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Background: This work investigated the impact of different cardiac gating methods on the assessment of cardiac function by FDG-PET in a cross-validation PET/MR study., Methods and Results: MR- and PET-based left ventricular end-diastolic, end-systolic volumes, and ejection fraction (EDV, ESV, and EF) were delineated in 30 patients with a PET/MR examination. Cardiac PET imaging was performed using three ECG gating methods: fixed number of gates per beat (STD), STD with a beat acceptance window (STD-BR), and fixed gate duration (FW). High MR-PET correlations were found in all the values. ESVs correlated better than EDVs and EFs: Pearson's r coefficient [0.92, 0.92, 0.92] in ESV vs [0.75, 0.81, 0.80] in EDV and [0.79, 0.91, 0.87] in EF, for each method [STD, STD-BR, FW]. Biases with respect to MRI for all the evaluated PET methods were less than 13% in EDV, 5% in ESV, and 14% in EF, but with wide limits of agreements, in the range (59-68)% in EDV, (65-70)% in ESV, and (49-71)% in EF. STD showed the strongest disagreement, while there were no marked differences between STD-BR and FW., Conclusion: Based on these findings, PET- and MR-based cardiac function parameters were highly correlated but in substantial disagreement with variabilities introduced by the selected PET ECG gating method. The most significant differences were associated with the ECG gating method susceptible to highly irregular beats, while similar performance was observed in the methods using uniform adjustment of gates width per beat with the beat acceptance window, and fixed gate width along all the beats. Thus, strict quality controls of R peak detection are needed to minimize its impact on the function assessment., (© 2022. The Author(s).)

Background: In patients with increasing PSA and suspicion for prostate cancer, but previous negative biopsies, PET/MRI is used to test for tumours and target potential following biopsy. We aimed to determine different PSMA PET timing effects on signal kinetics and test its correlation with the patients' PSA and Gleason scores (GS)., Methods: A total of 100 patients were examined for 900 s using PET/MRI approximately 1-2 h p.i. depending on the tracer used ( 68 Ga-PSMA-11, 18 F-PSMA-1007 or 18 F-rhPSMA7). The scans were reconstructed in static and dynamic mode (6 equal frames capturing "late" PSMA dynamics). TACs were computed for detected lesions as well as linear regression plots against time for static (SUV) and dynamic (SUV, SUL, and percent injected dose per gram) parameters. All computed trends were tested for correlation with PSA and GS., Results: Static and dynamic scans allowed unchanged lesion detection despite the difference in statistics. For all tracers, the lesions in the pelvic lymph nodes and bones had a mostly negative activity concentration trend (78% and 68%, resp.), while a mostly positive, stronger trend was found for the lesions in the prostate and prostatic fossa following RPE (84% and 83%, resp.). In case of 68 Ga-PSMA-11, a strong negative (R min  = - 0.62, R max  = - 0.73) correlation was found between the dynamic parameters and the PSA. 18 F-PSMA-1007 dynamic data showed no correlation with PSA, while for 18 F-rhPSMA7 dynamic data, it was consistently low positive (R min  = 0.29, R max  = 0.33). All tracers showed only moderate correlation against GS (R min  = 0.41, R max  = 0.48). The static parameters showed weak correlation with PSA (R min  = 0.24, R max  = 0.36) and no correlation with GS., Conclusion: "Late" dynamic PSMA data provided additional insight into the PSMA kinetics. While a stable moderate correlation was found between the PSMA kinetics in pelvic lesions and GS, a significantly variable correlation with the PSA values was shown depending on the radiotracer used, the highest being consistently for 68 Ga-PSMA-11. We reason that with such late dynamics, the PSMA kinetics are relatively stable and imaging could even take place at earlier time points as is now in the clinical routine., (© 2023. The Author(s).)

When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients., Methods: Patients with PCa, who underwent [ 68  Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1-3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student's t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS)., Results: All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS., Conclusion: All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics., (© 2021. The Author(s).)

Purpose: After a decade of PET/MR, the case of attenuation correction (AC) remains open. The initial four-compartment (air, water, fat, soft tissue) Dixon-based AC scheme has since been expanded with several features, the latest being MR field-of-view extension and a bone atlas. As this potentially changes quantification, we evaluated the impact of these features in PET AC in prostate cancer patients., Methods: Two hundred prostate cancer patients were examined with either 18 F- or 68 Ga-prostate-specific membrane antigen (PSMA) PET/MR. Qualitative and quantitative analysis (SUV mean , SUV max , correlation, and statistical significance) was performed on images reconstructed using different AC schemes: Dixon, Dixon+MLAA, Dixon+HUGE, and Dixon+HUGE+bones for 18 F-PSMA data; Dixon and Dixon+bones for 68 Ga-PSMA data. Uptakes were compared using linear regression against standard Dixon., Results: High correlation and no visually perceivable differences between all evaluated methods (r > 0.996) were found. The mean relative difference in lesion uptake of 18 F-PSMA and 68 Ga-PSMA remained, respectively, within 4% and 3% in soft tissue, and within 10% and 9% in bones for all evaluated methods. Bone registration errors were detected, causing mean uptake change of 5% in affected lesions., Conclusions: Based on these results and the encountered bone atlas registration inaccuracy, we deduce that including bones and extending the MR field-of-view did not introduce clinically significant differences in PSMA diagnostic accuracy and tracer uptake quantification in prostate cancer pelvic lesions, facilitating the analysis of serial studies respectively. However, in the absence of ground truth data, we advise against atlas-based methods when comparing serial scans for bone lesions.

Purpose: Positron emission tomography (PET) agents targeting the prostate-specific membrane antigen (PSMA) are currently under broad clinical and scientific investigation. (68)Ga-PSMA HBED-CC constitutes the first (68)Ga-labelled PSMA-inhibitor and has evolved as a promising agent for imaging PSMA expression in vivo. The aim of this study was to evaluate the whole-body distribution and radiation dosimetry of this new probe., Methods: Five patients with a history or high suspicion of prostate cancer were injected intravenously with a mean of 139.8 ± 13.7 MBq of (68)Ga-PSMA HBED-CC (range 120-158 MBq). Four static skull to mid-thigh scans using a whole-body fully integrated PET/MR-system were performed 10 min, 60 min, 130 min, and 175 min after the tracer injection. Time-dependent changes of the injected activity per organ were determined. Mean organ-absorbed doses and effective doses (ED) were calculated using OLINDA/EXM., Results: Injection of a standard activity of 150 MBq (68)Ga-PSMA HBED-CC resulted in a median effective dose of 2.37 mSv (Range 1.08E-02 - 2.46E-02 mSv/MBq). The urinary bladder wall (median absorbed dose 1.64E-01 mGv/MBq; range 8.76E-02 - 2.91E-01 mGv/MBq) was the critical organ, followed by the kidneys (median absorbed dose 1.21E-01 mGv/MBq; range 7.16E-02 - 1.75E-01), spleen (median absorbed dose 4.13E-02 mGv/MBq; range 1.57E-02 - 7.32E-02 mGv/MBq) and liver (median absorbed dose 2.07E-02 mGv/MBq; range 1.80E-02 - 2.57E-02 mGv/MBq). No drug-related pharmacological effects occurred., Conclusion: The use of (68)Ga-PSMA HBED-CC results in a relatively low radiation exposure, delivering organ doses that are comparable to those of other (68)Ga-labelled PSMA-inhibitors used for PET-imaging. Total effective dose is lower than for other PET-agents used for prostate cancer imaging (e.g. (11)C- and (18)F-Choline).

