review on the current use of the PROMISE framework in clinical research and prospective trials. PROMISE variables demonstrate a clear association with survival. PSMA expression assessed by the PSMA-expression score was used in several trials, and a low PSMA-expression score is a negative prognosticator of overall survival after ¹⁷⁷Lu-PSMA radioligand therapy. The proposed imaging parameters recorded for response assessment in clinical trials can be utilized to determine response according to PSMA-PET progression (PPP) or Response Evaluation Criteria in PSMA-PET/Computed Tomography (RECIP) frameworks, but also future response criteria.

CONCLUSIONS

PROMISE V2 offers standardized reporting of disease extent for clinical routine and research. Parameters recorded within clinical trials facilitate objective response assessment.

PATIENT SUMMARY

Prostate-specific membrane antigen (PSMA) targeting positron emission tomography (PET) has become a standard imaging examination for prostate cancer. We propose a comprehensive framework for the analysis and reporting of PSMA-PET findings that will improve the communication between imaging experts and uro-oncologists.

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

The other immuno-PET: Metabolic tracers in evaluation of immune responses to immune checkpoint inhibitor therapy for solid tumors

Unique patterns of response to immune checkpoint inhibitor therapy, discernable in the earliest clinical trials, demanded a reconsideration of the standard methods of radiological treatment assessment. Immunomonitoring, that characterizes immune responses, offers several significant advantages over the tumor-centric approach currently used in the clinical practice: 1) better understanding of the drugs' mechanism of action and treatment resistance, 2) earlier assessment of response to therapy, 3) patient/therapy selection, 4) evaluation of toxicity and 5) more accurate end-point in clinical trials. PET imaging in combination with the right agent offers non-invasive tracking of immune processes on a whole-body level and thus represents a method uniquely well-suited for immunomonitoring. Small molecule metabolic tracers, largely neglected in the immuno-PET discourse, offer a way to monitor immune responses by assessing cellular metabolism known to be intricately linked with immune cell function. In this review, we highlight the use of small molecule metabolic tracers in imaging immune responses, provide a view of their value in the clinic and discuss the importance of image analysis in the context of tracking a moving target.

68Ga-PSMA-11 PET/MRI in Patients with Newly Diagnosed Intermediate- or High-Risk Prostate Adenocarcinoma: PET Findings Correlate with Outcomes After Definitive Treatment

Prostate-specific membrane antigen (PSMA) PET offers an accuracy superior to other imaging modalities in initial staging of prostate cancer and is more likely to affect management. We examined the prognostic value of ⁶⁸Ga-PSMA-11 uptake in the primary lesion and presence of metastatic disease on PET in newly diagnosed prostate cancer patients before initial therapy. Methods: In a prospective study from April 2016 to December 2020, ⁶⁸Ga-PSMA-11 PET/MRI was performed in men with a new diagnosis of intermediate- or high-grade prostate cancer who were candidates for prostatectomy. Patients were followed up after initial therapy for up to 5 y. We examined the Kendall correlation between PET (intense uptake in the primary lesion and presence of metastatic disease) and clinical and pathologic findings (grade group, extraprostatic extension, nodal involvement) relevant for risk stratification, and examined the relationship between PET findings and outcome using Kaplan-Meier analysis. Results: Seventy-three men (age, 64.0 ± 6.3 y) were imaged. Seventy-two had focal uptake in the prostate, and in 20 (27%) PSMA-avid metastatic disease was identified. Uptake correlated with grade group and prostate-specific antigen (PSA). Presence of PSMA metastasis correlated with grade group and pathologic nodal stage. PSMA PET had higher per-patient positivity than nodal dissection in patients with only 5-15 nodes removed (8/41 vs. 3/41) but lower positivity if more than 15 nodes were removed (13/21 vs. 10/21). High uptake in the primary lesion (SUV_max > 12.5, P = 0.008) and presence of PSMA metastasis (P = 0.013) were associated with biochemical failure, and corresponding hazard ratios for recurrence within 2 y (4.93 and 3.95, respectively) were similar to or higher than other clinicopathologic prognostic factors. Conclusion: ⁶⁸Ga-PSMA-11 PET can risk-stratify patients with intermediate- or high-grade prostate cancer before prostatectomy based on degree of uptake in the prostate and presence of metastatic disease.

The 28th Annual Prostate Cancer Foundation Scientific Retreat report

BACKGROUND

The 28th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held virtually over 4 days, on October 28-29 and November 4-5, 2021.

METHODS

The Annual PCF Scientific Retreat is a leading global scientific conference that focuses on first-in-field, unpublished, and high-impact basic, translational, and clinical prostate cancer research, as well as research from other fields with high probability for impacting prostate cancer research and patient care.

RESULTS

Primary areas of research discussed at the 2021 PCF Retreat included: (i) prostate cancer disparities; (ii) prostate cancer survivorship; (iii) next-generation precision medicine; (iv) PSMA theranostics; (v) prostate cancer lineage plasticity; (vi) tumor metabolism as a cancer driver and treatment target; (vii) prostate cancer genetics and polygenic risk scores; (viii) glucocorticoid receptor biology in castration-resistant prostate cancer (CRPC); (ix) therapeutic degraders; (x) new approaches for immunotherapy in prostate cancer; (xi) novel technologies to overcome the suppressive tumor microenvironment; and (xii) real-world evidence and synthetic/virtual control arms.

CONCLUSIONS

This article provides a summary of the presentations from the 2021 PCF Scientific Retreat. We hope that sharing this knowledge will help to improve the understanding of the current state of research and direct new advances in prostate cancer research and care.

Prostate specific membrane antigen positron emission tomography in primary prostate cancer diagnosis: First-line imaging is afoot

Prostate specific membrane antigen positron emission tomography (PSMA PET) is an excellent molecular imaging technique for prostate cancer. Currently, PSMA PET for patients with primary prostate cancer is supplementary to conventional imaging techniques, according to guidelines. This supplementary function of PSMA PET is due to a lack of systematic review of its strengths, limitations, and potential development direction. Thus, we review PSMA ligands, detection, T, N, and M staging, treatment management, and false results of PSMA PET in clinical studies. We also discuss the strengths and challenges of PSMA PET. PSMA PET can greatly increase the detection rate of prostate cancer and accuracy of T/N/M staging, which facilitates more appropriate treatment for primary prostate cancer. Lastly, we propose that PSMA PET could become the first-line imaging modality for primary prostate cancer, and we describe its potential expanded application.

Predictors and Real-World Use of Prostate-Specific Radioligand Therapy: PSMA and Beyond

PSMA is a transmembrane protein that is markedly overexpressed in prostate cancer, making it an excellent target for imaging and treating patients with prostate cancer. Several small molecule inhibitors and antibodies of PSMA have been radiolabeled for use as therapeutic agents and are currently under clinical investigation. PSMA-based radionuclide therapy is a promising therapeutic option for men with metastatic prostate cancer. The phase II TheraP study demonstrated superior efficacy, lower side effects, and improved patient-reported outcomes compared with cabazitaxel. The phase III VISION study demonstrated that radionuclide therapy with β-emitter ¹⁷⁷Lu-PSMA-617 can prolong survival and improve quality of life when offered in addition to standard-of-care therapy in men with PSMA-positive metastatic castration-resistant prostate cancer whose disease had progressed with conventional treatments. Nevertheless, up to 30% of patients have inherent resistance to PSMA-based radionuclide therapy, and acquired resistance is inevitable. Hence, strategies to increase the efficacy of PSMA-based radionuclide therapy have been under clinical investigation. These include better patient selection; increased radiation damage delivery via dosimetry-based administered dose or use of α-emitters instead of β-emitters; or using combinatorial approaches to overcome radioresistance mechanisms (innate or acquired), such as with novel hormonal agents, PARP inhibitors, or immunotherapy.

Targeting PSMA Revolutionizes the Role of Nuclear Medicine in Diagnosis and Treatment of Prostate Cancer

Simple Summary

Imaging plays a crucial role in the accurate staging of prostate cancer. Prostate-specific membrane antigen (PSMA) is overexpressed in prostate cancer cells, and targeting the PSMA protein for diagnostic purposes has become of great clinical value. Another valuable feature of PSMA is its opportunity to serve as a target for delivering radionuclide therapy to cancer cells. PSMA-ligands can be labeled with various radionuclides, such as alpha and beta-emitters. This review offers an overview of the literature on recent developments in nuclear medicine regarding PSMA in prostate cancer diagnostics and targeted radionuclide therapy.

Abstract

Targeting the prostate-specific membrane antigen (PSMA) protein has become of great clinical value in prostate cancer (PCa) care. PSMA positron emission tomography/computed tomography (PET/CT) is increasingly used in initial staging and restaging at biochemical recurrence in patients with PCa, where it has shown superior detection rates compared to previous imaging modalities. Apart from targeting PSMA for diagnostic purposes, there is a growing interest in developing ligands to target the PSMA-protein for radioligand therapy (RLT). PSMA-based RLT is a novel treatment that couples a PSMA-antibody to (alpha or beta-emitting) radionuclide, such as Lutetium-177 (¹⁷⁷Lu), to deliver high radiation doses to tumor cells locally. Treatment with ¹⁷⁷Lu-PSMA RLT has demonstrated a superior overall survival rate within randomized clinical trials as compared to routine clinical care in patients with metastatic castration-resistant prostate cancer (mCRPC). The current review provides an overview of the literature regarding recent developments in nuclear medicine related to PSMA-targeted PET imaging and Theranostics.

Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography

Metastatic tumor deposits in bone marrow elicit differential bone responses that vary with the type of malignancy. This results in either sclerotic, lytic, or mixed bone lesions, which can change in morphology due to treatment effects and/or secondary bone remodeling. Hence, morphological imaging is regarded unsuitable for response assessment of bone metastases and in the current Response Evaluation Criteria In Solid Tumors 1.1 (RECIST1.1) guideline bone metastases are deemed unmeasurable. Nevertheless, the advent of functional and molecular imaging modalities such as whole-body magnetic resonance imaging (WB-MRI) and positron emission tomography (PET) has improved the ability for follow-up of bone metastases, regardless of their morphology. Both these modalities not only have improved sensitivity for visual detection of bone lesions, but also allow for objective measurements of bone lesion characteristics. WB-MRI provides a global assessment of skeletal metastases and for a one-step "all-organ" approach of metastatic disease. Novel MRI techniques include diffusion-weighted imaging (DWI) targeting highly cellular lesions, dynamic contrast-enhanced MRI (DCE-MRI) for quantitative assessment of bone lesion vascularization, and multiparametric MRI (mpMRI) combining anatomical and functional sequences. Recommendations for a homogenization of MRI image acquisitions and generalizable response criteria have been developed. For PET, many metabolic and molecular radiotracers are available, some targeting tumor characteristics not confined to cancer type (e.g. 18F-FDG) while other targeted radiotracers target specific molecular characteristics, such as prostate specific membrane antigen (PSMA) ligands for prostate cancer. Supporting data on quantitative PET analysis regarding repeatability, reproducibility, and harmonization of PET/CT system performance is available. Bone metastases detected on PET and MRI can be quantitatively assessed using validated methodologies, both on a whole-body and individual lesion basis. Both have the advantage of covering not only bone lesions but visceral and nodal lesions as well. Hybrid imaging, combining PET with MRI, may provide complementary parameters on the morphologic, functional, metabolic and molecular level of bone metastases in one examination. For clinical implementation of measuring bone metastases in response assessment using WB-MRI and PET, current RECIST1.1 guidelines need to be adapted. This review summarizes available data and insights into imaging of bone metastases using MRI and PET.

Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [18F]DCFPyL (PSMA) imaging for standardized reporting

Purpose

The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [¹⁸F]DCFPyL (PSMA) PET/CT scans. In the current study, we analytically validated aPROMISE, a software as a medical device that segments organs in low-dose CT images with deep learning, and subsequently detects and quantifies potential pathological lesions in PSMA PET/CT.

Methods

To evaluate the deep learning algorithm, the automated segmentations of the low-dose CT component of PSMA PET/CT scans from 20 patients were compared to manual segmentations. Dice scores were used to quantify the similarities between the automated and manual segmentations. Next, the automated quantification of tracer uptake in the reference organs and detection and pre-segmentation of potential lesions were evaluated in 339 patients with prostate cancer, who were all enrolled in the phase II/III OSPREY study. Three nuclear medicine physicians performed the retrospective independent reads of OSPREY images with aPROMISE. Quantitative consistency was assessed by the pairwise Pearson correlations and standard deviation between the readers and aPROMISE. The sensitivity of detection and pre-segmentation of potential lesions was evaluated by determining the percent of manually selected abnormal lesions that were automatically detected by aPROMISE.

Results

The Dice scores for bone segmentations ranged from 0.88 to 0.95. The Dice scores of the PSMA PET/CT reference organs, thoracic aorta and liver, were 0.89 and 0.97, respectively. Dice scores of other visceral organs, including prostate, were observed to be above 0.79. The Pearson correlation for blood pool reference was higher between any manual reader and aPROMISE, than between any pair of manual readers. The standard deviations of reference organ uptake across all patients as determined by aPROMISE (SD = 0.21 blood pool and SD = 1.16 liver) were lower compared to those of the manual readers. Finally, the sensitivity of aPROMISE detection and pre-segmentation was 91.5% for regional lymph nodes, 90.6% for all lymph nodes, and 86.7% for bone in metastatic patients.

Conclusion

In this analytical study, we demonstrated the segmentation accuracy of the deep learning algorithm, the consistency in quantitative assessment across multiple readers, and the high sensitivity in detecting potential lesions. The study provides a foundational framework for clinical evaluation of aPROMISE in standardized reporting of PSMA PET/CT.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05497-8.