Diagnostic value of dual-tracer PET/CT with [¹⁸F]FDG and PSMA ligands in prostate cancer: an updated systematic review.

Prostate-specific membrane antigen (PSMA) ligand PET/CT has significantly improved prostate cancer (PCa) imaging. However, in patients with poorly differentiated PCa or neuroendocrine transdifferentiation, [ <sup>18</sup> F]fluorodeoxyglucose ([ <sup>18</sup> F]FDG) PET/CT may provide additional diagnostic information. This systematic review evaluates the diagnostic value of combining [ <sup>18</sup> F]FDG PET/CT with PSMA ligand PET/CT in PCa patients. A systematic literature search of studies assessing the added diagnostic value of dual-tracer [ <sup>18</sup> F]FDG and PSMA ligands PET/CT in PCa patients was conducted using PubMed/MEDLINE and Cochrane Library databases and available information was summarized. Fourteen studies (n = 901 patients) met the inclusion criteria. The dual-tracer approach identified [ <sup>18</sup> F]FDG-positive/PSMA-negative (FDG+/PSMA-) lesions in a subset of patients, particularly those with Gleason Score (GS) ≥ 9. However, in patients with GS < 8, [ <sup>18</sup> F]FDG PET/CT did not significantly improve lesion detection over PSMA ligand PET/CT alone.The presence of FDG+/PSMA- lesions correlated with aggressive tumor biology, increased risk of metastases, and worse prognosis. Literature data showed that [ <sup>18</sup> F]FDG PET/CT may serve as a valuable complementary imaging modality for high risk PCa patients potentially influencing staging and treatment decisions. Future prospective studies are warranted to further elucidate the prognostic significance and cost-effectiveness of combining [ <sup>18</sup> F]FDG PET/CT with PSMA ligand PET/CT in PCa patients

Opportunistic skeletal muscle metrics as prognostic tools in metastatic castration-resistant prostate cancer patients candidates to receive Radium-223

Objective: Androgen deprivation therapy alters body composition promoting a significant loss in skeletal muscle (SM) mass through inflammation and oxidative damage. We verified whether SM anthropometric composition and metabolism are associated with unfavourable overall survival (OS) in a retrospective cohort of metastatic castration-resistant prostate cancer (mCRPC) patients submitted to 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) imaging before receiving Radium-223. Patients and methods: Low-dose CT were opportunistically analysed using a cross-sectional approach to calculate SM and adipose tissue areas at the third lumbar vertebra level. Moreover, a 3D computational method was used to extract psoas muscles to evaluate their volume, Hounsfield Units (HU) and FDG retention estimated by the standardized uptake value (SUV). Baseline established clinical, lab and imaging prognosticators were also recorded. Results: SM area predicted OS at univariate analysis. However, this capability was not additive to the power of mean HU and maximum SUV of psoas muscles volume. These factors were thus combined in the Attenuation Metabolic Index (AMI) whose power was tested in a novel uni- and multivariable model. While Prostate-Specific Antigen (PSA), Alkaline Phosphatase (ALP), Lactate Dehydrogenase and Hemoglobin, Metabolic Tumor Volume, Total Lesion Glycolysis and AMI were associated with long-term OS at the univariate analyses, only PSA, ALP and AMI resulted in independent prognosticator at the multivariate analysis. Conclusion: The present data suggest that assessing individual 'patients' SM metrics through an opportunistic operator-independent computational analysis of FDG PET/CT imaging provides prognostic insights in mCRPC patients candidates to receive Radium-223. Graphical abstract: [Figure not available: see fulltext.

Combining PARP Inhibitors and Androgen Receptor Signalling Inhibitors in Metastatic Prostate Cancer: A Quantitative Synthesis and Meta-analysis

Copyright \ua9 2023. Published by Elsevier B.V. CONTEXT: PARP inhibitors (PARPi) are established treatments for metastatic castration-resistant prostate cancer (mCRPC) with homologous recombination repair (HRR) deficiency after androgen receptor signalling inhibitor (ARSI) failure. New PARPi + ARSI combinations have been tested in all comers, although their clinical relevance in HRR-proficient tumours remains uncertain. OBJECTIVE: To quantitatively synthesise evidence from randomised trials assessing the efficacy and safety of PARPi + ARSI combinations for first-line treatment of mCRPC. EVIDENCE ACQUISITION: We searched the PubMed, EMBASE, SCOPUS, and Cochrane Library databases up to February 28, 2023. Randomised controlled trials (RCTs) comparing PARPi + ARSI versus placebo + ARSI for first-line treatment of mCRPC were eligible. Two reviewers independently performed screening and data extraction and assessed the risk of bias, while a third reviewer evaluated the eligibility criteria. EVIDENCE SYNTHESIS: Overall, three phase 3 RCTs were included in the systematic review: PROPEL, MAGNITUDE, and TALAPRO-2. A total of 2601 patients with mCRPC were enrolled. Two of these trials (PROPEL and TALAPRO-2) assessed the radiographic progression-free survival benefit of PARPi + ARSI for first-line treatment of mCRPC, independent of HRR status. The pooled hazard ratio was 0.62 (95% confidence interval 0.53-0.72). The pooled hazard ratio for overall survival was 0.84 (95% confidence interval 0.72-0.98), indicating a 16% reduction in the risk of death among patients who received the combination. CONCLUSIONS: Results from this meta-analysis support the use of ARSI + PARPi combinations in biomarker-unselected mCRPC. However, such combinations might be less clinically relevant in HRR-proficient cancers, especially considering the change in treatment landscape for mCRPC. PATIENT SUMMARY: We looked at outcomes from trials testing combinations of two classes of drugs (PARP inhibitors and ARSI) in advanced prostate cancer. We found that these combinations seem to work regardless of gene mutations identified as biomarkers of response to PARP inhibitors when used on their own