Unlabelled: We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners., Methods: Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed., Results: The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement., Conclusion: We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks., (© 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

Objective: The aim of this study was to demonstrate the feasibility of high-resolution 3-dimensional aortic vessel wall imaging using a novel elastin-specific magnetic resonance contrast agent (ESMA) in a large animal model., Materials and Methods: The thoracic aortic vessel wall of 6 Landrace pigs was imaged using a novel ESMA and a nonspecific control agent. On day 1, imaging was performed before and after the administration of a nonspecific control agent, gadolinium diethylenetriamine pentaacetic acid (Gd-DTPA; Bayer Schering AG, Berlin, Germany). On day 3, identical scans were repeated before and after the administration of a novel ESMA (Lantheus Medical Imaging, North Billerica, Massachusetts). Three-dimensional inversion recovery gradient echo delayed-enhancement imaging and magnetic resonance (MR) angiography of the thoracic aortic vessel wall were performed on a 1.5-T MR scanner (Achieva; Philips Medical Systems, the Netherlands). The signal-to-noise ratio and the contrast-to-noise ratio of arterial wall enhancement, including the time course of enhancement, were assessed for ESMA and Gd-DTPA. After the completion of imaging sessions, histology, electron microscopy, and inductively coupled plasma mass spectroscopy were performed to localize and quantify the gadolinium bound to the arterial vessel wall., Results: Administration of ESMA resulted in a strong enhancement of the aortic vessel wall on delayed-enhancement imaging, whereas no significant enhancement could be measured with Gd-DTPA. Ninety to 100 minutes after the administration of ESMA, significantly higher signal-to-noise ratio and contrast-to-noise ratio could be measured compared with the administration of Gd-DTPA (45.7 ± 9.6 vs 13.2 ± 3.5, P < 0.05 and 41.9 ± 9.1 vs 5.2 ± 2.0, P < 0.05). A significant correlation (0.96; P < 0.01) between area measurements derived from ESMA scans and aortic MR angiography scans could be found. Electron microscopy and inductively coupled plasma mass spectroscopy confirmed the colocalization of ESMA with elastic fibers., Conclusion: We demonstrate the feasibility of aortic vessel wall imaging using a novel ESMA in a large animal model under conditions resembling a clinical setting. Such an approach could be useful for the fast 3-dimensional assessment of the arterial vessel wall in the context of atherosclerosis, aortic aneurysms, and hypertension.

Background: The extracellular matrix (ECM) plays an important role in the pathogenesis of atherosclerosis and in-stent restenosis. Elastin is an essential component of the ECM. ECM degradation can lead to plaque destabilization, whereas enhanced synthesis typically leads to vessel wall remodeling resulting in arterial stenosis or in-stent restenosis after stent implantation. The objective of this study was to demonstrate the feasibility of MRI of vascular remodeling using a novel elastin-binding contrast agent (BMS-753951)., Methods and Results: Coronary injury was induced in 6 pigs by endothelial denudation and stent placement. At day 28, delayed-enhancement MRI coronary vessel wall imaging was performed before and after injection of gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA). Two days later, DE-MRI was repeated after administration of BMS-753951. Contrast-to-noise-ratio and areas of enhancement were determined. Delayed-enhancement MRI with BMS-753951 caused strong enhancement of the aortic, pulmonary artery, and injured coronary artery walls, whereas Gd-DTPA did not. Delayed-enhancement MRI of the stented coronary artery with BMS-753951 yielded a 3-fold higher contrast-to-noise-ratio when compared with the balloon-injured and control coronary artery (21±6 versus 7±3 versus 6±4; P<0.001). The area of enhancement correlated well with the area of remodeling obtained from histological data (R(2)=0.86, P<0.05)., Conclusions: We demonstrate the noninvasive detection and quantification of vascular remodeling in an animal model of coronary vessel wall injury using an elastin-specific MR contrast agent. This novel approach may be useful for the assessment of coronary vessel wall remodeling in patients with suspected coronary artery disease. Further studies in atherosclerotic animal models and degenerative ECM disease are now warranted.

Objectives: Our aim was to determine whether serial contrast-enhanced cardiac magnetic resonance (CE-CMR) is useful for the characterization of tissue signal changes within the coronary vessel wall in patients after acute myocardial infarction (AMI)., Background: Inflammation plays a key role in the development of AMI. CE-CMR of the vessel wall has been found useful for the characterization of inflammatory tissue signal changes in patients with carotid artery stenosis, giant cell arteritis, or Takayasu's arteritis; however, it has never been serially performed in the coronary artery wall in patients with acute and chronic myocardial infarction using a gadolinium-based contrast medium and compared with systemic markers of inflammation., Methods: CE-CMR using a T1-weighted 3-dimensional gradient echo inversion recovery sequence of the coronary artery wall and 0.2 mmol/kg of gadolinium-diethylenetriaminepentaacetic acid was performed in 10 patients with AMI 6 days and 3 months after coronary intervention and in 9 subjects without coronary artery disease on invasive coronary angiography. Contrast-to-noise ratio (CNR) within the coronary artery wall was quantified in comparison with blood signal., Results: Patients with AMI demonstrated a significantly increased coronary vessel wall enhancement 6 days after infarction compared with normal subjects (CNR 7.8 +/- 4.4 vs. 5.3 +/- 3.2, p < 0.001). Three months after infarction, CNR decreased to 6.5 +/- 4.7 (p < 0.03). This decrease paralleled declines in C-reactive protein. Angiographically normal segments showed no contrast changes, but CNR significantly decreased in stenotic segments, from 10.9 +/- 3.8 to 6.8 +/- 5.0 (p < 0.002), resulting in a reduction of enhanced segments from 70% to 25% (p < 0.01)., Conclusions: Serial CE-CMR identified changes in spatial extent and intensity of coronary contrast enhancement in patients after AMI. This technique may be useful for the characterization of transient coronary tissue signal changes, which may represent edema or inflammation during the post-infarction phase. In addition, CE-CMR may offer the potential for visualization of inflammatory activity in atherosclerosis associated with acute coronary syndromes.

Introduction: Recent evidence suggests the blood-to-brain influx rate (K1) in TSPO PET imaging as a promising biomarker of blood-brain barrier (BBB) permeability alterations commonly associated with peripheral inflammation and heightened immune activity in the brain. However, standard compartmental modeling quantification is limited by the requirement of invasive and laborious procedures for extracting an arterial blood input function. In this study, we validate a simplified blood-free methodologic framework for K1 estimation by fitting the early phase tracer dynamics using a single irreversible compartment model and an image-derived input function (1T1K-IDIF). Methods: The method is tested on a multi-site dataset containing 177 PET studies from two TSPO tracers ([11C]PBR28 and [18F]DPA714). Firstly, 1T1K-IDIF K1 estimates were compared in terms of both bias and correlation with standard kinetic methodology. Then, the method was tested on an independent sample of [11C]PBR28 scans before and after inflammatory interferon-α challenge, and on test-retest dataset of [18F]DPA714 scans. Results: Comparison with standard kinetic methodology showed good-toexcellent intra-subject correlation for regional 1T1K-IDIF-K1 (ρintra = 0.93 ± 0.08), although the bias was variable depending on IDIF ability to approximate blood input functions (0.03-0.39 mL/cm3/min). 1T1K-IDIF-K1 unveiled a significant reduction of BBB permeability after inflammatory interferon-α challenge, replicating results from standard quantification. High intra-subject correlation (ρ = 0.97 ± 0.01) was reported between K1 estimates of test and retest scans. Discussion: This evidence supports 1T1K-IDIF as blood-free alternative to assess TSPO tracers' unidirectional blood brain clearance. K1 investigation could complement more traditional measures in TSPO studies, and even allow further mechanistic insight in the interpretation of TSPO signal. [ABSTRACT FROM AUTHOR]

Prostate cancer represents a significant public health challenge, with its management requiring precise diagnostic and prognostic tools. Prostate-specific membrane antigen (PSMA), a cell surface enzyme overexpressed in prostate cancer cells, has emerged as a pivotal biomarker. PSMA's ability to increase the sensitivity of PET imaging has revolutionized its application in the clinical management of prostate cancer. The advancements in PET-PSMA imaging technologies and methodologies, including the development of PSMA-targeted radiotracers and optimized imaging protocols, led to diagnostic accuracy and clinical utility across different stages of prostate cancer. This highlights its superiority in staging and its comparative effectiveness against conventional imaging modalities. This paper analyzes the impact of PET-PSMA on prostate cancer management, discussing the existing challenges and suggesting future research directions. The integration of recent studies and reviews underscores the evolving understanding of PET-PSMA imaging, marking its significant but still expanding role in clinical practice. This comprehensive review serves as a crucial resource for clinicians and researchers involved in the multifaceted domains of prostate cancer diagnosis, treatment, and management. [ABSTRACT FROM AUTHOR]

