Semiautomatically Quantified Tumor Volume Using 68Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer

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.

Comparison of discovery rates and prognostic utility of [68Ga]Ga-PSMA-11 PET/CT and circulating tumor DNA in prostate cancer-a cross-sectional study

BACKGROUND

Circulating-tumor DNA (ctDNA) and prostate-specific membrane antigen (PSMA) ligand positron-emission tomography (PET) enable minimal-invasive prostate cancer (PCa) detection and survival prognostication. The present study aims to compare their tumor discovery abilities and prognostic values.

METHODS

One hundred thirty men with confirmed PCa (70.5 ± 8.0 years) who underwent [68Ga]Ga-PSMA-11 PET/CT (184.8 ± 19.7 MBq) imaging and plasma sample collection (March 2019-August 2021) were included. Plasma-extracted cell-free DNA was subjected to whole-genome-based ctDNA analysis. PSMA-positive tumor lesions were delineated and their quantitative parameters extracted. ctDNA and PSMA PET/CT discovery rates were compared, and the prognostic value for overall survival (OS) was evaluated.

RESULTS

PSMA PET discovery rates according to castration status and PSA ranges did differ significantly (P = 0.013, P < 0.001), while ctDNA discovery rates did not (P = 0.311, P = 0.123). ctDNA discovery rates differed between localized and metastatic disease (P = 0.013). Correlations between ctDNA concentrations and PSMA-positive tumor volume (PSMA-TV) were significant in all (r = 0.42, P < 0.001) and castration-resistant (r = 0.65, P < 0.001), however not in hormone-sensitive patients (r = 0.15, P = 0.249). PSMA-TV and ctDNA levels were associated with survival outcomes in the Logrank (P < 0.0001, P < 0.0001) and multivariate Cox regression analysis (P = 0.0023, P < 0.0001).

CONCLUSION

These findings suggest that PSMA PET imaging outperforms ctDNA analysis in detecting prostate cancer across the whole spectrum of disease, while both modalities are independently highly prognostic for survival outcomes.

Prostate Cancer Theranostics With 177Lu-PSMA

This review paper highlights the transformative role of PSMA-targeted diagnostics and therapy in prostate cancer management, particularly focusing on 177Lu-PSMA-617, approved by the FDA and EMA for metastatic castration-resistant prostate cancer (mCRPC) patients post-chemotherapy and ARPI treatment. Originating from the VISION trial's success, this paper navigates the current radioligand therapy (RLT) indications, emphasizing practical patient selection, planning, and treatment execution. It critically examines Lu-PSMA's comparative effectiveness against cabazitaxel and Ra-223, addressing decision-making dilemmas for mCRPC treatments. Furthermore, the paper discusses Lu-PSMA in chemotherapy-naïve patients and its application in hormone-sensitive prostate cancer, underlined by ongoing global studies. A significant concern is Lu-PSMA's long-term safety profile, particularly nephrotoxicity risks, necessitating further investigation. The possibility of Lu-PSMA rechallenge in responsive patients is explored, stressing the need for comprehensive analyses and real-world data to refine treatment protocols. Conclusively, PSMA-targeted therapy marks a significant advance in prostate cancer therapy, advocating for its integration into a multimodal, patient-centric treatment approach. The review underscores the imperative for additional comparative studies to optimize treatment sequences and outcomes, ultimately enhancing long-term prognosis and disease control in prostate cancer management.

Future Perspectives of Artificial Intelligence in Bone Marrow Dosimetry and Individualized Radioligand Therapy

Radioligand therapy is an emerging and effective treatment option for various types of malignancies, but may be intricately linked to hematological side effects such as anemia, lymphopenia or thrombocytopenia. The safety and efficacy of novel theranostic agents, targeting increasingly complex targets, can be well served by comprehensive dosimetry. However, optimization in patient management and patient selection based on risk-factors predicting adverse events and built upon reliable dose-response relations is still an open demand. In this context, artificial intelligence methods, especially machine learning and deep learning algorithms, may play a crucial role. This review provides an overview of upcoming opportunities for integrating artificial intelligence methods into the field of dosimetry in nuclear medicine by improving bone marrow and blood dosimetry accuracy, enabling early identification of potential hematological risk-factors, and allowing for adaptive treatment planning. It will further exemplify inspirational success stories from neighboring disciplines that may be translated to nuclear medicine practices, and will provide conceptual suggestions for future directions. In the future, we expect artificial intelligence-assisted (predictive) dosimetry combined with clinical parameters to pave the way towards truly personalized theranostics in radioligand therapy.

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.

Volumetric Parameters Derived from CXCR4-Directed PET/CT Predict Outcome in Patients with Gastrointestinal Neuroendocrine Carcinomas

BACKGROUND

Gastro-entero-pancreatic neuroendocrine carcinomas (GEP-NECs) are an aggressive subgroup of neuroendocrine neoplasms (NENs). In patients affected with NEN, there is a growing body of evidence that increased C-X-C motif chemokine receptor (CXCR4) expression is linked to decreasing overall survival (OS) in an ex-vivo setting. Thus, we aimed to determine whether the in-vivo-derived CXCR4-directed whole-body PET signal can also determine GEP-NEC patients with shorter OS.

METHODS

We retrospectively included 16 patients with histologically proven GEP-NEC, who underwent CXCR4-directed PET/CT for staging and therapy planning. We assessed maximum, peak, and mean standardized uptake values as well as whole-body tumor volume (TV) and total-lesion uptake (TLU = SUVmean × TV) using a semi-automatic segmentation tool with a 50% threshold. Association of PET-based biomarkers and OS or radiographic progression-free survival (rPFS; according to RECIST 1.1 criteria) was analyzed using univariable and multivariable cox regression.

RESULTS

Median OS and rPFS was 7.5 and 7 months, respectively. A significant correlation between TV and TLU was found for OS (TV: hazard ratio (HR) 1.007 95% confidence interval (CI) 1.000-1.014, p = 0.0309; TLU: HR 1.002 95% CI 1.000-1.003, p = 0.0350) and rPFS (TV: HR 1.010 95% CI 1.002-1.021; p = 0.0275; TLU: HR 1.002 95% CI 1.000-1.004, p = 0.0329), respectively. No significant correlation with OS or rPFS was found for non-volumetric parameters (p > 0.4). TV remained a significant predictive marker for OS and rPFS in multivariable analysis (OS: HR 1.012 95%, CI 1.003-1.022, p = 0.0084; rPFS: HR 1.009, 95% CI 0.9999-1.019, p = 0.0491), whereas TLU remained only prognostic for OS (HR 1.009, 95% CI 0.9999-1.019, p = 0.0194) but narrowly failed significance for rPFS (p = 0.0559).

CONCLUSION

In-vivo assessment of CXCR4 PET-derived volumetric parameters is predictive for outcome of patients with GEP-NEC and could be used as a risk stratification tool, which detects patients prone to early progression.

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.

Detection support of lesions in patients with prostate cancer using [Formula: see text]-PSMA 1007 PET/CT

PURPOSE

This study proposes a detection support system for primary and metastatic lesions of prostate cancer using [Formula: see text]-PSMA 1007 positron emission tomography/computed tomography (PET/CT) images with non-image information, including patient metadata and location information of an input slice image.

METHODS

A convolutional neural network with condition generators and feature-wise linear modulation (FiLM) layers was employed to allow input of not only PET/CT images but also non-image information, namely, Gleason score, flag of pre- or post-prostatectomy, and normalized z-coordinate of an input slice. We explored the insertion position of the FiLM layers to optimize the conditioning of the network using non-image information.

RESULTS

[Formula: see text]-PSMA 1007 PET/CT images were collected from 163 patients with prostate cancer and applied to the proposed system in a threefold cross-validation manner to evaluate the performance. The proposed system achieved a Dice score of 0.5732 (per case) and sensitivity of 0.8200 (per lesion), which are 3.87 and 4.16 points higher than the network without non-image information.

CONCLUSION

This study demonstrated the effectiveness of the use of non-image information, including metadata of the patient and location information of the input slice image, in the detection of prostate cancer from [Formula: see text]-PSMA 1007 PET/CT images. Improvement in the sensitivity of inactive and small lesions remains a future challenge.

Change in total lesion PSMA (TLP) during [177Lu]Lu-PSMA-617 radioligand therapy predicts overall survival in patients with mCRPC: monocentric evaluation of a prospective registry

PURPOSE

This study investigates imaging response of [177Lu]Lu-PSMA-617 radioligand therapy (RLT) based on the whole-body parameter total lesion PSMA (TLP), derived by PSMA-PET/CT and reflecting the total tumor burden, in patients with metastatic castration-resistant prostate cancer (mCRPC) enrolled in a prospective registry (NCT04833517).

METHODS

A total of n = 102 mCRPC patients received a [68Ga]Ga-PSMA-11 PET/CT at baseline and after two cycles of PSMA-RLT, in which TLP was measured by using a semi-automated tumor segmentation. TLP was defined as the summed products of volume and uptake (∑ Volume × SUVmean) of all tumor lesions. The Kaplan-Meier method was used to determine the most appropriate ∆TLP thresholds for classification into partial remission (PR), stable disease (SD), and progressive disease (PD) regarding overall survival (OS). Furthermore, we analyzed criteria that are also frequently used in established response frameworks, such as the occurrence of new metastases as independent criterion (I) or in combination with change in tumor burden (II), and the change in PSA serum value (III).

RESULTS

For the ∆TLP thresholds -30%/+30% (and also for higher thresholds, -40%/+40% or -50%/+50%), significant differences between all three response categories became apparent (PR/PD: p = 0.001; PR/SD: p = 0.001; SD/PD: p = 0.018). Including the development of new metastases as independent criterion of PD, there was no significant difference in OS between SD and PD (p = 0.455), neither when applied in combination with TLP (p = 0.191). Similarly, significant differentiation between SD and PD was not achieved by PSA serum value (p = 0.973).

CONCLUSION

In the largest monocentric study to date, TLP is shown to be a qualified prognostic biomarker, applying ∆TLP thresholds of -30%/+30%. It significantly differentiated between PR, SD, and PD, whereas other response criteria did not differentiate SD vs. PD. Using TLP, the development of new metastases is not a required information for predicting OS.

Prostate-Specific Membrane Antigen: Gateway to Management of Advanced Prostate Cancer

Prostate-specific membrane antigen (PSMA) as a transmembrane protein is overexpressed by prostate cancer (PC) cells and is accessible for binding antibodies or low-molecular-weight radioligands due to its extracellular portion. Successful targeting of PSMA began with the development of humanized J591 antibody. Due to their faster clearance compared to antibodies, small-molecule radioligands for targeted imaging and therapy of PC have been favored in recent development efforts. PSMA positron emission tomography (PET) imaging has higher diagnostic performance than conventional imaging for initial staging of high-risk PC and biochemical recurrence detection/localization. However, it remains to be demonstrated how to integrate PSMA PET imaging for therapy response assessment and as an outcome endpoint measure in clinical trials. With the recent approval of 177Lu-PSMA-617 by the US Food and Drug Administration for metastatic castration-resistant PC progressing after chemotherapy, the high value of PSMA-targeted therapy was confirmed. Compared to standard of care, PSMA-based radioligand therapy led to a better outcome and a higher quality of life. This review, focusing on the advanced PC setting, provides an overview of different approved and nonapproved PSMA-targeted imaging and therapeutic modalities and discusses the future of PSMA-targeted theranostics, also with an outlook on non-radiopharmaceutical-based PSMA-targeted therapies.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Standardized PSMA-PET Imaging of Advanced Prostate Cancer

Imaging of advanced prostate cancer is a challenging task, as it requires longitudinal characterization of disease extent in a standardized way to enable appropriate treatment selection and evaluation of treatment efficacy. In the last years, prostate-specific membrane antigen (PSMA)-PET/CT has become the reference standard examination for patients with advanced prostate cancer. Together with the rise of PSMA-PET, standardized frameworks for the reporting of image findings have been proposed, eg, the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) and the structured reporting system for PSMA targeted PET imaging (PSMA-RADS) framework. Therefore, recent evidence on PSMA-PET derived tumor volume as useful a biomarker for outcome prognostication and related frameworks will be discussed in the article. The PROMISE framework recommends quantifying the tumor volume per-organ system, which accounts for the fact that the location of the metastases greatly influence its biological aggressiveness. In addition, changes in PSMA-PET derived tumor volume have been shown to be promising biomarkers for response assessment. Limitations of PSMA-PET will also be discussed because the tumor volume might not always be suited for response assessment. As a pitfall of PSMA-based systems, decreasing PSMA-expression might erroneously be interpreted as response to therapy. Also, especially for patients with limited disease, the tumor volume might not be ideal for response assessment. Therefore, various frameworks have been introduced to objectively measure response to therapy with PSMA-PET. Amongst these, the PSMA-PET progression (PPP) criteria and the response evaluation criteria in PSMA (RECIP) are optimized for earlier and later phenotypes of advanced prostate cancer, respectively. Variables needed to determine PPP or RECIP outcome on PSMA-PET are recorded under the umbrella of PROMISE recommendations. In this article, various reporting and response assessment frameworks are explained and discussed. Also, recent evidence for the relevance of PSMA-PET biomarkers for clinical management and outcome prognostication are shown.

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.

Single time point quantitation of cerebral glucose metabolism by FDG-PET without arterial sampling

BACKGROUND

Until recently, quantitation of the net influx of 2-[18F]fluorodeoxyglucose (FDG) to brain (Ki) and the cerebrometabolic rate for glucose (CMRglc) required serial arterial blood sampling in conjunction with dynamic positron emission tomography (PET) recordings. Recent technical innovations enable the identification of an image-derived input function (IDIF) from vascular structures, but are frequently still encumbered by the need for interrupted sequences or prolonged recordings that are seldom available outside of a research setting. In this study, we tested simplified methods for quantitation of FDG-Ki by linear graphic analysis relative to the descending aorta IDIF in oncology patients examined using a Biograph Vision 600 PET/CT with continuous bed motion (Aarhus) or using a recently installed Biograph Vision Quadra long-axial field-of-view (FOV) scanner (Bern).

RESULTS

Correlation analysis of the coefficients of a tri-exponential decomposition of the IDIFs measured during 67 min revealed strong relationships among the total area under the curve (AUC), the terminal normalized arterial integral (theta(52-67 min)), and the terminal image-derived arterial FDG concentration (Ca(52-67 min)). These relationships enabled estimation of the missing AUC from late recordings of the IDIF, from which we then calculated FDG-Ki in brain by two-point linear graphic analysis using a population mean ordinate intercept and the single late frame. Furthermore, certain aspects of the IDIF data from Aarhus showed a marked age-dependence, which was not hitherto reported for the case of FDG pharmacokinetics.

CONCLUSIONS

The observed interrelationships between pharmacokinetic parameters in the IDIF measured during the PET recording support quantitation of FDG-Ki in brain using a single averaged frame from the interval 52-67 min post-injection, with minimal error relative to calculation from the complete dynamic sequences.

Prognostic Value of End-of-Treatment PSMA PET/CT in Patients Treated with 177Lu-PSMA Radioligand Therapy: A Retrospective, Single-Center Analysis

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.

Quantitative Perfusion Imaging with Total-Body PET

Recently, PET systems with a long axial field of view have become the current state of the art. Total-body PET scanners enable unique possibilities for scientific research and clinical diagnostics, but this new technology also raises numerous challenges. A key advantage of total-body imaging is that having all the organs in the field of view allows studying biologic interaction of all organs simultaneously. One of the new, promising imaging techniques is total-body quantitative perfusion imaging. Currently, 15O-labeled water provides a feasible option for quantitation of tissue perfusion at the total-body level. This review summarizes the status of the methodology and the analysis and provides examples of preliminary findings on applications of quantitative parametric perfusion images for research and clinical work. We also describe the opportunities and challenges arising from moving from single-organ studies to modeling of a multisystem approach with total-body PET, and we discuss future directions for total-body imaging.

Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [68Ga]Ga-PSMA-11 PET images

PURPOSE

This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model.

METHODS

One hundred ninety-nine patients were imaged with [68Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTVman) in all patients and using a novel AI method (TTVAI) in a subset (n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen's kappa statistic.

RESULTS

Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96-7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23-11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen's k = 0.31).

CONCLUSION

RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.

Quantitative [68Ga]Ga-PSMA-11 PET biomarkers for the analysis of lesion-level progression in biochemically recurrent prostate cancer: a multicentre study

[68Ga]Ga-PSMA-11 PET has become the standard imaging modality for biochemically recurrent (BCR) prostate cancer (PCa). However, its prognostic value in assessing response at this stage remains uncertain. The study aimed to assess the prognostic significance of radiographic patient-level patterns of progression derived from lesion-level biomarker quantitation in metastatic disease sites. A total of 138 BCR PCa patients with both baseline and follow-up [68Ga]Ga-PSMA-11 PET scans were included in this analysis. Tumour response was quantified at the lesion level using commonly used quantitative parameters (SUVmean, SUVmax, SUVpeak, volume), and patients were classified as systemic, mixed, or no-progression based on these response classifications. A total of 328 matched lesions between baseline and follow-up scans were analysed. The results showed that systemic progressors had a significantly higher risk of death than patients with no progression with SUVmean demonstrating the highest prognostic value (HR = 5.70, 95% CI = 2.63-12.37, p < 0.001, C-Index = 0.69). Moreover, progressive disease as measured by SUVmean using the radiographic PSMA PET Progression Criteria (rPPP) was found to be significantly prognostic for patient overall survival (HR = 3.67, 95% CI = 1.82-7.39, p < 0.001, C-Index = 0.65). This work provides important evidence supporting the prognostic utility of PSMA response quantitation in the BCR setting.

Associations of quantitative whole-body PSMA-PET metrics with PSA progression status under long-term androgen deprivation therapy in prostate cancer patients: a retrospective single-center study

PURPOSE

To evaluate whether quantitative whole-body (WB) PSMA-PET metrics under long-term androgen deprivation therapy (ADT) and/or androgen receptor signaling inhibitors (ARSi) are associated with PSA progression.

METHODS

Patients who underwent at least 2 68Ga-PSMA-11 PET/CT scans between October 2016 and April 2021 (n = 372) and started a new line of ADT ± ARSi between PET1 and PET2 were retrospectively screened for inclusion. We investigated the association between PCWG3-defined PSA progression status at PET2 and the following PSMA-PET parameters: appearance of new lesions on PET2, ≥ 20% increase in WB-PSMA tumor volume (WB-PSMA-VOL), progression of disease (PD) by RECIP 1.0, and ≥ 30% increase in WB-PSMA-SUVmean from PET1 to PET2. Spearman's rank correlation coefficients and Fisher's exact test were used to evaluate the associations.

RESULTS

Thirty-five patients were included: 12/35 (34%) were treated with ADT only and 23/35 (66%) with ARSi ± ADT. The median time between PET1 and PET2 was 539 days. Changes (%) in median PSA levels, WB-PSMA-SUVmean, and WB-PSMA-VOL from PET1 to PET2 were -86%, -23%, and -86%, respectively. WB-PSMA-VOL ≥ 20%, new lesions, RECIP-PD, and WB-PSMA-SUVmean ≥ 30% were observed in 5/35 (14%), 9/35 (26%), 5/35 (14%), and 4/35 (11%) of the whole cohort, in 3/9 (33%), 7/9 (78%), 3/9 (33%), and 2/9 (22%) of patients with PSA progression at PET2, and in 2/26 (8%), 2/26 (8%), 2/26 (8%), and 2/26 (8%) of patients without PSA progression at PET2 (p = 0.058, p < 0.001, p = 0.058, p = 0.238, respectively). Changes in PSA were correlated to percent changes in WB-PSMA-VOL and WB-PSMA-SUVmean (Spearman ρ: 0.765 and 0.633, respectively; p < 0.001).

CONCLUSION

Changes in PSA correlated with changes observed on PSMA-PET, although discordance between PSA and PSMA-PET changes was observed. Further research is necessary to evaluate if PSMA-PET parameters can predict progression-free survival and overall survival and serve as novel endpoints in clinical trials.

Analysing the tumor transcriptome of prostate cancer to predict efficacy of Lu-PSMA therapy

RATIONALE

177Lu-PSMA ([177Lu]Lutetium-PSMA-617) therapy is an effective treatment option for patients with prostate specific membrane antigen (PSMA)-positive metastatic castration-resistant prostate cancer, but still shows a non-responder rate of approximately 30%. Combination regimes of programmed death-ligand 1 (PD-L1) inhibition and concomitant 177Lu-PSMA therapy have been proposed to increase the response rate. However, the interplay of immune landscape and 177Lu-PSMA therapy efficacy is poorly understood.

METHODS

Between March 2018 and December 2021, a total of 168 patients were referred to 177Lu-PSMA therapy in our department and received a mean total dose of 21.9 GBq (three cycles in mean). All patients received baseline PSMA positron emission tomography to assess the PSMA uptake. The histopathological specimen of the primary prostate tumor was available with sufficient RNA passing quality control steps for genomic analysis in n=23 patients. In this subset of patients, tumor RNA transcriptomic analyses assessed 74 immune-related features in total, out of which n=24 signatures were not co-correlated and investigated further for outcome prognostication.

RESULTS

In the subset of patients who received 177Lu-PSMA therapy, PD-L1 was not significantly associated with OS (HR per SD change (95% CI) 0.74 (0.42 to 1.30); SD: 0.18; p=0.29). In contrast, PD-L2 signature was positively associated with longer OS (HR per SD change 0.46 (95% CI 0.29 to 0.74); SD: 0.24; p=0.001; median OS 17.2 vs 5.7 months in higher vs lower PD-L2 patients). In addition, PD-L2 signature correlated with PSA-response (ϱ=-0.46; p=0.04). The PD-L2 signature association with OS was significantly moderated by L-Lactatdehydrogenase (LDH) levels (Cox model interaction p=0.01).

CONCLUSION

Higher PD-L2 signature might be associated with a better response to 177Lu-PSMA therapy and warrants further studies investigating additional immunotherapy. In contrast, PD-L1 was not associated with outcome. The protective effect of PD-L2 signature might be present only in men with lower LDH levels.

SUVmean on baseline [18F]PSMA-1007 PET and clinical parameters are associated with survival in prostate cancer patients scheduled for [177Lu]Lu-PSMA I&T

BACKGROUND

Quantification of [68 Ga]-labeled PSMA PET predicts response in patients with prostate cancer (PC) who undergo PSMA-targeted radioligand therapy (RLT). Given the increasing use [18F]-labeled radiotracers, we aimed to determine whether the uptake derived from [18F]PSMA-1007 PET can also identify responders and to assess its prognostic value relative to established clinical parameters.

METHODS

We retrospectively analyzed 103 patients with metastatic, castration-resistant PC who were treated with [177Lu]Lu-PSMA I&T. We calculated SUVmean, SUVmax, PSMA-avid tumor volume (TV), and total lesion PSMA (defined as PSMA-TV*SUVmean) on pre-therapeutic [18F]PSMA-1007 PET. Laboratory values for hemoglobin, C-reactive protein (CRP), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and alkaline phosphatase (AP) were also collected prior to RLT. We performed univariable Cox regression followed by multivariable and Kaplan-Meier analyses with overall survival (OS) serving as endpoint. Last, we also computed a risk factor (RF) model including all items reaching significance on multivariable analysis to determine whether an increasing number of RFs can improve risk stratification.

RESULTS

A total of 48 patients died and median OS was 16 months. On univariable Cox regression, SUVmean, CRP, LDH, hemoglobin, and the presence of liver metastases were significantly associated with OS. On multivariable Cox regression, the following significant prognostic factors for OS were identified: SUVmean (per unit, HR, 0.91; P = 0.04), the presence of liver metastases (HR, 2.37; P = 0.03), CRP (per mg/dl, HR, 1.13; P = 0.003), and hemoglobin (per g/dl, HR, 0.76; P < 0.01). Kaplan-Meier analysis showed significant separation between patients with a SUVmean below or above a median SUVmean of 9.4 (9 vs 19 months, HR 0.57; P = 0.03). Of note, patients with only one RF (median OS not reached) showed longest survival compared to patients with two (11 months; HR 2.43 95% CI 1.07-5.49, P = 0.02) or more than two RFs (7 months; HR 3.37 95% CI 1.62-7.03, P < 0.001).

CONCLUSION

A lower SUVmean derived from [18F]PSMA-1007, higher CRP, lower hemoglobin, and the presence of liver metastases are associated with reduced OS in patients undergoing RLT. An early RF model also demonstrated that an increasing number of those factors is linked to worse outcome, thereby emphasizing the importance of clinical and imaging parameters for adequate risk stratification.

Impact of [177Lu]Lu-PSMA-617 Radioligand Therapy on Reference Organ Uptake Assessed by [68Ga]Ga-PSMA-11-PET/CT

This study aims to assess the change in uptake to reference organs, including the liver, parotid and salivary glands after radioligand therapy (RLT) with [177Lu]Lu-PSMA-617 in relation to pretreatment imaging metrics. Eighty-five patients with mCRPC underwent [68Ga]Ga-PSMA-11 PET/CT imaging prior to (pre RLT PET) and after (post RLT PET) a median of 3 (IQR 2-6) RLT cycles with [177Lu]Lu-PSMA-617. PSMA-positive tumor burden was stratified into 4 groups based on modified PROMISE criteria (oligofocal, multifocal, disseminated, diffuse). Uptake (SUVmean, SUVmax) in liver tissue, parotid and submandibular glands was measured. A control group was established with 54 patients who had received two separate PET acquisitions following the same protocol (PET1, PET2) within 12 months for localized or oligofocal prostate cancer without RLT in the interim. Baseline uptake values (SUVmean, SUVmax) in parotid (10.8 ± 3.2, 16.8 ± 5.4) and submandibular glands (11.3 ± 2.8, 18.1 ± 4.7) are 2-fold compared to liver uptake (4.9 ± 1.4, 7.7 ± 2.0), with no significant change between PET 1 and PET 2 in the control group. In the RLT group, increasing tumor burden class is significantly associated with decreasing uptake in the liver (p = 0.013), parotid (p < 0.001) and submandibular glands (p < 0.001); this tumor sink effect by respective tumor burden is widely maintained after RLT (p = 0.011, p < 0.001, p < 0.001). RLT has a significant impact on salivary gland uptake with decreasing values per patient in all groups of disease burden change (up to -30.4% in submandibular glands, p < 0.001), while liver tissue shows rising values in patients with declining tumor burden throughout RLT (+18.6%, p = 0.020). Uptake in liver tissue and salivary glands on [68Ga]Ga-PSMA-11 PET/CT imaging is inversely related to tumor burden prior to and following RLT with [177Lu]Lu-PSMA-617. Per patient, salivary gland uptake is further reduced throughout RLT independently from tumor burden, while changes in liver uptake remain burden-dependent. Liver and salivary gland uptake-derived metrics and segmentation thresholds may thus be of limited value when used as reference for response assessment to RLT.

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.

Tumor Volume on PSMA PET as a Prognostic Biomarker in Prostate Cancer Patients Treated With Cabazitaxel

PURPOSE

The aim of this study was to evaluate the prognostic value of 68Ga-labeled prostate-specific membrane antigen (PSMA) PET/CT in metastatic castration-resistant prostate cancer patients receiving second-line chemotherapy with cabazitaxel.

METHODS

All patients with metastatic castration-resistant prostate cancer who underwent a PSMA PET/CT within 8 weeks before initiating the cabazitaxel treatment were retrospectively evaluated. The whole-body PSMA total tumor volume (PSMA-TV) was measured for each patient. Other factors such as prostate-specific antigen, hemoglobin, lactate dehydrogenase, and alkaline phosphatase were recorded. A log-rank cutoff finder was used to define the PSMA-TV optimal cutoff. Survival analyses were performed using Cox regression and Kaplan-Meier methods.

RESULTS

In total, 32 patients were included, receiving a median of 6 cycles of cabazitaxel (range, 2-10). After a median follow-up of 12 months, 28 patients presented disease progression, and 18 died. Baseline PSMA-TV presented a significant association with progression-free survival (PFS) and overall survival (OS; P = 0.035 and P = 0.002, respectively). Optimal PSMA-TV cutoffs were 515 mL for PFS and 473 mL for OS. Patients with low volume presented longer PFS and OS than those with high volume: median PFS, 21 versus 12 weeks, respectively (hazard ratio, 0.33; P = 0.017); and median OS, 24 versus 8.5 months, respectively (hazard ratio, 0.21; P = 0.002). On the multivariable analyses, PSMA-TV remained an independent predictor of OS (P = 0.016).

CONCLUSION

Our results show that total tumor volume measured on PSMA PET/CT is a prognostic biomarker in patients treated with cabazitaxel. High PSMA-TV before treatment initiation is associated with shorter PFS and OS.

Evaluation of thresholding methods for the quantification of [68Ga]Ga-PSMA-11 PET molecular tumor volume and their effect on survival prediction in patients with advanced prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy

PURPOSE

The aim of this study was to systematically evaluate the effect of thresholding algorithms used in computer vision for the quantification of prostate-specific membrane antigen positron emission tomography (PET) derived tumor volume (PSMA-TV) in patients with advanced prostate cancer. The results were validated with respect to the prognostication of overall survival in patients with advanced-stage prostate cancer.

MATERIALS AND METHODS

A total of 78 patients who underwent [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to December 2020 were retrospectively included in this study. [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide therapy, were used for the analysis of thresholding algorithms. All PET images were first analyzed semi-automatically using a pre-evaluated, proprietary software solution as the baseline method. Subsequently, five histogram-based thresholding methods and two local adaptive thresholding methods that are well established in computer vision were applied to quantify molecular tumor volume. The resulting whole-body molecular tumor volumes were validated with respect to the prognostication of overall patient survival as well as their statistical correlation to the baseline methods and their performance on standardized phantom scans.

RESULTS

The whole-body PSMA-TVs, quantified using different thresholding methods, demonstrate a high positive correlation with the baseline methods. We observed the highest correlation with generalized histogram thresholding (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and Sauvola thresholding (r = 0.974, p < 0.001) and the lowest correlation with Multiotsu (r = 0.877, p < 0.001) and Yen thresholding methods (r = 0.878, p < 0.001). The median survival time of all patients was 9.87 months (95% CI [9.3 to 10.13]). Stratification by median whole-body PSMA-TV resulted in a median survival time from 11.8 to 13.5 months for the patient group with lower tumor burden and 6.5 to 6.6 months for the patient group with higher tumor burden. The patient group with lower tumor burden had significantly higher probability of survival (p < 0.00625) in eight out of nine thresholding methods (Fig. 2); those methods were SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p = 0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p = 0.0001), Otsu (p = 0.0053), and Li thresholding (p = 0.0053).