Somatostatin receptor PET/CT imaging for the detection and staging of pancreatic NET. A systematic review and meta-analysis

We investigated the diagnostic performance of Somatostatin Receptor Positron Emission Tomography/Computed Tomography (SSR-PET/CT) for the detection of primary lesion and initial staging of pancreatic neuroendocrine tumors (pNETs). A comprehensive literature search up to January 2020 was performed selecting studies in presence of: sample size ≥10 patients; index test (i.e., 68Ga-DOTATOC or 68Ga-DOTANOC or 68Ga-DOTATATE PET/CT); and outcomes (i.e., detection rate (DR), true positive, true negative, false positive, and false-negative). The methodological quality was evaluated with QUADAS-2. Pooled DR and pooled sensitivity and specificity for the identification of the primary tumor were assessed by a patient-based and a lesion-based analysis. Thirty-eight studies were selected for the qualitative analysis, while 18 papers were included in the meta-analysis. The number of pNET patients ranged from 10 to 142, for a total of 1143 subjects. At patient-based analysis, the pooled sensitivity and specificity for the assessment of primary pNET were 79.6% (95% confidence interval (95%CI): 71–87%) and 95% (95%CI: 75–100%) with a heterogeneity of 59.6% and 51.5%, respectively. Pooled DR for the primary lesion was 81% (95%CI: 65–90%) and 92% (95%CI: 80–97%), respectively, at patient-based and lesion-based analysis. In conclusion, SSR-PET/CT has high DR and diagnostic performances for primary lesion and initial staging of pNETs

Immune Checkpoint Inhibitors in Advanced Prostate Cancer: Current Data and Future Perspectives

In the last 10 years, many new therapeutic options have been approved in advanced prostate cancer (PCa) patients, granting a more prolonged survival in patients with metastatic disease, which, nevertheless, remains incurable. The emphasis on immune checkpoint inhibitors (ICIs) has led to many trials in this setting, with disappointing results until now. Therefore, we discuss the immunobiology of PCa, presenting ongoing trials and the available clinical data, to understand if immunotherapy could represent a valid option in this disease, and which subset of patients may be more likely to benefit. Current evidence suggests that the tumor microenvironment needs a qualitative rather than quantitative evaluation, along with the genomic determinants of prostate tumor cells. The prognostic or predictive value of immunotherapy biomarkers, such as PD-L1, TMB, or dMMR/MSI-high, needs further evaluation in PCa. Monotherapy with immune checkpoint inhibitors (ICIs) has been modestly effective. In contrast, combined strategies with other standard treatments (hormonal agents, chemotherapy, PARP inhibitors, radium-223, and TKIs) have shown some results. Immunotherapy should be better investigated in biomarker-selected patients, particularly with specific pathway aberrations (e.g., AR-V7 variant, HRD, CDK12 inactivated tumors, MSI-high tumors). Lastly, we present new possible targets in PCa that could potentially modulate the tumor microenvironment and improve antitumor activity with ICIs

Sex differences in neuroimaging biomarkers in healthy subjects and dementia

Several studies have investigated sex differences in morphological and functional neuroimaging in healthy conditions, but few data are available on the specific effect of sex on neuroimaging in dementia and related disorders. Sex is more often considered as a confounding variable than as a variable of interest. Furthermore, especially in the dementia field, the data are drawn from studies designed for other specific aims, and discrepant results are often found due to different equipment, studied cohorts, and analytical methods. Notably, investigating this aspect would be pivotal to better understand how age-related diseases, such as dementia, differentially impact on the main correlates of brain functions in males and females. We review some of the main findings of the literature, highlighting the need for a more systematic appraisal of sex and even gender differences in future neuroimaging studies

The DASciS Software for BSI Calculation as a Valuable Prognostic Tool in mCRPC Treated with 223RaCl2: A Multicenter Italian Study

