EBONI: A Tool for Automated Quantification of Bone Metastasis Load in PSMA PET/CT

Measurements of peri-prostatic adipose tissue by MRI predict bone metastasis in patients with newly diagnosed prostate cancer

OBJECTIVES

To investigate the role of MRI measurements of peri-prostatic adipose tissue (PPAT) in predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa).

METHODS

We performed a retrospective study on 156 patients newly diagnosed with PCa by prostate biopsy between October 2010 and November 2022. Clinicopathologic characteristics were collected. Measurements including PPAT volume and prostate volume were calculated by MRI, and the normalized PPAT (PPAT volume/prostate volume) was computed. Independent predictors of BM were determined by univariate and multivariate logistic regression analysis, and a new nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves were used to estimate predictive performance.

RESULTS

PPAT and normalized PPAT were associated with BM (P<0.001). Normalized PPAT positively correlated with clinical T stage(cT), clinical N stage(cN), and Grading Groups(P<0.05). The results of ROC curves indicated that PPAT and normalized PPAT had promising predictive value for BM with the AUC of 0.684 and 0.775 respectively. Univariate and multivariate analysis revealed that high normalized PPAT, cN, and alkaline phosphatase(ALP) were independently predictors of BM. The nomogram was developed and the concordance index(C-index) was 0.856.

CONCLUSIONS

Normalized PPAT is an independent predictor for BM among with cN, and ALP. Normalized PPAT may help predict BM in patients with newly diagnosed prostate cancer, thus providing adjunctive information for BM risk stratification and bone scan selection.

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.

Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images

Non-small cell lung cancer (NSCLC) patients with the metastatic spread of disease to the bone have high morbidity and mortality. Stereotactic ablative body radiotherapy increases the progression free survival and overall survival of these patients with oligometastases. FDG-PET/CT, a functional imaging technique combining positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) and computer tomography (CT) provides improved staging and identification of treatment response. It is also associated with reduction in size of the radiotherapy tumour volume delineation compared with CT based contouring in radiotherapy, thus allowing for dose escalation to the target volume with lower doses to the surrounding organs at risk. FDG-PET/CT is increasingly being used for the clinical management of NSCLC patients undergoing radiotherapy and has shown high sensitivity and specificity for the detection of bone metastases in these patients. Here, we present a software tool for detection, delineation and quantification of bone metastases using FDG-PET/CT images. The tool extracts standardised uptake values (SUV) from FDG-PET images for auto-segmentation of bone lesions and calculates volume of each lesion and associated mean and maximum SUV. The tool also allows automatic statistical validation of the auto-segmented bone lesions against the manual contours of a radiation oncologist. A retrospective review of FDG-PET/CT scans of more than 30 candidate NSCLC patients was performed and nine patients with one or more metastatic bone lesions were selected for the present study. The SUV threshold prediction model was designed by splitting the cohort of patients into a subset of 'development' and 'validation' cohorts. The development cohort yielded an optimum SUV threshold of 3.0 for automatic detection of bone metastases using FDG-PET/CT images. The validity of the derived optimum SUV threshold on the validation cohort demonstrated that auto-segmented and manually contoured bone lesions showed strong concordance for volume of bone lesion (r = 0.993) and number of detected lesions (r = 0.996). The tool has various applications in radiotherapy, including but not limited to studies determining optimum SUV threshold for accurate and standardised delineation of bone lesions and in scientific studies utilising large patient populations for instance for investigation of the number of metastatic lesions that can be treated safety with an ablative dose of radiotherapy without exceeding the normal tissue toxicity.

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.

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.

Automated bone healing evaluation: New approach to histomorphometric analysis

This study aimed to assess different approaches for bone healing evaluation on histological images and to introduce a new automatic evaluation method based on segmentation with distinct thresholds. We evaluated the hyperbaric oxygen therapy (HBO) effects on bone repair in type 1 diabetes mellitus rats. Twelve animals were divided into four groups (n = 3): non-diabetic, non-diabetic + HBO, diabetic, and diabetic + HBO. Diabetes was induced by intravenous administration of streptozotocin (50 mg/kg). Bone defects were created in femurs and HBO was immediately started at one session/day. After 7 days, the animals were euthanized, femurs were removed, demineralized, and embedded in paraffin. Histological sections were stained with hematoxylin and eosin (HE) and Mallory's trichrome (MT), and evaluated using three approaches: (1) conventional histomorphometric analysis (HE images) using a 144-point grid to quantify the bone matrix; (2) a semi-automatic method based on bone matrix segmentation to assess the bone matrix percentage (MT images); and (3) automatic approach, with the creation of a plug-in for ImageJ software. The time required to perform the analysis in each method was measured and subjected to Bland-Altman statistical analysis. All three methods were satisfactory for measuring bone formation and were not statistically different. The automatic approach reduced the working time compared to visual grid and semi-automated method (p < .01). Although histological evaluation of bone healing was performed successfully using all three methods, the novel automatic approach significantly shortened the time required for analysis and had high accuracy.

The VISION Forward: Recognition and Implication of PSMA-/18F-FDG+ mCRPC

Metastatic castration resistant prostate cancer (mCRPC) is incurable. The expression of the transmembrane protein prostate-specific membrane antigen (PSMA) is markedly increased in most mCRPC lesions. PSMA has been recognized as a viable biologic target for imaging and radionuclide therapy (theranostics) in mCRPC. The PET agents 68Ga-PSMA-11 and 18F-DCFPyL have recently been approved for imaging evaluation of patients with suspected metastasis who are candidates for initial definitive therapy and patients with suspected recurrence based on elevated serum prostate-specific antigen level. Radioligand therapy (RLT) with 177Lu-PSMA-617 (177Lu-vipivotide tetraxetan, Pluvicto, Novartis/AAA) was approved on March 23, 2022, based on the favorable results of the VISION trial. It has been recognized that PET imaging of PSMA expression and glucose metabolism (with 18F-FDG) provides a more comprehensive assessment of the tumor burden and heterogeneity. However, there are many unresolved issues that surround whether or not imaging with 18F-FDG PET is advantageous in the clinical setting of PSMA RLT in mCRPR.

Application of SPECT and PET / CT with computer-aided diagnosis in bone metastasis of prostate cancer: a review

Bone metastasis has a significant influence on the prognosis of prostate cancer(PCa) patients. In this review, we discussed the current application of PCa bone metastasis diagnosis with single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) computer-aided diagnosis(CAD) systems. A literature search identified articles concentrated on PCa bone metastasis and PET/CT or SPECT CAD systems using the PubMed database. We summarized the previous studies focused on CAD systems and manual quantitative markers calculation, and the coincidence rate was acceptable. We also analyzed the quantification methods, advantages, and disadvantages of CAD systems. CAD systems can detect abnormal lesions of PCa patients' 99mTc-MDP-SPECT, 18F-FDG-PET/CT, 18F-NaF-PET/CT, and 68 Ga-PSMA PET/CT images automated or semi-automated. CAD systems can also calculate the quantitative markers, which can quantify PCa patients' whole-body bone metastasis tumor burden accurately and quickly and give a standardized and objective result. SPECT and PET/CT CAD systems are potential tools to monitor and quantify bone metastasis lesions of PCa patients simply and accurately, the future clinical application of CAD systems in diagnosing PCa bone metastasis lesions is necessary and feasible.

Prostate-specific membrane antigen modulates the progression of prostate cancer by regulating the synthesis of arginine and proline and the expression of androgen receptors and Fos proto-oncogenes

The expression of prostate-specific membrane antigen (PSMA) is strikingly upregulated during oncogenesis and prostate cancer (PCa) progression, but the functions of this antigen in PCa remain unclear. Here, we constructed PSMA-knockdown LNCaP and 22rv1 cell lines and performed metabonomic and transcriptomic analyses to determine the effects of PSMA on PCa metabolism and transcription. The metabolism of arginine and proline was detected using specific kits. The mRNA and protein expression levels of the identified differentially expressed genes were quantified by RT-qPCR and Western blotting. The proliferation of each cell line was evaluated through CCK-8, EdU and colony formation assays. The migration and invasion abilities of each cell line were detected using wound healing and transwell assays, respectively. PSMA knockdown led to metabolic disorder and abnormal transcription in PCa and resulted in inhibition of the proliferation and metastasis of PCa cells in vitro and in vivo. The depletion of PSMA also promoted the biosynthesis of arginine and proline, inhibited the expression of AR and PSA, and induced the expression of c-Fos and FosB. PSMA plays an important role in the metabolism, proliferation and metastasis of human PCa and may be a promising therapeutic target.

Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans

The importance of machine learning (ML) in the clinical environment increases constantly. Differentiation of pathological from physiological tracer-uptake in positron emission tomography/computed tomography (PET/CT) images is considered time-consuming and attention intensive, hence crucial for diagnosis and treatment planning. This study aimed at comparing and validating supervised ML algorithms to classify pathological uptake in prostate cancer (PC) patients based on prostate-specific membrane antigen (PSMA)-PET/CT. Retrospective analysis of ⁶⁸Ga-PSMA-PET/CTs of 72 PC patients resulted in a total of 77 radiomics features from 2452 manually delineated hotspots for training and labeled pathological (1629) or physiological (823) as ground truth (GT). As the held-out test dataset, 331 hotspots (path.:128, phys.: 203) were delineated in 15 other patients. Three ML classifiers were trained and ranked to assess classification performance. As a result, a high overall average performance (area under the curve (AUC) of 0.98) was achieved, especially to detect pathological uptake (0.97 mean sensitivity). However, there is still room for improvement to detect physiological uptake (0.82 mean specificity), especially for glands. The ML algorithm applied to manually delineated lesions predicts hotspot labels with high accuracy on unseen data and may be an important tool to assist in clinical diagnosis.

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

Purpose

In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers.

Methods

In 173 subjects imaged with ⁶⁸Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of ¹⁸F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework.

Results

In the development set, including ¹⁸F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with ¹⁸F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1–87.8] for identification of suspicious uptake sites, 77% (CI: 70.0–83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage.

Conclusion

The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body ⁶⁸Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05473-2.

Semi-automatic evaluation of baseline whole-body tumor burden as an imaging biomarker of 68Ga-PSMA-11 PET/CT in newly diagnosed prostate cancer

OBJECTIVES

The prognostic value of baseline tumor burden of prostate cancer was rarely studied. We aimed to evaluate the whole-body tumor burden of ⁶⁸Ga- prostate specific membrane antigen-HBED-CC (⁶⁸Ga-PSMA-11) PET/CT in newly diagnosed prostate cancer semi-automatically, and explore its preliminary application in predicting prognosis.

METHODS

Similar to metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of ¹⁸F-FDG PET/CT, ⁶⁸Ga-PSMA-11 PET/CT tumor burden parameters including whole-body PSMA tumor volume (wbPSMA-TV) and whole-body total lesions PSMA uptake (wbTL-PSMA) were acquired semi-automatically. The intra-observer and inter-observer reliability was analyzed. The relationship between tumor burden and prostate-specific antigen (PSA) value or Gleason score was investigated. The preliminary application of tumor burden in predicting progression-free survival (PFS) was explored.

RESULTS

Fifty-nine newly diagnosed prostate cancer patients were retrospectively analyzed. Semi-automatic quantification of whole-body tumor burden had excellent intra-observer and inter-observer consistency [all intra-class correlation coefficient (ICC) > 0.990]. wbPSMA-TV and wbTL-PSMA were 32.6 (range 1.0-3968.2) cm³ and 161.9 (range 6.0-24971.7), respectively. wbPSMA-TV and wbTL-PSMA correlated with PSA (r = 0.858, p < 0.001; r = 0.879, p < 0.001) and Gleason score (r = 0.793, p < 0.001; r = 0.805, p < 0.001) significantly. In univariate analysis, wbPSMA-TV, wbTL-PSMA, SUV_max, SUV_peak, SUV_mean, PSMA-TV, TL-PSMA of primary tumor, fPSA and Gleason score were independent significant predictors of PFS (all p < 0.05). Moreover, in multivariate analysis, wbTL-PSMA [hazard ratio (HR): 1.001, p = 0.014] and Gleason score (HR: 5.124, p = 0.031) can significantly predict progression-free prognosis.

CONCLUSIONS

As imaging biomarkers, wbPSMA-TV and wbTL-PSMA correlated with clinical characteristics significantly. High wbTL-PSMA or Gleason score was associated with shorter PFS of newly diagnosed prostate cancer independently.

Metabolic Tumour Volume from PSMA PET/CT Scans of Prostate Cancer Patients during Chemotherapy-Do Different Software Solutions Deliver Comparable Results?

(1) Background: Prostate-specific membrane antigen (PSMA)-derived tumour volume (PSMA-TV) and total lesion PSMA (TL-PSMA) from PSMA PET/CT scans are promising biomarkers for assessing treatment response in prostate cancer (PCa). Currently, it is unclear whether different software tools for assessing PSMA-TV and TL-PSMA produce comparable results. (2) Methods: ⁶⁸Ga-PSMA PET/CT scans from n = 21 patients with castration-resistant PCa (CRPC) receiving chemotherapy were identified from our single-centre database. PSMA-TV and TL-PSMA were calculated with Syngo.via (Siemens) as well as the freely available Beth Israel plugin for FIJI (Fiji Is Just ImageJ) before and after chemotherapy. While statistical comparability was illustrated and quantified via Bland-Altman diagrams, the clinical agreement was estimated by matching PSMA-TV, TL-PSMA and relative changes of both variables during chemotherapy with changes in serum PSA (ΔPSA) and PERCIST (Positron Emission Response Criteria in Solid Tumors). (3) Results: Comparing absolute PSMA-TV and TL-PSMA as well as Bland-Altman plotting revealed a good statistical comparability of both software algorithms. For clinical agreement, classifying therapy response did not differ between PSMA-TV and TL-PSMA for both software solutions and showed highly positive correlations with BR. (4) Conclusions: due to the high levels of statistical and clinical agreement in our CRPC patient cohort undergoing taxane chemotherapy, comparing PSMA-TV and TL-PSMA determined by Syngo.via and FIJI appears feasible.

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

Prostate-specific membrane antigen (PSMA)-targeting PET imaging is becoming the reference standard for prostate cancer staging, especially in advanced disease. Yet, the implications of PSMA PET-derived whole-body tumor volume for overall survival are poorly elucidated to date. This might be because semiautomated quantification of whole-body tumor volume as a PSMA PET biomarker is an unmet clinical challenge. Therefore, in the present study we propose and evaluate a software that enables the semiautomated quantification of PSMA PET biomarkers such as whole-body tumor volume. Methods: The proposed quantification is implemented as a research prototype. PSMA-accumulating foci were automatically segmented by a percental threshold (50% of local SUV_max). Neural networks were trained to segment organs in PET/CT acquisitions (training CTs: 8,632, validation CTs: 53). Thereby, PSMA foci within organs of physiologic PSMA uptake were semiautomatically excluded from the analysis. Pretherapeutic PSMA PET/CTs of 40 consecutive patients treated with ¹⁷⁷Lu-PSMA-617 were evaluated in this analysis. The whole-body tumor volume (PSMA_TV50), SUV_max, SUV_mean, and other whole-body imaging biomarkers were calculated for each patient. Semiautomatically derived results were compared with manual readings in a subcohort (by 1 nuclear medicine physician). Additionally, an interobserver evaluation of the semiautomated approach was performed in a subcohort (by 2 nuclear medicine physicians). Results: Manually and semiautomatically derived PSMA metrics were highly correlated (PSMA_TV50: R ² = 1.000, P < 0.001; SUV_max: R ² = 0.988, P < 0.001). The interobserver agreement of the semiautomated workflow was also high (PSMA_TV50: R ² = 1.000, P < 0.001, interclass correlation coefficient = 1.000; SUV_max: R ² = 0.988, P < 0.001, interclass correlation coefficient = 0.997). PSMA_TV50 (ml) was a significant predictor of overall survival (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006 P = 0.004). Conclusion: PSMA_TV50 is a promising PSMA PET biomarker that is reproducible and easily quantified by the proposed semiautomated software. Moreover, PSMA_TV50 is a significant predictor of overall survival in patients with advanced prostate cancer who receive ¹⁷⁷Lu-PSMA-617 therapy.

qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using ⁶⁸Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic ⁶⁸Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUV_mean, and PSMA SUV_max Twenty ⁶⁸Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUV_mean, and SUV_max were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in ⁶⁸Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

Theranostics for Advanced Prostate Cancer: Current Indications and Future Developments

CONTEXT

Advanced prostate cancer (PCa) is a prominent cause of cancer death in men; positron emission tomography (PET) imaging may play a relevant role in detecting metastases and thus allowing a more tailored therapy in these patients. Radioligand therapy (RLT) may also gain relevance as a treatment strategy in advanced disease.

OBJECTIVE

The aim of this review is to highlight how the recently developed theranostic processes may become a part of both the available diagnostic and the therapy arsenal in advanced PCa patients.

EVIDENCE ACQUISITION

An expert panel of nuclear medicine physicians and a urologist, highly experienced in the fields of radionuclide imaging and RLT in advanced PCa, performed a nonsystematic review of the current indications, performance, limitations, and potential future developments of the currently available options in PCa theranostics.

EVIDENCE SYNTHESIS

Among PET radiotracers, prostate-specific membrane antigen (PSMA)-based compounds in advanced PCa are the focus of a continuously growing interest, mostly due to their potential relevance as theranostic agents. The impact of PSMA-based PET/computed tomography imaging on treatment strategies and prognosis is promising, but still not unquestionably clear. Potential applications may include a role as a gatekeeper to PSMA-directed RLT, as well as monitoring the spread of systemic disease. Currently, initial results seem to substantiate the role of PSMA-directed RLT in terms of feasibility and efficacy.

CONCLUSIONS

PSMA is a promising molecule for both imaging and therapy in advanced PCa patients; nevertheless, further studies are needed to investigate its role and to determine the impact of its side effects and its overall strategy outcome.

PATIENT SUMMARY

Prostate-specific membrane antigen (PSMA), a protein, is highly expressed on prostate cancer cells. The possibility to perform diagnostic imaging and subsequently administer therapies by the means of the same molecule is called "theranostics". In patients with advanced prostate cancer, PSMA might have a role in detecting disease spread through both positron emission tomography and single-photon emission computed tomography imaging, while treating prostate cancer systemic localizations with radioligand therapy. Further studies are needed to better determine patients' risks and benefits of these therapeutic approaches.

Uptake in non-affected bone tissue does not differ between [18F]-DCFPyL and [68Ga]-HBED-CC PSMA PET/CT

Introduction

[⁶⁸Ga]PSMA-HBED-CC and [¹⁸F]DCFPyL show a high potential for the detection of recurrent prostate cancer. While ¹⁸F-based tracers have several advantages in availability and image resolution, their sensitivity in the skeleton might be impaired by released [¹⁸F]fluoride due to its high bone affinity. In turn, chemically unbound trivalent ⁶⁸Ga might also accumulate in osseous tissue, in cases of occupied binding sites of plasma proteins and thereby influence bone signal.

Methods

A comparison of average bone SUV was performed in 17 bone-negative and 4 bone-positive patients. All patients underwent PET/CT 125 minutes after application of [¹⁸F]DCFPyL and 73 minutes after application of [⁶⁸Ga]PSMA-HBED-CC at another date.

Results

Native SUVs in unaffected bone tissue and SUVs relative to liver uptake were lower in [¹⁸F]DCFPyL (0.49) than in [⁶⁸Ga]PSMA-HBED-CC scans (0.52). SUVs relative to gluteal muscles did not differ between the two tracers. Average lesional SUVs did not differ between tracers.

Conclusion

No difference of average bone signal intensity was observed for [¹⁸F]DCFPyL-PET/CT in comparison to [⁶⁸Ga]PSMA-HBED-CC scans indicating that diagnostic assessment of the skeleton is not affected by non-specific accumulation of free [¹⁸F]fluoride or ⁶⁸Ga.

Clinical perspectives of PSMA PET/MRI for prostate cancer

Prostate cancer imaging has become an important diagnostic modality for tumor evaluation. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) has been extensively studied, and the results are robust and promising. The advent of the PET/magnetic resonance imaging (MRI) has added morphofunctional information from the standard of reference MRI to highly accurate molecular information from PET. Different PSMA ligands have been used for this purpose including 68gallium and 18fluorine-labeled PET probes, which have particular features including spatial resolution, imaging quality and tracer biodistribution. The use of PSMA PET imaging is well established for evaluating biochemical recurrence, even at low prostate-specific antigen (PSA) levels, but has also shown interesting applications for tumor detection, primary staging, assessment of therapeutic responses and treatment planning. This review will outline the potential role of PSMA PET/MRI for the clinical assessment of PCa.