CONCLUSION

Thresholding methods commonly used in computer vision are promising tools for the semiautomatic quantification of whole-body PSMA-TV in [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven thresholding strategy is less arbitrary and less prone to biases than thresholding with predefined values, potentially improving the application of whole-body PSMA-TV as an imaging biomarker.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

BACKGROUND

Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.

METHODS

A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm³ (≥ 99.9% reduction).

RESULTS

The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10^-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054).

CONCLUSION

We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

A Prognostic Risk Score for Prostate Cancer Based on PSMA PET-derived Organ-specific Tumor Volumes

Background Prostate-specific membrane antigen (PSMA) PET has high specificity in localizing primary tumors and metastases in patients with prostate cancer, but the individual overall survival probability is still difficult to estimate. Purpose To develop a prognostic risk score using PSMA PET-derived organ-specific total tumor volumes for predicting overall survival in patients with prostate cancer. Materials and Methods Men with prostate cancer who underwent PSMA PET/CT from January 2014 to December 2018 were evaluated retrospectively. All patients from center A were split into training (80%) and internal validation (20%) cohorts. Randomly selected patients from center B were used for external validation. Organ-specific tumor volumes were automatically quantified from PSMA PET scans by a neural network. A prognostic score was selected using multivariable Cox regression guided by the Akaike information criterion (AIC). The final prognostic risk score fitted on the training set was applied to both validation cohorts. Results A total of 1348 men (mean age, 70 years ± 8 [SD]) were included, with 918 patients in the training cohort, 230 in the internal validation cohort, and 200 in the external validation cohort. The median follow-up time was 55.7 months (IQR, 46.7-65.1 months; >4 years; 429 deaths occurred). A body weight-adjusted prognostic risk score integrating total, bone, and visceral tumor volumes obtained high C index values in the internal (0.82) and external (0.74) validation cohorts, as well as in patients with castration-resistant (0.75) and hormone-sensitive (0.68) disease. The fit of the statistical model for the prognostic score was improved compared with a model containing total tumor volume only (AIC, 3324 vs 3351; likelihood ratio test, P < .001). Calibration plots ascertained good model fit. Conclusion The newly developed risk score that included prostate-specific membrane antigen PET-derived organ-specific tumor volumes had good model fit for predicting overall survival in both internal and external validation cohorts. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Civelek in this issue.

Reversible epigenetic alterations mediate PSMA expression heterogeneity in advanced metastatic prostate cancer

Prostate-specific membrane antigen (PSMA) is an important cell surface target in prostate cancer. There are limited data on the heterogeneity of PSMA tissue expression in metastatic castration-resistant prostate cancer (mCRPC). Furthermore, the mechanisms regulating PSMA expression (encoded by the FOLH1 gene) are not well understood. Here, we demonstrate that PSMA expression is heterogeneous across different metastatic sites and molecular subtypes of mCRPC. In a rapid autopsy cohort in which multiple metastatic sites per patient were sampled, we found that 13 of 52 (25%) cases had no detectable PSMA and 23 of 52 (44%) cases showed heterogeneous PSMA expression across individual metastases, with 33 (63%) cases harboring at least 1 PSMA-negative site. PSMA-negative tumors displayed distinct transcriptional profiles with expression of druggable targets such as MUC1. Loss of PSMA was associated with epigenetic changes of the FOLH1 locus, including gain of CpG methylation and loss of histone 3 lysine 27 (H3K27) acetylation. Treatment with histone deacetylase (HDAC) inhibitors reversed this epigenetic repression and restored PSMA expression in vitro and in vivo. Collectively, these data provide insights into the expression patterns and regulation of PSMA in mCRPC and suggest that epigenetic therapies - in particular, HDAC inhibitors - can be used to augment PSMA levels.

Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index

PURPOSE

Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa.

METHODS

A total of 168 patients from three centers were divided into training, validation, and test groups. Manual annotations of skeletal lesions in [¹⁸F]fluoride PET/CT scans were used to train a CNN. The AI model was evaluated in 26 patients and compared to segmentations by physicians and to a SUV 15 threshold. PET index representing the percentage of skeletal volume taken up by lesions was estimated.

RESULTS

There was no case in which all readers agreed on prevalence of lesions that the AI model failed to detect. PET index by the AI model correlated moderately strong to physician PET index (mean r = 0.69). Threshold PET index correlated fairly with physician PET index (mean r = 0.49). The sensitivity for lesion detection was 65-76% for AI, 68-91% for physicians, and 44-51% for threshold depending on which physician was considered reference.

CONCLUSION

It was possible to develop an AI-based model for automated assessment of PET/CT skeletal tumor burden. The model's performance was superior to using a threshold and provides fully automated calculation of whole-body skeletal tumor burden. It could be further developed to apply to different radiotracers. Objective scan evaluation is a first step toward developing a PET/CT imaging biomarker for PCa skeletal metastases.

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.

Prostate-specific membrane antigen expression predicts recurrence of papillary thyroid carcinoma after total thyroidectomy

BACKGROUND

Prostate-specific membrane antigen (PSMA) overexpression has been observed in the endothelial neovasculature of several solid malignancies. This study aimed to identify PSMA expression in the primary tumor of classical papillary thyroid carcinoma (PTC) and assess the correlation between the degree of PSMA expression and recurrence.

METHODS

We reviewed the electronic medical records of patients who underwent total thyroidectomy and central neck dissection, with or without lateral neck dissection, for classical PTC between 2009 and 2014 at our institution. Recurrence was defined as a structural disease based on histological confirmation on follow-up. Fifty-one patients with the recurrent structural disease were matched, using a propensity score matching method, to patients with no disease evidence during follow-up. Clinicopathological and follow-up data were collected for 102 patients. The monoclonal mouse anti-human PSMA/FOLH1/NAALADase I antibody was used for staining the primary tumor. The score of PSMA expression was classified as negative (< 5% positivity), weak (5-10 % positivity), moderate (11-49% positivity), and strong (more than 50% positivity). Clinicopathological factors were compared between patients with low and high PSMA expression. Moreover, whether the degree of PSMA expression and clinicopathological factors could predict recurrence was investigated. Cox proportional hazard regression models were used to evaluate the risk of recurrence.

RESULTS

There was no significant difference in clinicopathological factors between low (negative or weak) and high (moderate or strong) PSMA expression. Gross extrathyroidal extension (ETE), absence of chronic lymphocytic thyroiditis, and high PSMA expression were all associated with lower recurrence-free survival (RFS) rate in a univariate analysis. In multivariate analysis, gross ETE (hazard ratio [HR], 2.279; 95% confidence interval [CI], 1.257-4.132; p = 0.007) and high PSMA expression (HR, 1.895; 95% CI, 1.073-3.348; p = 0.028) were associated with poor RFS.

CONCLUSIONS

High PSMA expression in the primary tumor was a significant factor in predicting recurrence in classic PTC. PSMA could be a potential biomarker for personalized management for PTC.