Background/Aim: Radium-223 dichloride (223RaCl2) represents a therapeutic option for metastatic castration-resistant prostate cancer (mCRPC) patients dealing with symptomatic bone metastases. The identification of baseline variables potentially affecting the life-prolonging role of 223RaCl2 is still ongoing. Bone scan index (BSI) defines the total load of bone metastatic disease detected on a bone scan (BS) and is expressed as a percentage value of the whole bone mass. The aim of this multicenter study was to assess the impact of baseline BSI on overall survival (OS) in mCRPC patients treated with 223RaCl2. For this purpose, the DASciS software developed by the Sapienza University of Rome for BSI calculation was shared between six Italian Nuclear Medicine Units. Methods: 370 pre-treatment BS were analyzed through the DASciS software. Other clinical variables relevant to OS analysis were taken into account for the statistical analysis. Results: Of a total of 370 patients, 326 subjects had died at the time of our retrospective analysis. The median OS time from the first cycle of 223RaCl2 to the date of death from any cause or last contact was 13 months (95%CI 12–14 months). The mean BSI value resulted in 2.98% ± 2.42. The center-adjusted univariate analysis showed that baseline BSI was significantly associated with OS as an independent risk factor (HR 1.137, 95%CI: 1.052–1.230, p = 0.001), meaning that patients with higher BSI values had worse OS. When adjusting for other measures on multivariate analysis, in addition to Gleason score and baseline values of Hb, tALP, and PSA, baseline BSI was confirmed to be a statistically significant parameter (HR 1.054, 95%CI: 1.040–1.068, p < 0.001). Conclusions: Baseline BSI significantly predicts OS in mCRPC treated with 223RaCl2. The DASciS software was revealed to be a valuable tool for BSI calculation, showing rapid processing time and requiring no more than a single demonstrative training for each participating center

Spinal cord hypermetabolism extends to skeletal muscle in amyotrophic lateral sclerosis: a computational approach to [18F]-fluorodeoxyglucose PET/CT images

Purpose: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease leading to neuromuscular palsy and death. We propose a computational approach to [18F]-fluorodeoxyglucose (FDG) PET/CT images to analyze the structure and metabolic pattern of skeletal muscle in ALS and its relationship with disease aggressiveness. Materials and methods: A computational 3D method was used to extract whole psoas muscle\u2019s volumes and average attenuation coefficient (AAC) from CT images obtained by FDG PET/CT performed in 62 ALS patients and healthy controls. Psoas average standardized uptake value (normalized on the liver, N-SUV) and its distribution heterogeneity (defined as N-SUV variation coefficient, VC-SUV) were also extracted. Spinal cord and brain motor cortex FDG uptake were also estimated. Results: As previously described, FDG uptake was significantly higher in the spinal cord and lower in the brain motor cortex, in ALS compared to controls. While psoas AAC was similar in patients and controls, in ALS a significant reduction in psoas volume (3.6 \ub1 1.02 vs 4.12 \ub1 1.33 mL/kg; p < 0.01) and increase in psoas N-SUV (0.45 \ub1 0.19 vs 0.29 \ub1 0.09; p < 0.001) were observed. Higher heterogeneity of psoas FDG uptake was also documented in ALS (VC-SUV 8 \ub1 4%, vs 5 \ub1 2%, respectively, p < 0.001) and significantly predicted overall survival at Kaplan\u2013Meier analysis. VC-SUV prognostic power was confirmed by univariate analysis, while the multivariate Cox regression model identified the spinal cord metabolic activation as the only independent prognostic biomarker. Conclusion: The present data suggest the existence of a common mechanism contributing to disease progression through the metabolic impairment of both second motor neuron and its effector

Immune Checkpoint Inhibitors in Advanced Prostate Cancer: Current Data and Future Perspectives

\ua9 2022 by the authors. Licensee MDPI, Basel, Switzerland.In the last 10 years, many new therapeutic options have been approved in advanced prostate cancer (PCa) patients, granting a more prolonged survival in patients with metastatic disease, which, nevertheless, remains incurable. The emphasis on immune checkpoint inhibitors (ICIs) has led to many trials in this setting, with disappointing results until now. Therefore, we discuss the immunobiology of PCa, presenting ongoing trials and the available clinical data, to understand if immunotherapy could represent a valid option in this disease, and which subset of patients may be more likely to benefit. Current evidence suggests that the tumor microenvironment needs a qualitative rather than quantitative evaluation, along with the genomic determinants of prostate tumor cells. The prognostic or predictive value of immunotherapy biomarkers, such as PD-L1, TMB, or dMMR/MSI-high, needs further evaluation in PCa. Monotherapy with immune checkpoint inhibitors (ICIs) has been modestly effective. In contrast, combined strategies with other standard treatments (hormonal agents, chemotherapy, PARP inhibitors, radium-223, and TKIs) have shown some results. Immunotherapy should be better investigated in biomarker-selected patients, particularly with specific pathway aberrations (e.g., AR-V7 variant, HRD, CDK12 inactivated tumors, MSI-high tumors). Lastly, we present new possible targets in PCa that could potentially modulate the tumor microenvironment and improve antitumor activity with ICIs

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones