Role of textural heterogeneity parameters in patient selection for 177Lu-PSMA therapy via response prediction

Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma

Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging and directing prostate carcinoma treatment. Here, we summarize the available literature data on radiomics and machine learning using these imaging agents in prostate carcinoma. Gleason scores derived from biopsy and after resection are discordant in a large number of prostate carcinoma patients. Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma might be better performing than biopsy-based Gleason-scoring and could serve as an alternative for non-invasive GS characterization. Furthermore, it may allow for the prediction of biochemical recurrence with a net benefit for clinical utilization. Machine learning based on PET/CT radiomics features was also shown to be able to differentiate benign from malignant increased tracer uptake on PSMA-targeting radioligand PET/CT examinations, thus paving the way for a fully automated image reading in nuclear medicine. As for prediction to treatment outcome following 177Lu-PSMA therapy and overall survival, a limited number of studies have reported promising results on radiomics and machine learning applied to PSMA-targeting radioligand PET/CT images for this purpose. Its added value to clinical parameters warrants further exploration in larger datasets of patients.

Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning

INTRODUCTION

Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in metastatic castration-resistant prostate cancer (MCRPC). We aimed to predict lesion-based treatment response after Lu-177 PSMA treatment using machine learning with texture analysis data obtained from pretreatment Gallium-68 (Ga-68) PSMA positron emission tomography/computed tomography (PET/CT).

METHODS

Eighty-three progressed, and 91 nonprogressed malignant foci on pretreatment Ga-68 PSMA PET/CT of 9 patients were used for analysis. Malignant foci with at least a 30% increase in Ga-68 PSMA uptake after two cycles of treatment were considered progressed lesions. All other changes in Ga-68 PSMA uptake of the lesions were considered nonprogressed lesions. The classifiers tried to predict progressed lesions.

RESULTS

Logistic regression, Naive Bayes, and k-nearest neighbors' area under the ROC curve (AUC) values in detecting progressed lesions in the training group were 0.956, 0.942, and 0.950, respectively, and their accuracy was 87%, 85%, and 89%, respectively. The AUC values of the classifiers in the testing group were 0.937, 0.954, and 0.867, respectively, and their accuracy was 85%, 88%, and 79%, respectively.

CONCLUSION

Using machine learning with texture analysis data obtained from pretreatment Ga-68 PSMA PET/CT in MCRPC predicted lesion-based treatment response after two cycles of Lu-177 PSMA treatment.

Biology-guided radiotherapy in metastatic prostate cancer: time to push the envelope?

The therapeutic landscape of metastatic prostate cancer has undergone a profound revolution in recent years. In addition to the introduction of novel molecules in the clinics, the field has witnessed a tremendous development of functional imaging modalities adding new biological insights which can ultimately inform tailored treatment strategies, including local therapies. The evolution and rise of Stereotactic Body Radiotherapy (SBRT) have been particularly notable in patients with oligometastatic disease, where it has been demonstrated to be a safe and effective treatment strategy yielding favorable results in terms of disease control and improved oncological outcomes. The possibility of debulking all sites of disease, matched with the ambition of potentially extending this treatment paradigm to polymetastatic patients in the not-too-distant future, makes Biology-guided Radiotherapy (BgRT) an attractive paradigm which can be used in conjunction with systemic therapy in the management of patients with metastatic prostate cancer.

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.

Essentials of Theranostics: A Guide for Physicians and Medical Physicists

Radiopharmaceutical therapies (RPTs) are gaining increased interest with the recent emergence of novel safe and effective theranostic agents, improving outcomes for thousands of patients. The term theranostics refers to the use of diagnostic and therapeutic agents that share the same molecular target; a major step toward precision medicine, especially for oncologic applications. The authors dissect the fundamentals of theranostics in nuclear medicine. First, they explain the radioactive decay schemes and the characteristics of emitted electromagnetic radiation used for imaging, as well as particles used for therapeutic purposes, followed by the interaction of the different types of radiation with tissue. These concepts directly apply to clinical RPTs and play a major role in the efficacy and toxicity profile of different radiopharmaceutical agents. Personalized dosimetry is a powerful tool that can help estimate patient-specific absorbed doses, in tumors as well as normal organs. Dosimetry in RPT is an area of active investigation, as most of what we know about the relationship between delivered dose and tissue damage is extrapolated from external-beam radiation therapy; more research is needed to understand this relationship as it pertains to RPTs. Tumor heterogeneity is increasingly recognized as an important prognostic factor. Novel molecular imaging agents, often in combination with fluorine 18-fluorodeoxyglucose, are crucial for assessment of target expression in the tumor and potential hypermetabolic disease that may lack the molecular target expression. ©RSNA, 2023 Test Your Knowledge questions are available in the supplemental material.

Prospective inter- and intra-tracer repeatability analysis of radiomics features in [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET scans in metastatic prostate cancer

OBJECTIVE

This study aimed to quantify both the intra- and intertracer repeatability of lesion-level radiomics features in [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 and [18F]F-PSMA-1007 positron emission tomography (PET) scans.

METHODS

Eighteen patients with metastatic prostate cancer (mPCa) were prospectively recruited for the study and randomised to one of three test-retest groups: (i) intratracer [68Ga]Ga-PSMA-11 PET, (ii) intratracer [18F]F-PSMA-1007 PET or (iii) intertracer between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET. Four conventional PET metrics (standardised uptake value (SUV)max, SUVmean, SUVtotal and volume) and 107 radiomics features were extracted from 75 lesions and assessed using the repeatability coefficient (RC) and the ICC. Radiomic feature repeatability was also quantified after the application of 16 filters to the PET image.

RESULTS

Test-retest scans were taken a median of 5 days apart (range: 2-7 days). SUVmean demonstrated the lowest RC limits of the conventional features, with RCs of 7.9%, 14.2% and 24.7% for the [68Ga]Ga-PSMA-11 PET, [18F]F-PSMA-1007 PET, and intertracer groups, respectively. 69%, 66% and 9% of all radiomics features had good or excellent ICC values (ICC ≥ 0.75) for the same groups. Feature repeatability therefore diminished considerably for the intertracer group relative to intratracer groups.

CONCLUSION

In this study, robust biomarkers for each tracer group that can be used in subsequent clinical studies were identified. Overall, the repeatability of conventional and radiomic features were found to be substantially lower for the intertracer group relative to both intratracer groups, suggesting that assessing patient response quantitatively should be done using the same radiotracer where possible.

ADVANCES IN KNOWLEDGE

Intertracer biomarker repeatability limits are significantly larger than intratracer limits.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.

Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer

Objective

The treatment with 177Lutetium PSMA (177Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been approved by the FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and identification of short- and long-term survivors after treatment could help tailor mCRPC diagnosis and treatment accordingly. The aim of this study is to investigate the value of radiomic parameters extracted from pretreatment 68Ga-PSMA PET images for the prediction of treatment response.

Methods

A total of 45 mCRPC patients treated with 177Lu-PSMA-617 from two university hospital centers were retrospectively reviewed for this study. Radiomic features were extracted from the volumetric segmentations of metastases in the bone. A random forest model was trained and validated to predict treatment response based on age and conventionally used PET parameters, radiomic features and combinations thereof. Further, overall survival was predicted by using the identified radiomic signature and compared to a Cox regression model based on age and PET parameters.

Results

The machine learning model based on a combined radiomic signature of three features and patient age achieved an AUC of 0.82 in 5-fold cross-validation and outperformed models based on age and PET parameters or radiomic features (AUC, 0.75 and 0.76, respectively). A Cox regression model based on this radiomic signature showed the best performance to predict overall survival (C-index, 0.67).

Conclusion

Our results demonstrate that a machine learning model to predict response to 177Lu-PSMA treatment based on a combination of radiomics and patient age outperforms a model based on age and PET parameters. Moreover, the identified radiomic signature based on pretreatment 68Ga-PSMA PET images might be able to identify patients with an improved outcome and serve as a supportive tool in clinical decision making.

Joint EANM/SNMMI procedure guideline for the use of 177Lu-labeled PSMA-targeted radioligand-therapy (177Lu-PSMA-RLT)

Prostate-specific membrane antigen (PSMA) is expressed by the majority of clinically significant prostate adenocarcinomas, and patients with target-positive disease can easily be identified by PSMA PET imaging. Promising results with PSMA-targeted radiopharmaceutical therapy have already been obtained in early-phase studies using various combinations of targeting molecules and radiolabels. Definitive evidence of the safety and efficacy of [177Lu]Lu-PSMA-617 in combination with standard-of-care has been demonstrated in patients with metastatic castration-resistant prostate cancer, whose disease had progressed after or during at least one taxane regimen and at least one novel androgen-axis drug. Preliminary data suggest that 177Lu-PSMA-radioligand therapy (RLT) also has high potential in additional clinical situations. Hence, the radiopharmaceuticals [177Lu]Lu-PSMA-617 and [177Lu]Lu-PSMA-I&T are currently being evaluated in ongoing phase 3 trials. The purpose of this guideline is to assist nuclear medicine personnel, to select patients with highest potential to benefit from 177Lu-PSMA-RLT, to perform the procedure in accordance with current best practice, and to prepare for possible side effects and their clinical management. We also provide expert advice, to identify those clinical situations which may justify the off-label use of [177Lu]Lu-PSMA-617 or other emerging ligands on an individual patient basis.

Lack of repeatability of radiomic features derived from PET scans: Results from a 18 F-DCFPyL test-retest cohort

OBJECTIVES

PET-based radiomic metrics are increasingly utilized as predictive image biomarkers. However, the repeatability of radiomic features on PET has not been assessed in a test-retest setting. The prostate-specific membrane antigen-targeted compound ¹⁸ F-DCFPyL is a high-affinity, high-contrast PET agent that we utilized in a test-retest cohort of men with metastatic prostate cancer (PC).

METHODS

Data of 21 patients enrolled in a prospective clinical trial with histologically proven PC underwent two ¹⁸ F-DCFPyL PET scans within 7 days, using identical acquisition and reconstruction parameters. Sites of disease were segmented and a set of 29 different radiomic parameters were assessed on both scans. We determined repeatability of quantification by using Pearson's correlations, within-subject coefficient of variation (wCOV), and Bland-Altman analysis.

RESULTS

In total, 230 lesions (177 bone, 38 lymph nodes, 15 others) were assessed on both scans. For all investigated radiomic features, a broad range of inter-scan correlation was found (r, 0.07-0.95), with acceptable reproducibility for entropy and homogeneity (wCOV, 16.0% and 12.7%, respectively). On Bland-Altman analysis, no systematic increase or decrease between the scans was observed for either parameter (±1.96 SD: 1.07/-1.30, 0.23/-0.18, respectively). The remaining 27 tested radiomic metrics, however, achieved unacceptable high wCOV (≥21.7%).

CONCLUSION

Many common radiomic features derived from a test-retest PET study had poor repeatability. Only Entropy and homogeneity achieved good repeatability, supporting the notion that those image biomarkers may be incorporated in future clinical trials. Those radiomic features based on high frequency aspects of images appear to lack the repeatability on PET to justify further study.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA

Introduction

This study was conducted to evaluate the predictive values of volumetric parameters and radiomic features (RFs) extracted from pretreatment 68Ga-PSMA PET and baseline clinical parameters in response to 177Lu-PSMA therapy.

Materials and methods

In this retrospective multicenter study, mCRPC patients undergoing 177Lu-PSMA therapy were enrolled. According to the outcome of therapy, the patients were classified into two groups including positive biochemical response (BCR) (≥ 50% reduction in the serum PSA value) and negative BCR (< 50%). Sixty-five RFs, eight volumetric parameters, and also seventeen clinical parameters were evaluated for the prediction of BCR. In addition, the impact of such parameters on overall survival (OS) was evaluated.

Results

33 prostate cancer patients with a median age of 69 years (range: 49-89) were enrolled. BCR was observed in 22 cases (66%), and 16 cases (48.5%) died during the follow-up time. The results of Spearman correlation test indicated a significant relationship between BCR and treatment cycle, administered dose, HISTO energy, GLCM entropy, and GLZLM LZLGE (p<0.05). In addition, according to the Mann-Whitney U test, age, cycle, dose, GLCM entropy, and GLZLM LZLGE were significantly different between BCR and non BCR patients (p<0.05). According to the ROC curve analysis for feature selection for prediction of BCR, GLCM entropy, age, treatment cycle, and administered dose showed acceptable results (p<0.05). According to SVM for assessing the best model for prediction of response to therapy, GLCM entropy alone showed the highest predictive performance in treatment planning. For the entire cohort, the Kaplan-Meier test revealed a median OS of 21 months (95% CI: 12.12-29.88). The median OS was estimated at 26 months (95% CI: 17.43-34.56) for BCR patients and 13 months (95% CI: 9.18-16.81) for non BCR patients. Among all variables included in the Kaplan Meier, the only response to therapy was statistically significant (p=0.01).

Conclusion

This exploratory study showed that the heterogeneity parameter of pretreatment 68Ga-PSMA PET images might be a potential predictive value for response to 177Lu-PSMA therapy in mCRPC; however, further prospective studies need to be carried out to verify these findings.

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.

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

177 Lu-PSMA radioligand therapy effectiveness in metastatic castration-resistant prostate cancer: An updated systematic review and meta-analysis

BACKGROUND

An updated systematic review and meta-analysis of relevant studies to evaluate the effectiveness of prostate-specific membrane antigen (PSMA)-targeted endoradiotherapy/radioligand therapy (PRLT) in castration resistant prostate cancer (CRPC).

METHODS

A systematic search was performed in July 2020 using PubMed/Medline database to update our prior systematic review. The search was limited to papers published from 2019 to June 2020. A total of 472 papers were reviewed. The studied parameters included pooled proportion of patients showing any or ≥50% prostate-specific antigen (PSA) decline after PRLT. Survival effects of PRLT were assessed based on pooled hazard ratios (HRs) of the overall survival (OS) according to any PSA as well as ≥50% PSA decline after PRLT. Response to therapy based on ≥50% PSA decrease after PRLT versus controls was evaluated using Mantel-Haenszel random effect meta-analysis. All p values < 0.05 were considered as statistically significant.

RESULTS

A total of 45 publications were added to the prior 24 studies. 69 papers with total of 4157 patients were included for meta-analysis. Meta-analysis of the two recent randomized controlled trials showed that patients treated with ¹⁷⁷ Lu-PSMA 617 had a significantly higher response to therapy compared to controls based on ≥50% PSA decrease. Meta-analysis of the HRs of OS according to any PSA decline and ≥50% PSA decline showed survival prolongation after PRLT.

CONCLUSIONS

PRLT results in higher proportion of patients responding to therapy based on ≥50% PSA decline compared to controls. Any PSA decline and ≥50% PSA decline showed survival prolongation after PRLT.

ADVANCES IN KNOWLEDGE

This is the first meta-analysis to aggregate the recent randomized controlled trials of PRLT which shows CRPC patients had a higher response to therapy after PRLT compared to controls.

The role of Ga68 PSMA PET/CT imaging in Lu177 PSMA treatment planning in metastatic castration-resistant prostate cancer

OBJECTIVE

Lutetium-177 (Lu¹⁷⁷) prostate-specific membrane antigen (Lu¹⁷⁷ PSMA) is a novel targeted treatment for patients with metastatic castration-resistant prostate cancer (CRPC). The purpose of the study was to determine the molecular volumetric Gallium-68 (Ga⁶⁸) PSMA PET/CT parameters that can predict patients who will respond to treatment.

METHODS

These single-center retrospective data were obtained from metastatic CRPC patients receiving intravenous 6.0-8.5 GBq Lu¹⁷⁷ PSMA treatment every 6-8 weeks for a maximum of 3-8 cycles, with baseline Ga⁶⁸ PSMA PET/CT scan, clinical data, and information on treatment responses. All lesions were divided into two groups according to the increase and decrease in PSMA expression levels of 600 bone lesions and 85 lymph nodes that were compatible with metastasis of 23 patients after the treatment. The primary endpoint of our study was the evaluation of the relation between the baseline SUVmax, PSMA TV, TL PSMA values, and the treatment response of the two groups. The threshold values were determined for the parameters that had significant relations. In the present study, the prostate-specific antigen (PSA) response and treatment-induced toxicities were also evaluated as the secondary endpoint.

RESULTS

It was found that SUVmax, PSMA TV, and TL PSMA values in bone metastases showed significant differences between the groups with decreased and increased PSMA expression levels after the treatment. The AUC value for SUVmax was significant (AUC = 0.677; p < 0.001). The cutoff value was > 10.50 (sensitivity = 91.8%, Specificity = 41.5%) for SUVmax, > 1.50 cm³ (sensitivity = 49.1%, specificity = 70%) for PSMA TV and > 8.50 g (sensitivity = %60.9, specificity = %72.2) for TL PSMA. The median SUVmax value before the treatment in all metastatic lymph nodes was found to be 7.1 (5.4-12.4), and the median SUVmax after the treatment was 2.5 (1.6-12.1) (p < 0.001).

CONCLUSION

It was shown in the present study that Lu¹⁷⁷ PSMA treatment response may be higher in CRPC patients with metastatic bone lesion with high baseline PSMA expression level, and better treatment response may be achieved in patients with lymph node metastases than in bone metastases.

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

Evaluation of 68Ga-PSMA-11 PET-MRI in Patients with Advanced Prostate Cancer Receiving 177Lu-PSMA-617 Therapy: A Radiomics Analysis

¹⁷⁷Lutetium PSMA-617 (Lu-PSMA) therapy in patients with metastatic castration resistant prostate cancer (mCRPC) has gained visibility through the ongoing phase III trial. The data on prediction of therapy outcome and survival out of pretherapeutic imaging parameters is still sparse. In this study, the predictive and prognostic value of radiomic features from ⁶⁸Ga-PSMA-11 PET-MRI are analyzed. In total, 21 patients with mCRPC underwent ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy. The PET-positive tumor volume was defined and transferred to whole-body T2-, T1- and contrast-enhanced T1-weighted MRI-sequences. The radiomic features from PET and MRI sequences were extracted by using a freely available software package. For selecting features that allow differentiation of biochemical response (PSA decrease > 50%), a stepwise dimension reduction was performed. Logistic regression models were fitted, and selected features were tested for their prognostic value (overall survival) in all patients. Eight patients achieved biochemical response after Lu-PSMA therapy. Ten independent radiomic features differentiated well between responders and non-responders. The logistic regression model, including the feature interquartile range from T2-weighted images, revealed the highest accuracy (AUC = 0.83) for the prediction of biochemical response after Lu-PSMA therapy. Within the final model, patients with a biochemical response (p = 0.003) and higher T2 interquartile range values in pre-therapeutic imaging (p = 0.038) survived significantly longer. This proof-of-concept study provides first evidence on a potential predictive and prognostic value of radiomic analysis of pretherapeutic ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy.

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.

Lutetium-177 Labelled PSMA Targeted Therapy in Advanced Prostate Cancer: Current Status and Future Perspectives

Patients suffering from metastatic castration-resistant prostate cancer (mCRPC) have a poor prognosis. As a further treatment option ¹⁷⁷Lutetium (Lu) prostate-specific membrane antigen (PSMA) radioligand therapy gained a significant interest of many investigators. Several publications showed great response and prolonged survival with limited adverse events. However, to this point, it still remains unclear which patients benefit the most from ¹⁷⁷Lu-PSMA therapy, and how to improve the treatment regimen to achieve best outcome while minimizing potential adverse events. The efficacy for mCRPC patients is a given fact, and with the newly published results of the VISION trial its approval is only a matter of time. Recently, investigators started to focus on treating prostate cancer patients in earlier disease stages and in combination with other compounds. This review gives a brief overview of the current state and the future perspectives of ¹⁷⁷Lu labelled PSMA radioligand therapy.

Target Heterogeneity in Oncology: The Best Predictor for Differential Response to Radioligand Therapy in Neuroendocrine Tumors and Prostate Cancer

Simple Summary

In the era of precision medicine, novel targets have emerged on the surface of cancer cells, which have been exploited for the purpose of radioligand therapy. However, there have been variations in the way these receptors are expressed, especially in prostate cancers and neuroendocrine tumors. This variable expression of receptors across the grades of cancers led to the concept of ‘target heterogeneity’, which has not just impacted therapeutic decisions but also their outcomes. Radiopharmaceuticals targeting receptors need to be used when there are specific indicators—either clinical, radiological, or at molecular level—warranting their use. In addition, response to these radioligands can be assessed using different techniques, whereby we can prognosticate further outcomes. We shall also discuss, in this review, the conventional as well as novel approaches of detecting heterogeneity in prostate cancers and neuroendocrine tumors.

Abstract

Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.

A Systematic Review and Meta-analysis of the Effectiveness and Toxicities of Lutetium-177-labeled Prostate-specific Membrane Antigen-targeted Radioligand Therapy in Metastatic Castration-Resistant Prostate Cancer

CONTEXT

Castration-resistant prostate cancer (CRPC) treatment is an evolving challenge. Prostate-specific membrane antigen (PSMA)-targeted endoradiotherapy/radioligand therapy (PRLT) with small-molecule, urea-based agents labeled with the β-particle-emitting radionuclide lutetium-177 (177Lu) is a promising new approach.

OBJECTIVE

In this systematic review and meta-analysis, we evaluated the efficacy and toxicity of PRLT.

EVIDENCE ACQUISITION

A systematic search was performed in PubMed/Medline (last updated February 18, 2019). A total of 250 studies were reviewed, and 24 studies with 1192 patients were included in the analysis. Proportions of patients with ≥50% serum prostate-specific antigen (PSA) decrease, any PSA decrease, and any PSA increase were extracted. Proportions of patients showing any grade toxicity and those with grade 3/4 toxicities based on Common Terminology Criteria for Adverse Events (CTCAE) grading were extracted from manuscripts. Overall survival and progression-free survival were evaluated. A meta-analysis of single proportions was carried out. Furthermore, we compared the two most common PRLT agents, 177Lu-PSMA with 177Lu-PSMA-I&T, for effectiveness and toxicity.

EVIDENCE SYNTHESIS

Among the 24 included studies, 20 included data on 177Lu-PSMA-617, three included data on 177Lu-PSMA-I&T, and one study had aggregated data for 177Lu-PSMA-617 and 177Lu-PSMA-I&T. The estimated proportion of 177Lu-PSMA-617-treated patients who showed a serum PSA decrease of ≥50% with at least an 8-wk interval between therapy and PSA measurement was 0.44 (0.39; 0.50). Therapy with 177Lu-PSMA-I&T demonstrated an estimated proportion of patients with ≥50% PSA reduction to be 0.36 (0.26; 0.47). The aggregate results for men treated with more than one cycle of any kind of PRLT showed an estimated proportion of 0.46 (0.41; 0.51) for PSA response ≥50%. Regarding aggregate data from all of the PRLT agents, we found that grade 3 and 4 toxicities were uncommon, with estimated proportions from 0.01 (0.00;0.04) for nausea, fatigue, diarrhea, and elevated aspartate transaminase up to 0.08 (0.05; 0.12) for anemia. There was considerable heterogeneity among the studies in the "any-grade toxicity" groups. Meta-regression showed that more than one cycle of PRLT is associated with a greater proportion of patients with ≥50% PSA reduction. Overall survival according to pooled hazard ratios (HRs) for any PSA decline was 0.29 (0.18; 0.46), and for >50% PSA reduction was 0.67 (0.43; 1.07). Progression-free survival according to a pooled HR of >50% PSA reduction was 0.53 (0.32; 0.86).

CONCLUSIONS

The relatively high number of PSA responders alongside the low rate of severe toxicity reflects the potentially promising role of PRLT in treating CRPC. The ultimate utility of this treatment modality will become clearer as multiple prospective studies continue to accrue. In the interim, this systematic review and meta-analysis can serve as a compendium of effectiveness and adverse events associated with PRLT for treating clinicians.

PATIENT SUMMARY

Prostate-specific membrane antigen-targeted endoradiotherapy/radioligand therapy (PRLT) is associated with ≥50% reduction in prostate-specific antigen level in a large number of patients and a low rate of toxicity, reflecting its potential in treating castration-resistant prostate cancer. This systematic review and meta-analysis presents as a compendium of the effectiveness and adverse events related to PRLT for treating clinicians.

Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters

Background

Treatment with radiolabeled ligands to prostate-specific membrane antigen (PSMA) is gaining importance in the treatment of patients with advanced prostate carcinoma. Previous imaging with positron emission tomography/computed tomography (PET/CT) is mandatory. The aim of this study was to investigate the role of radiomics features in PSMA-PET/CT scans and clinical parameters to predict response to ¹⁷⁷Lu-PSMA treatment given just baseline PSMA scans using state-of-the-art machine learning (ML) methods.

Methods

A total of 2,070 pathological hotspots annotated in 83 prostate cancer patients undergoing PSMA therapy were analyzed. Two main tasks are performed: (I) analyzing correlation of averaged (per patient) values of radiomics features of individual hotspots and clinical parameters with difference in prostate specific antigen levels (ΔPSA) in pre- and post-therapy as a therapy response indicator. (II) ML-based classification of patients into responders and non-responders based on averaged features values and clinical parameters. To achieve this, machine learning (ML) algorithms and linear regression tests are applied. Grid search, cross validation (CV) and permutation test were performed to assure that the results were significant.

Results

Radiomics features (PET_Min, PET_Correlation, CT_Min, CT_Busyness and CT_Coarseness) and clinical parameters such as Alp1 and Gleason score showed best correlations with ΔPSA. For the treatment response prediction task, 80% area under the curve (AUC), 75% sensitivity (SE), and 75% specificity (SP) were obtained, applying ML support vector machine (SVM) classifier with radial basis function (RBF) kernel on a selection of radiomics features and clinical parameters with strong correlations with ΔPSA.

Conclusions

Machine learning based on ⁶⁸Ga-PSMA PET/CT radiomics features holds promise for the prediction of response to ¹⁷⁷Lu-PSMA treatment, given only base-line ⁶⁸Ga-PSMA scan. In addition, it was shown that, the best correlating set of radiomics features with ΔPSA are superior to clinical parameters for this therapy response prediction task using ML classifiers.

In Comparison to PSA, Interim Ga-68-PSMA PET/CT Response Evaluation Based on Modified RECIST 1.1 After 2nd Cycle Is Better Predictor of Overall Survival of Prostate Cancer Patients Treated With 177Lu-PSMA

Background

Prostate-specific membrane antigen (PSMA) targeting radioligands have transformed treatment of prostate cancer. Radioligand therapy (RLT) with ¹⁷⁷Lu-PSMA in metastasized castration resistant prostate cancer (mCRPC) achieves objective response and disease stabilization in roughly two third of patients, whereas one third of patients progress. This study was performed to assess the role of interim PSMA PET/CT after the 2^nd cycle of RLT for early prediction of overall survival in patients undergoing RLT with ¹⁷⁷Lu-PSMA.

Methods

38 mCRPC patients (68.9 ± 8.1 y) treated with at least two cycles of RLT at 8 week intervals and interim ⁶⁸Ga-PSMA PET/CT (PET) at 8–10 weeks after the 2^nd cycle of RLT were included in this study. Prostate-specific antigen (PSA) response was evaluated according to the Prostate Cancer Working Group 3 criteria. Radiographic response assessment of soft tissue, lymph node, and bone lesions was performed according to RECIST 1.1 including the PET component. Patients’ data were collected for follow-up from the local Comprehensive Cancer Center Register.

Results

Median follow-up was 19.7 months (4.7–45.3). PSA response after the 2^nd therapy cycle showed partial remission (PR) in 23.7%, stable disease (SD) in 50%, and progressive disease (PD) in 26.3% of patients. In comparison, 52.6, 23.7, and 23.7% of patients showed PR, SD, and PD respectively on PET/CT. The strength of agreement between PSA response and PET/CT response criteria was only fair (kappa 0.346). Median overall survival (OS) was 22.5 months (95% CI: 15.8–29.2). Median OS stratified to PSA/PET response was 25.6/25.6 months for PR, 21.7/30.6 months for SD and 19.4/13.1 months for PD (p = 0.496 for PSA and 0.013 for PET/CT response).

Conclusions

Interim PSMA PET/CT based response evaluation at 8–10 weeks after the 2^nd cycle of RLT is predictive of overall survival and PD in patients treated with ¹⁷⁷Lu-PSMA. On the contrary, PSA appears to have only limited predictive value.

Explorative analysis of a score predicting the therapy response of patients with metastatic, castration resistant prostate cancer undergoing radioligand therapy with 177Lu-labeled prostate-specific membrane antigen

OBJECTIVE

Up to 60% of patients with metastatic, castration-resistant prostate cancer (mCRPC) treated with 177Lu prostate-specific membrane antigen (PSMA) radioligand therapy (RLT) achieves a partial biochemical response with a decrease of > 50% in prostate-specific antigen (PSA) levels. The remaining fractions, however, do not respond to RLT. The aim of this explorative analysis was to identify pre-therapeutic factors for the prediction of response.

METHODS

46 patients [age = 68 years (50-87)] with mCRPC who consecutively underwent RLT with 177Lu PSMA [median applied activity = 6 GBq (2.9-6.2)] were included and analysed retrospectively. The association of different clinical and laboratory factors and parameters from pre-therapeutic 68Ga PSMA positron emission tomography (PET) with the outcome of RLT was tested (Fisher's test). Outcome was defined as PSA changes 8 weeks after second RLT [partial response (PR), PSA decrease > 50%; progressive disease (PD), PSA increase ≥ 25%; stable disease (SD), others]. Significant predictive factors were combined in a predictive score.

RESULTS

30% showed a post-treatment PR (median 73% PSA decrease), 35% SD (median 17% PSA decrease) and 35% PD (median 42% PSA increase). Significant predictors for PD were alkaline phosphatase (ALP) > 135 U/l (p = 0.002), PSA > 200 ng/ml (p = 0.036), and maximum standardized uptake value (SUVmax) of the "hottest lesion" in pre-therapeutic PET < 45 (p = 0.005). The predictive score including PSA, ALP and SUVmax could separate 2 distinct groups of patients: ≤ 2 predictive factors (19% PD) and 3 predictive factors (90% PD).

CONCLUSION

The presented predictive score allowed a pre-therapeutic estimate of the expected response to 2 cycles of RLT. As our study was retrospective, prospective trials are needed for validation.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Tumor heterogeneity for differentiation between liver tumors and normal liver tissue in 18F-FDG PET/CT

AIM

 Malignancies show higher spatial heterogeneity than normal tissue. We investigated, if textural parameters from FDG PET describing the heterogeneity function as tool to differentiate between tumor and normal liver tissue.

METHODS

 FDG PET/CT scans of 80 patients with liver metastases and 80 patients with results negative upper abdominal organs were analyzed. Metastases and normal liver tissue were analyzed drawing up to three VOIs with a diameter of 25 mm in healthy liver tissue of the tumoral affected and results negative liver, whilst up to 3 metastases per patient were delineated. Within these VOIs 30 different textural parameters were calculated as well as SUV. The parameters were compared in terms of intra-patient and inter-patient variability (2-sided t test). ROC analysis was performed to analyze predictive power and cut-off values.

RESULTS

 28 textural parameters differentiated healthy and pathological tissue (p < 0.05) with high sensitivity and specificity. SUV showed ability to differentiate but with a lower significance. 15 textural parameters as well as SUV showed a significant variation between healthy tissues out of tumour infested and negative livers. Mean intra- and inter-patient variability of metastases were found comparable or lower for 6 of the textural features than the ones of SUV. They also showed good values of mean intra- and inter-patient variability of VOIs drawn in liver tissue of patients with metastases and of results negative ones.

CONCLUSION

 Heterogeneity parameters assessed in FDG PET are promising to classify tissue and differentiate malignant lesions usable for more personalized treatment planning, therapy response evaluation and precise delineation of tumors for target volume determination as part of radiation therapy planning.

Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with 177Lu-PSMA

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PSMA-PET/CT) scans can facilitate diagnosis and treatment of prostate disease. Radiomics signature (RS) is widely used for the analysis of overall survival (OS) in cancer diseases. This study aims at investigating the role of radiomics features (RFs) and RS from pretherapeutic gallium-68 (⁶⁸Ga)-PSMA-PET/CT findings and patient-specific clinical parameters to analyze overall survival of prostate cancer (PC) patients when treated with lutethium-177 (¹⁷⁷Lu)-PSMA. A cohort of 83 patients with advanced PC was retrospectively analyzed. Average values of 73 RFs of 2070 malignant hotspots as well as 22 clinical parameters were analyzed for each patient. From the Cox proportional hazard model, the least absolute shrinkage and selection operator (LASSO) regularization method is used to select most relevant features (standardized uptake value (SUV)_Min and kurtosis with the coefficients of 0.984 and −0.118, respectively) and to calculate the RS from the RFs. Kaplan–Meier (KM) estimator was used to analyze the potential of RFs and conventional clinical parameters, such as metabolic tumor volume (MTV) and standardized uptake value (SUV) for the prediction of survival. As a result, SUV_Min, kurtosis, the calculated RS, SUV_Mean, as well as Hemoglobin (Hb)1, C-reactive protein (CRP)1, and ECOG1 (clinical parameters) achieved p-values less than 0.05, which suggest the potential of findings from ⁶⁸Ga-PSMA-PET/CT scans as well as patient-specific clinical parameters for the prediction of OS for patients with advanced PC treated with ¹⁷⁷Lu-PSMA therapy.

Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy

Gallium-68 prostate-specific membrane antigen positron emission tomography (⁶⁸Ga-PSMA-PET) is a highly sensitive method to detect prostate cancer (PC) metastases. Visual discrimination between malignant and physiologic/unspecific tracer accumulation by a nuclear medicine (NM) specialist is essential for image interpretation. In the future, automated machine learning (ML)-based tools will assist physicians in image analysis. The aim of this work was to develop a tool for analysis of ⁶⁸Ga-PSMA-PET images and to compare its efficacy to that of human readers. Five different ML methods were compared and tested on multiple positron emission tomography/computed tomography (PET/CT) data-sets. Forty textural features extracted from both PET- and low-dose CT data were analyzed. In total, 2419 hotspots from 72 patients were included. Comparing results from human readers to those of ML-based analyses, up to 98% area under the curve (AUC), 94% sensitivity (SE), and 89% specificity (SP) were achieved. Interestingly, textural features assessed in native low-dose CT increased the accuracy significantly. Thus, ML based on ⁶⁸Ga-PSMA-PET/CT radiomics features can classify hotspots with high precision, comparable to that of experienced NM physicians. Additionally, the superiority of multimodal ML-based analysis considering all PET and low-dose CT features was shown. Morphological features seemed to be of special additional importance even though they were extracted from native low-dose CTs.

The 68Ga/177Lu-theragnostic concept in PSMA-targeting of metastatic castration-resistant prostate cancer: impact of post-therapeutic whole-body scintigraphy in the follow-up

INTRODUCTION

A new therapeutic option for metastatic castration-resistant prostate cancer (mCRPC) of heavily pre-treated patients lies in ¹⁷⁷Lu-PSMA-617 radioligand therapy.

METHODS

On the basis of PSMA-targeted ⁶⁸Ga-PSMA-11 PET/CT, 32 consecutive mCRPC patients were selected for ¹⁷⁷Lu-PSMA-617 therapy (6 GBq/cycle, 2 to 6 cycles, 6-10 weeks apart) and followed until death. Post-therapy whole-body (WB) dosimetry and ⁶⁸Ga-PSMA-11 PET/CT data were compared and related to progression free and overall survival.

RESULTS

¹⁷⁷Lu-PSMA-617 dosimetry after the first cycle indicated high tumor doses for skeletal (4.01 ± 2.64; range 1.10-13.00 Gy/GBq), lymph node (3.12 ± 2.07; range 0.70-8.70 Gy/GBq), and liver (2.97 ± 1.38; range 0.76-5.00 Gy/GBq) metastases whereas the dose for tissues/organs was acceptable in all patients for an intention-to-treat activity of 24 GBq. Any PSA decrease after the first cycle was found in 23/32 (72%), after the second cycle in 22/32 (69%), after the third cycle in 16/28 (57%), and after the fourth cycle in 8/18 (44%) patients. Post-therapy 24 h WB scintigraphy showed decreased tumor-to-background ratios in 24/32 (75%) after the first therapy cycle, after the second cycle in 17/29 (59%), and after the third cycle in 13/21 (62%) patients. The median PFS was 7 months and the median OS 12 months. In the group of PSA responders (n = 22) the median OS was 17 months versus 11 months in the group of non-responders (n = 10), p < 0.05. Decreasing SUV_max values were found for parotid (15.93 ± 6.23 versus 12.33 ± 4.07) and submandibular glands (17.65 ± 7.34 versus 13.12 ± 4.62) following treatment, along with transient (n = 6) or permanent (n = 2) xerostomia in 8/32 (25%) patients. In 3/32 patients, nephrotoxicity changed from Grade 2 to 3, whereas neither Grade 4 nephrotoxicity nor hematotoxicity was found. In most patients a good agreement was observed for the visual interpretation of the tracer accumulation between 24 h WB and PET/CT scans. However, no significance could be calculated for baseline-absorbed tumor doses and SUV_max values of tumor lesions. 5/32 (16%) patients showed a mixed response pattern, which resulted in disease progression over time.

CONCLUSION

Serial PSA measurements and post-therapy 24 h WB scintigraphy seems to allow a sufficiently accurate follow-up of ¹⁷⁷Lu-PSMA-617-treated mCRPC patients whereas ⁶⁸Ga-PSMA-11 PET/CT should be performed for patient selection and final response assessment.

PSMA-Based Theranostics: A Step-by-Step Practical Approach to Diagnosis and Therapy for mCRPC Patients

To date, several papers have been published about prostate-specific membrane antigen (PSMA)-based radioligand diagnostic and therapeutic approaches. This paper mainly provides information for nuclear medicine physicians that are clinically engaged in the diagnosis and treatment of prostate cancer patients. It aims to present the utility of PSMA imaging and therapy in a step-by-step practical approach; thus, it does not discuss radiochemistry and the molecular basics of PSMA.