Using 68Ga-PSMA-11 PET/CT for Therapy Response Assessment in Patients with Metastatic Castration-Resistant Prostate Cancer: Application of EAU/EANM Recommendations in Clinical Practice

For patients with metastatic castration-resistant prostate cancer (mCRPC), no reliable biomarkers for predicting therapeutic response or assisting in treatment selection and sequencing are currently available. Using the recent European Association of Urology and European Association of Nuclear Medicine recommendations, we aimed to compare response assessment between prostate-specific membrane antigen (PSMA) PET/CT and conventional imaging in mCRPC patients starting first-line treatment with a novel hormonal agent (NHA) and to perform a sequential comparative analysis of PSMA PET/CT-derived parameters after 4 and 12 wk of therapy. Methods: Data from 18 mCRPC patients who started NHA treatment and underwent ⁶⁸Ga-PSMA-11 PET/CT before therapy initiation (baseline), at week 4 (W4), and at week 12 (W12) in addition to conventional imaging (bone scintigraphy, CT) at baseline and W12 were retrospectively included. PET/CT images were quantitatively analyzed for maximum and mean SUV and total PSMA ligand-positive lesions. Comparative analysis of PET/CT-derived parameters was performed, and patients were classified as having nonprogressive disease or progressive disease (PD) according to ⁶⁸Ga-PSMA-11 PET/CT, prostate-specific antigen, and conventional imaging criteria. Results: Treatment response was evaluable by ⁶⁸Ga-PSMA-11 PET/CT in 16 of 18 patients (89%) and by conventional imaging in 11 of 18 patients (61%). Five of 16 patients classified as having PD by ⁶⁸Ga-PSMA-11 PET/CT at W12 had already met progression criteria at W4, and substantial agreement was observed between W4 and W12 (κ, 0.74) ⁶⁸Ga-PSMA-11 PET/CT results. Nonetheless, 2 of 16 patients (13%) were incorrectly classified as having PD because of a flare phenomenon on PSMA PET/CT that disappeared at W12. Conclusion: Volumetric assessments of ⁶⁸Ga-PSMA-11 PET/CT imaging can improve response evaluation in NHA-treated patients with mCRPC. Although early response assessments at W4 need to be approached with caution because of flare, ⁶⁸Ga-PSMA-11 PET/CT imaging at W4 and W12 revealed substantial agreement in therapy response assessments; these findings warrant further investigation to distinguish PD from flare at W4 and help improve the understanding of resistance to therapy.

Time after Synthesis and Time after Injection Do Not Affect Diagnostic Quality of [18F]F-PSMA 1007 PET

Simple Summary

The total number of PSMA-PET-CT examinations for the staging of prostate cancer patients has increased in recent years, following its superior imaging properties. Fluorinated PSMA ligands can be produced in larger amounts, facilitating higher patient throughput compared to the initially developed gallium labelled PSMA-tracers; allowing PET in only a few patients per synthesis. This results in a longer time after, typically early morning, radiochemical synthesis (TaS) of [¹⁸]F-PSMA when the PSMA-PET scan is performed. Moreover, novel fluorinated-PSMA compounds are injected around two hours before PET-CT and the time after injection (TaI) might show significant deviation. [18F]-PSMA PET offers improved detection rates in the proximity of the urinary tract; however, it has the disadvantage of more unspecific uptake, e.g., in the skeleton. This article focusses on the question whether TaS or TaI have an influence on uptake patterns in malignant lesions and healthy tissue.

Abstract

PET imaging using PSMA ligands is increasingly used for staging in prostate cancer patients in different clinical indications. Unlike [⁶⁸Ga]Ga-labeled PSMA ligands, fluorinated compounds can be produced in large amounts; thus, they can be used for a higher number of patients. One concern is that in patients studied a long time after synthesis (TaS) or time after injection (TaI), the specific activity may decline; thus, the signal may be lower in these patients. In this study, we investigated a potential effect of TaS and TaI on image quality. In total, 134 consecutive patients were included in this retrospective analysis on the effect of TaS and TaI on uptake in prostate cancer lesions. All the patients underwent [¹⁸F]F-PSMA-1007 PET-CT from 99 min up to 549 min after tracer quality control. TaS and TaI were compared to the quantitative tumoral uptake parameters SUVmax and SUVpeak. In a second exploratory part of the analysis, TaS and TaI were correlated to a physiological tracer uptake in different organs. TaS and TaI did not affect the SUVmax and SUVpeak in tumor lesions in [¹⁸F]F-PSMA-1007 PET. The physiological uptake in salivary glands, lacrimal glands and the ganglia, spleen and urine was not significantly correlated to TaS or TaI; in contrast to the mean liver uptake, showing a weak, but significant correlation to TaS. The [¹⁸F]F-PSMA-1007 uptake in prostate cancer lesions is not significantly dependent on the TaS and TaI. These results are extremely reassuring when performing [¹⁸F]F-PSMA-1007 PET a considerable time after synthesis.

Reliability of gradient-based segmentation for measuring metabolic parameters influenced by uptake time on 18F-PSMA-1007 PET/CT for prostate cancer

Purpose

To determine an optimal setting for functional contouring and quantification of prostate cancer lesions with minimal variation by evaluating metabolic parameters on 18F-PSMA-1007 PET/CT measured by threshold-based and gradient-based methods under the influence of varying uptake time.

Methods and materials

Dual time point PET/CT was chosen to mimic varying uptake time in clinical setting. Positive lesions of patients who presented with newly diagnosed disease or biochemical recurrence after total prostatectomy were reviewed retrospectively. Gradient-based and threshold-based tools at 40%, 50% and 60% of lesion SUVmax (MIM 6.9) were used to create contours on PET. Contouring was considered completed if the target lesion, with its hottest voxel, was delineated from background tissues and nearby lesions under criteria specific to their operations. The changes in functional tumour volume (FTV) and metabolic tumour burden (MTB, defined as the product of SUVmean and FTV) were analysed. Lesion uptake patterns (increase/decrease/stable) were determined by the percentage change in tumour SUVmax at ±10% limit.

Results

A total of 275 lesions (135 intra-prostatic lesions, 65 lymph nodes, 45 bone lesions and 30 soft tissue lesions in pelvic region) in 68 patients were included. Mean uptake time of early and delayed imaging were 94 and 144 minutes respectively. Threshold-based method using 40% to 60% delineated only 85 (31%), 110 (40%) and 137 (50%) of lesions which all were contoured by gradient-based method. Although the overall percentage change using threshold at 50% was the smallest among other threshold levels in FTV measurement, it was still larger than gradient-based method (median: 50%=-7.6% vs gradient=0%). The overall percentage increase in MTB of gradient-based method (median: 6.3%) was compatible with the increase in tumour SUVmax. Only a small proportion of intra-prostatic lesions (&lt;2%), LN (&lt;4%), bone lesions (0%) and soft tissue lesions (&lt;4%) demonstrated decrease uptake patterns.

Conclusions

With a high completion rate, gradient-based method is reliable for prostate cancer lesion contouring on 18F-PSMA-1007 PET/CT. Under the influence of varying uptake time, it has smaller variation than threshold-based method for measuring volumetric parameters. Therefore, gradient-based method is recommended for tumour delineation and quantification on 18F-PSMA-1007 PET/CT.

Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [¹⁸F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians’ corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.

Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [68Ga]Ga-PSMA-11 PET/CT images

PURPOSE

This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [⁶⁸Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers.

METHODS

Three hundred thirty-seven [⁶⁸Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLV_auto) and total lesional uptake (TLU_auto) were calculated from the automated segmentations, and Kaplan-Meier analysis was used to assess biomarker relationship with patient overall survival.

RESULTS

At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLV_auto and TLU_auto showed they were significantly associated with patient overall survival (both p < 0.005).

CONCLUSION

The fully automated assessment of whole-body [⁶⁸Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival.

TRIAL REGISTRATION

This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.

Diffuse Bone Marrow Involvement of Multiple Myeloma on [18F]PSMA-1007 PET/CT: Is There a Theranostic Potential?

ABSTRACT

A 71-year-old man presented with chronic anemia (hemoglobin 7.3 g/dL). Further serum analyses showed elevated prostate-specific antigen (13 ng/mL), suggestive of prostate cancer. However, ultrasound-guided transrectal sextant biopsy did not find any evidence of prostate cancer. In order to improve guidance of intended repeated biopsy, [18F]prostate-specific membrane antigen (PSMA) 1007 PET/CT was performed, which showed a solitary lesion with strong PSMA expression in the left peripheral zone in the prostate gland. Surprisingly, also a diffuse bone marrow involvement with predominantly osteolytic lesions was observed. This massive osseous tumor burden was clearly discordant to the only relatively mild elevated prostate-specific antigen. The subsequent bone biopsy revealed multiple myeloma. This case does not only highlight a possible pitfall on PSMA PET/CT, but also raises the question on how far PSMA ligands may offer diagnostic and therapeutic potential in multiple myeloma.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool

Background

Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry.

Methods

We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989–2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models’ clinical utility. We characterized the magnitude and direction of model features.

Results

The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV).

Conclusions

We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

Reduced Segmentation of Lesions Is Comparable to Whole-Body Segmentation for Response Assessment by PSMA PET/CT: Initial Experience with the Keyhole Approach

(1) Background: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET)-derived parameters, such as the commonly used standardized uptake value (SUV) and PSMA-positive tumor volume (PSMA-TV), have been proposed for response assessment in metastatic prostate cancer (PCa) patients. However, the calculation of whole-body PSMA-TV remains a time-consuming procedure. We hypothesized that it may be possible to quantify changes in PSMA-TV by considering only a limited number of representative lesions. (2) Methods: Sixty-five patients classified into different disease stages were assessed by PSMA PET/CT for staging and restaging after therapy. Whole-body PSMA-TV and whole-body SUVmax were calculated. We then repeated this calculation only including the five or ten hottest or largest lesions. The corresponding serum levels of prostate-specific antigen (PSA) were also determined. The derived delta between baseline and follow-up values provided the following parameters: ΔSUVmaxall, ΔSUVmax10, ΔSUVmax5, ΔPSMA-TVall, ΔPSMA-TV10, ΔPSMA-TV5, ΔPSA. Finally, we compared the findings from our whole-body segmentation with the results from our keyhole approach (focusing on a limited number of lesions) and correlated all values with the biochemical response (ΔPSA). (3) Results: Among patients with metastatic hormone-sensitive PCa (mHSPC), none showed a relevant deviation for ΔSUVmax10/ΔSUVmax5 or ΔPSMA-TV10/ΔPSMA-TV5 compared to ΔSUVmaxall and ΔPSMA-TVall. For patients treated with taxanes, up to 6/21 (28.6%) showed clinically relevant deviations between ΔSUVmaxall and ΔSUVmax10 or ΔSUVmax5, but only up to 2/21 (9.5%) patients showed clinically relevant deviations between ΔPSMA-TVall and ΔPSMA-TV10 or ΔPSMA-TV5. For patients treated with radioligand therapy (RLT), up to 5/28 (17.9%) showed clinically relevant deviations between ΔSUVmaxall and ΔSUVmax10 or ΔSUVmax5, but only 1/28 (3.6%) patients showed clinically relevant deviations between ΔPSMA-TVall and ΔPSMA-TV10 or ΔPSMA-TV5. The highest correlations with ΔPSA were found for ΔPSMA-TVall (r ≥ 0.59, p ≤ 0.01), followed by ΔPSMA-TV10 (r ≥ 0.57, p ≤ 0.01) and ΔPSMA-TV5 (r ≥ 0.53, p ≤ 0.02) in all cohorts. ΔPSA only correlated with ΔSUVmaxall (r = 0.60, p = 0.02) and with ΔSUVmax10 (r = 0.53, p = 0.03) in the mHSPC cohort, as well as with ΔSUVmaxall (r = 0.51, p = 0.01) in the RLT cohort. (4) Conclusion: Response assessment using PSMA-TV with a reduced number of lesions is feasible, and may allow for a simplified evaluation process for PSMA PET/CT.

Bone marrow impairment during early [177Lu]PSMA-617 radioligand therapy: Haematotoxicity or tumour progression?

Background

The recent phase III VISION-trial confirms the treatment efficacy of radioligand therapy with [¹⁷⁷Lu]PSMA-617 (PSMA-RLT) in metastatic castration-resistant prostate cancer (mCRPC). In PSMA-RLT, the relatively low absorbed bone marrow dose allows for multiple therapy cycles with relatively low risk of haematological adverse events (hAE). However, as disease progression itself may be a cause of bone marrow impairment, the aim of this study was to assess potential relations between impairment of haematological status and response to PSMA-RLT.

Methods

In this retrospective analysis, haematological parameters (HP) of 64 patients with mCRPC were systematically acquired over two cycles (12–16 weeks) of PSMA-RLT from baseline to restaging. Changes in HP were analysed qualitatively (CTCAE 5.0) and quantitatively. The HP changes from baseline were compared to quantitative and qualitative biochemical and imaging response, using PCWG3 and PROMISE criteria.

Results

All grade 3/4 hAE observed were associated with disseminated or diffuse bone involvement as well as biochemical non-response at restaging. Quantitatively, at baseline, HP inversely correlated with biochemical and volumetric (on PET) tumour burden as well as bone involvement pattern (p ≤ 0.043). Among patients with disseminated or diffuse bone involvement, percentage changes in HP (%HP) at restaging inversely correlated with serological and imaging tumour burden (p ≤ 0.017). Biochemical non-responders showed a significant decrease in %HP (p ≤ 0.001) while HP in biochemical responders remained stable (p ≥ 0.079).

Conclusion

During early cycles of PSMA-RLT, qualitative and quantitative bone marrow impairment appears to be closely associated with osseous tumour burden as only patients with advanced bone involvement and non-response to therapy exhibited high-grade haematological adverse events, showing a significant decline of haematological parameters. This implies that in patients with advanced mCRPC, non-response to PSMA-RLT may be a major cause of bone marrow impairment during early treatment cycles.

German Clinical Trial Register DRKS00013665. Registered 28 December 2017, retrospectively registered (www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013665)

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.

Prediction of early biochemical response after 177Lu-PSMA radioligand therapy with 68Ga-PSMA PET, a different perspective with quantitative parameters

OBJECTIVE

In this study, our aim was to evaluate the relationship of the quantitative data obtained from pretreatment 68Ga prostate-specific membrane antigen (PSMA) PET-computerized tomography (PET/CT) with treatment response of the patients with the diagnosis of metastatic castrationresistant prostate cancer (mCRPC) who received 177Lu-PSMA radioligand therapy (RLT).

METHODS

The patients who were given three or four cycles of 177Lu-PSMA RLT between January 2016 and June 2018 were evaluated retrospectively. Volumetric data; PSMA tumor volume (TV) and total lesion (TL) PSMA, were obtained from 68Ga-PSMA PET/CT for whole (PSMA-TVT and TL-PSMAT). The distance between the two furthest lesions (Dmax) was calculated. Posttreatment early prostate-specific antigen (PSA) values on the fourteenth day after treatment were obtained. According to the PSA responses, the patients were divided into two groups as progressed and nonprogressed. In univariate analysis, the relationship of PET quantitative data with biochemical response groups was evaluated with Mann-Whitney U test. Logistic regression was used in multivariate analysis.

RESULTS

A total of 38 patients were included in the study. In univariate analysis, Dmax, PSMA-TVT and TL-PSMAT values were obtained at lower levels in the progressed group. In multivariate analysis, only Dmax was found to be a prognostic factor in predicting early biochemical response.

CONCLUSION

Dmax is the most prognostic parameter in predicting the early biochemical response in patients with mCRPC; high total tumor volume and burden are also parameters that give us an idea about the response to treatment. The success rate will be higher if 177Lu-PSMA RLT treatment is planned for patients with higher tumor volume and spread.

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.

Implications of physics, chemistry and biology for dosimetry calculations using theranostic pairs

Theranostics is an emerging paradigm that combines imaging and therapy in order to personalize patient treatment. In nuclear medicine, this is achieved by using radiopharmaceuticals that target identical molecular targets for both imaging (using emitted gamma rays) and radiopharmaceutical therapy (using emitted beta, alpha or Auger-electron particles) for the treatment of various diseases, such as cancer. If the therapeutic radiopharmaceutical cannot be imaged quantitatively, a “theranostic pair” imaging surrogate can be used to predict the absorbed radiation doses from the therapeutic radiopharmaceutical. However, theranostic dosimetry assumes that the pharmacokinetics and biodistributions of both radiopharmaceuticals in the pair are identical or very similar, an assumption that still requires further validation for many theranostic pairs. In this review, we consider both same-element and different-element theranostic pairs and attempt to determine if factors exist which may cause inaccurate dose extrapolations in theranostic dosimetry, either intrinsic (e.g. chemical differences) or extrinsic (e.g. injecting different amounts of each radiopharmaceutical) to the radiopharmaceuticals. We discuss the basis behind theranostic dosimetry and present common theranostic pairs and their therapeutic applications in oncology. We investigate general factors that could create alterations in the behavior of the radiopharmaceuticals or the quantitative accuracy of imaging them. Finally, we attempt to determine if there is evidence showing some specific pairs as suitable for theranostic dosimetry. We show that there are a variety of intrinsic and extrinsic factors which can significantly alter the behavior among pairs of radiopharmaceuticals, even if they belong to the same chemical element. More research is needed to determine the impact of these factors on theranostic dosimetry estimates and on patient outcomes, and how to correctly account for them.

First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients

PURPOSE

To investigate the kinetics of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) by positron emission tomography (PET) in multiple organs and test the feasibility of total-body parametric imaging using an image-derived input function (IDIF).

METHODS

Twenty-four oncological patients underwent dynamic ¹⁸F-FDG scans lasting 65 min using a long  axial FOV (LAFOV) PET/CT system. Time activity curves (TAC) were extracted from semi-automated segmentations of multiple organs, cerebral grey and white matter, and from vascular structures. The tissue and tumor lesion TACs were fitted using an irreversible two-tissue compartment (2TC) and a Patlak model. Parametric images were also generated using direct and indirect Patlak methods and their performances were evaluated.

RESULTS

We report estimates of kinetic parameters and metabolic rate of glucose consumption (MR_FDG) for different organs and tumor lesions. In some organs, there were significant differences between MR_FDG values estimated using 2TC and Patlak models. No statistically significant difference was seen between MR_FDG values estimated using 2TC and Patlak methods in tumor lesions (paired t-test, P = 0.65). Parametric imaging showed that net influx (K_i) images generated using direct and indirect Patlak methods had superior tumor-to-background ratio (TBR) to standard uptake value (SUV) images (3.1- and 3.0-fold mean increases in TBR_mean, respectively). Influx images generated using the direct Patlak method had twofold higher contrast-to-noise ratio in tumor lesions compared to images generated using the indirect Patlak method.

CONCLUSION

We performed pharmacokinetic modelling of multiple organs using linear and non-linear models using dynamic total-body ¹⁸F-FDG images. Although parametric images did not reveal more tumors than SUV images, the results confirmed that parametric imaging furnishes improved tumor contrast. We thus demonstrate the feasibility of total-body kinetic modelling and parametric imaging in basic research and oncological studies. LAFOV PET can enhance dynamic imaging capabilities by providing high sensitivity parametric images and allowing total-body pharmacokinetic analysis.

Clinical Use of PET/MR in Oncology: An Update

The combination of PET and MRI is one of the recent advances of hybrid imaging. Yet to date, the adoption rate of PET/MRI systems has been rather slow. This seems to be partially caused by the high costs of PET/MRI systems and the need to verify an incremental benefit over PET/CT or sequential PET/CT and MRI. In analogy to PET/CT, the MRI part of PET/MRI was primarily used for anatomical imaging. Though this can be advantageous, for example in diseases where the superior soft tissue contrast of MRI is highly appreciated, the sole use of MRI for anatomical orientation lessens the potential of PET/MRI. Consequently, more recent studies focused on its multiparametric potential and employed diffusion weighted sequences and other functional imaging sequences in PET/MRI. This integration puts the focus on a more wholesome approach to PET/MR imaging, in terms of releasing its full potential for local primary staging based on multiparametric imaging and an included one-stop shop approach for whole-body staging. This approach as well as the implementation of computational analysis, in terms of radiomics analysis, has been shown valuable in several oncological diseases, as will be discussed in this review article.

Early PSA Change after [177Lu]PSMA-617 Radioligand Therapy as a Predicator of Biochemical Response and Overall Survival

Simple Summary

Radioligand therapy with [¹⁷⁷Lu]PSMA-617 (PSMA-RLT) is a promising therapeutic option for metastatic castration-resistant prostate cancers (mCRPC), as its clinical relevance has recently been confirmed in the phase III VISION-trial. As prostate-specific antigen (PSA) plays an important role in the response evaluation of this therapy, and the aim of this study was to prospectively assess the prognostic value of early PSA measurements. We found PSA changes as early as four weeks after the first administration of PSMA-RLT to be predictive of both long-term biochemical and PET imaging response, as well as overall survival. We then evaluated relevant predictive thresholds in PSA change at that time point, as the early detection of long-term (non-)response to PSMA-RLT can be of great benefit in the clinical management of terminally ill mCRPC-patients.

Abstract

Purpose: Radioligand therapy with [¹⁷⁷Lu]PSMA-617 (PSMA-RLT) is a promising therapeutic option for metastatic castration-resistant prostate cancer (mCPRP). This study assessed the prognostic value of early PSA measurements during PSMA-RLT. Methods: 27 patients with mCRPC scheduled for PSMA-RLT were prospectively enrolled for a serial short-interval PSA-assessment. Change in PSA (∆%PSA) during two treatment cycles was correlated with biochemical response (BR) and change in tumor volume on PET (TV) after 16 weeks (w16), as well as overall survival (OS). PCWG3 criteria and the recently recommended threshold of ∆%PSA ≤ −30% were assessed for their predictive value. Results: ∆%PSA first correlated with BR, TV and OS after 4 weeks (c1w4). At c1w4, ∆%PSA ≤ −30% was associated with the biochemical response at w16 (p = 0.003) and a longer median OS (p = 0.025), whereas the PCWG3-derived threshold of ∆%PSA ≤ −50% showed no such correlation. In contrast, ∆%PSA ≥ 25% at c1w4 was associated with biochemical progression at w16 (p = 0.003) and a shorter median OS (p < 0.001). Conclusion: PSA changes as early as four weeks after PSMA-RLT allow a significant prediction of later biochemical and PET-based imaging response, as well as OS. At this early time point, a more lenient threshold for a PSA decrease of at least 30% appears better-suited for the prediction of a positive biochemical response and longer OS. In contrast, the PCWG3-derived threshold for PSA increase (+25%) reliably anticipates biochemical progression and shorter OS.

A new nomogram for the prediction of bone metastasis in patients with prostate cancer

Objective

This study aimed to establish a new prognostic nomogram for bone metastasis in patients with prostate cancer (PCa).

Methods

This study retrospectively analyzed clinical data from 332 patients diagnosed with PCa from 2014 to 2019, and patients were randomly divided into a training set (n = 184) and a validation set (n = 148). Multivariate logistic regression analysis was used to establish a prediction model based on the training set, and a nomogram was constructed for visual presentation. The calibration, discrimination and clinical usefulness of the model were evaluated using the validation set.

Results

Total prostate-specific antigen, clinical tumor stage, Gleason score, prostate volume, red cell distribution width and serum alkaline phosphatase were selected as predictors to develop a prediction model of bone metastasis. After evaluation, the model developed in our study exhibited good discrimination (area under the curve: 0.958; 95% confidence interval: 0.93–0.98), calibration (U = 0.01) and clinical usefulness.

Conclusions

The new proposed model showed high accuracy for bone metastasis prediction in patients with PCa and good clinical usefulness.