Purpose: During [(90)Y]DOTATOC therapy, for determination of kidney doses a conventional approach using co-injected [(111)In]DOTATOC was evaluated for validity, reliability and reproducibility as well as for the influence of methodological variations and bremsstrahlung. Biologically effective doses were estimated by calculating the relative effectiveness (RE) of kidney doses., Methods: Fractionated [(90)Y]DOTATOC therapy (n=20 patients, 3.1+/-0.7 GBq/therapy cycle, 45 therapy cycles) included co-injection of 157+/-37 MBq [(111)In]DOTATOC and a nephroprotective infusion regimen. From serial gamma camera/probe measurements, individual region of interest (ROI) sets were established and kidney doses were determined according to MIRDOSE3 (corrected for individual kidney mass) by use of three methodological variants: (1) correction for interfering organs (liver/spleen) and background activity, (2) correction for interfering organs alone and (3) no corrections. A phantom study was performed with (111) In alone and with (111)In +(90)Y to estimate the influence of (90)Y bremsstrahlung., Results: Mean kidney dose (method 1, n=20 patients, 20 therapy cycles) was 1.51+/-0.60 Gy/GBq [(90)Y]DOTATOC (1.41+/-0.48 Gy/GBq for n=20 patients, 45 therapy cycles). With partial correction (method 2) or no correction (method 3) for interfering activity, kidney doses increased significantly, to 2.71+/-1.26 Gy/GBq and 3.15+/-1.22 Gy/GBq, respectively. The span of REs ranged from 1.02 to 1.24. Inter-observer variability was as high as +/-32% (+/-2SD). (90)Y bremsstrahlung accounted for a 4-11% underestimation of obtained target activity., Conclusion: The obtained kidney doses are highly influenced by methodological variations. Full correction for interfering activity clearly underestimates kidney doses. By comparison, (90)Y bremsstrahlung and variability in the relative effectiveness of kidney doses cause minor bias. Inter-observer variability must be considered when interpreting kidney doses.

Simple Summary: The contemporary development of radiomics offers an opportune methodology for the interpretation of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT). While both technologies are relatively new for consideration of clinical integration, the present exploration seeks to review current literature on their intersection. Review of twenty-three peer-reviewed articles revealed promising results for the use of PSMA PET/CT-derived radiomics in the prediction of biopsy Gleason score, adverse pathology, and treatment outcomes for prostate cancer (PC). Clinical integration of these findings, however, are limited by lack of biologic validation and reproducible methodology. The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85–0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719–0.84 and 0.84–0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698–0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized. [ABSTRACT FROM AUTHOR]

Background: In patients with prostate cancer (PCa), imaging with gastrin-releasing peptide receptor (GRPR) ligands is an alternative to PSMA-targeted tracers, particularly if PSMA expression is low or absent. [99mTc]Tc-N4-BTG is a newly developed GRPR-directed probe for conventional scintigraphy and single photon emission computed tomography (SPECT) imaging. The current study aims to investigate the safety, biodistribution and dosimetry of [99mTc]Tc-N4-BTG in patients with biochemical recurrence (BCR) of PCa. Results: No adverse pharmacologic effects were observed. Injection of [99mTc]Tc-N4-BTG resulted in an effective dose of 0.0027 ± 0.0002 mSv/MBq. The urinary bladder was the critical organ with the highest mean absorbed dose of 0.028 ± 0.001 mGy/MBq, followed by the pancreas with 0.0043 ± 0.0015 mGy/MBq, osteogenic cells with 0.0039 ± 0.0005 mGy/MBq, the kidneys with 0.0034 ± 0.0003 mGy/MBq, and the liver with 0.0019 ± 0.0004 mGy/MBq, respectively. No focal tracer uptake suggestive of PCa recurrence could be revealed for any of the patients. Conclusion: [99mTc]Tc-N4-BTG appears to be a safe diagnostic agent. Compared to GRPR-targeted PET tracers, this 99mTc-labelled SPECT agent could contribute to a broader application and better availability of this novel approach. Further research to assess its clinical value is warranted. [ABSTRACT FROM AUTHOR]

(1) Background: PSMA ligand PET/CT is increasingly important for diagnostics of prostate cancer and other tumor diseases. In particular, the radiopharmaceutical [68Ga]Ga-PSMA-11 is widely used. Besides its tumor-specific binding, the uptake within the kidneys is dominant and seems to visualize the renal cortex specifically. Kidney diseases may alter the uptake of radiopharmaceuticals. Therefore, the correlation between renal uptake in PET/CT imaging and renal function should be investigated. (2) Methods: A group of 103 male patients were retrospectively evaluated for eGFR according to the CKD-EPI equation, tracer uptake intensity (SUVmax, SUVpeak, SUVmean), the molecular volume of the renal cortex, morphological kidney size, and total renal uptake. Manual and three different computer-assisted contouring methods (thresholds at 50% of SUVmax, 30% of SUVmax, and absolute SUV of 20) were used for measurements. Correlations between parameters were calculated using linear regression models. (3) Results: Renal SUVmax, SUVpeak, and SUVmean do not correlate with eGFR for manual or computer-assisted measurements. In contrast, molecular cortex volume shows a moderate correlation with eGFR (R2 = 0.231, p < 0.001), superior to morphological kidney size. A contouring threshold of 30% of SUVmax outperformed the other settings for renal cortex volume and total renal uptake. (4) Conclusions: Renal uptake of [68Ga]Ga-PSMA-11 cannot predict eGFR, but the functional renal cortex can be quantified by PET/CT imaging. [ABSTRACT FROM AUTHOR]

Background: Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer. Methods: Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC). Results: Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86). Conclusions: The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis. [ABSTRACT FROM AUTHOR]

Objectives: To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate-specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy)., Materials and Methods: This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: 'artificial intelligence', 'prostate cancer', and 'PSMA PET'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used., Results: Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high-grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non-PSMA-avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume., Conclusion: Although the current state of AI differentiating high-grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed., (© 2024 The Author(s). BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Hybrid imaging consists of a combination of two or more imaging modalities, which equally contribute to image information. To date, hybrid cardiovascular imaging can be performed by either merging images acquired on different scanners, or with truly hybrid PET/CT and PET/MR scanners. The European Association of Nuclear Medicine (EANM), and the European Association of Cardiovascular Imaging (EACVI) of the European Society of Cardiology (ESC) aim to review clinical situations that may benefit from the use of hybrid cardiac imaging and provide advice on acquisition protocols providing the most relevant information to reach diagnosis in various clinical situations., (© 2024. The Author(s).)

Background: Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18 F-PSMA-1007 PET/CT of PPAT., Methods: Data from 268 prostate cancer (PCa) patients who underwent 18 F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA., Results: The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78-0.91), 0.77 (95% CI: 0.62-0.91) and 0.84 (95% CI: 0.70-0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram., Conclusion: The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The effectiveness and precision of disease diagnosis and treatment have increased, thanks to developments in clinical imaging over the past few decades. Science is developing and progressing steadily in imaging modalities, and effective outcomes are starting to show up as a result of the shorter scanning periods needed as well as the higher-resolution images generated. The choice of one clinical device over another is influenced by technical disparities among the equipment, such as detection medium, shorter scan time, patient comfort, cost-effectiveness, accessibility, greater sensitivity and specificity, and spatial resolution. Lately, computational algorithms, artificial intelligence (AI), in particular, have been incorporated with diagnostic and treatment techniques, including imaging systems. AI is a discipline comprised of multiple computational and mathematical models. Its applications aided in manipulating sophisticated data in imaging processes and increased imaging tests' accuracy and precision during diagnosis. Computed tomography (CT), positron emission tomography (PET), and Single Photon Emission Computed Tomography (SPECT) along with their corresponding radiation detectors have been reviewed in this study. This review will provide an in-depth explanation of the above-mentioned imaging modalities as well as the radiation detectors that are their essential components. From the early development of these medical instruments till now, various modifications and improvements have been done and more is yet to be established for better performance which calls for a necessity to capture the available information and record the gaps to be filled for better future advances. [ABSTRACT FROM AUTHOR]

Urinary tumors primarily consist of kidney, urothelial, and prostate malignancies, which pose significant treatment challenges, particularly in advanced stages. Antibody-drug conjugates (ADCs) have emerged as a promising therapeutic approach, combining monoclonal antibody specificity with cytotoxic chemotherapeutic payloads. This review highlights recent advancements, opportunities, and challenges in ADC application for urinary tumors. We discuss the FDA-approved ADCs and other novel ADCs under investigation, emphasizing their potential to improve patient outcomes. Furthermore, we explore strategies to address challenges, such as toxicity management, predictive biomarker identification, and resistance mechanisms. Additionally, we examine the integration of ADCs with other treatment modalities, including immune checkpoint inhibitors, targeted therapies, and radiation therapy. By addressing these challenges and exploring innovative approaches, the development of ADCs may significantly enhance therapeutic options and outcomes for patients with advanced urinary tumor. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the role of [ 18 F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective study included 143 PCa patients who underwent [ 18 F]-PSMA-1007 PET/CT imaging. PCa areas were manually contoured on PET images and 1781 image biomarker standardization initiative (IBSI)-compliant radiomics features were extracted. A 30 times iterated preliminary analysis pipeline, comprising of the least absolute shrinkage and selection operator (LASSO) for feature selection and fivefold cross-validation for model optimization, was adopted to identify the most robust features to dataset variations, select candidate models for ensemble modelling, and optimize hyperparameters. Thirteen subsets of selected features, 11 generated from the preliminary analysis plus two additional subsets, the first based on the combination of robust and fine-tuning features, and the second only on fine-tuning features were used to train the model ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, and f-score values were calculated to provide models' performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction for multiple comparisons, was used to verify if statistically significant differences were found in the different ensemble models over the 30 iterations. The model ensemble trained with the combination of robust and fine-tuning features obtained the highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), and f-score (78.26%). Statistically significant differences (p < 0.05) were found for some performance metrics. These findings support the role of [ 18 F]-PSMA-1007 PET radiomics in improving risk stratification for PCa, by reducing dependence on biopsies., (© 2024. The Author(s).)

Objectives: To develop an end-to-end radiomics-based pipeline for the prediction of International Society of Urological Pathology grade group (ISUP GG) in prostate cancer (PCa)., Methods: This retrospective study includes 356 patients (241 in training set and 115 in independent test set) with histopathologically confirmed PCa who underwent [ 18 F]PSMA-1007 PET/CT scan. Patients were classified into two groups according to their ISUP GG (1-3 vs. 4-5). Radiomics features were extracted from the whole, automatically segmented prostate on PET/CT images, 30 models were constructed by combining 6 feature selection algorithms and 5 machine learning classifiers. The clinical model incorporated age, total prostate-specific antigen (tPSA), maximum standardized uptake value (SUVmax), and prostate volume. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), balanced accuracy (bAcc), and decision curve analysis (DCA)., Results: The best-performing radiomics model significantly outperformed clinical model (AUC 0.879 ± 0.041 vs. 0.799 ± 0.051, bAcc 0.745 ± 0.074 vs. 0.629 ± 0.045). On an external independent test set, best-performing radiomics model perform better than clinical model, with an AUC of 0.861 vs. 0.750, p = 0.002 (Delong), and bAcc of 0.764 vs. 0.582, p = 0.043 (McNemar). The learning curve, calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice., Conclusion: The end-to-end radiomics-based pipeline is an effective non-invasive tool to predict ISUP GG in PCa., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Recognition of the importance of prostate-specific membrane antigen (PSMA) PET/CT in the diagnosis of prostate cancer has steadily increased following the publication of extensive data on its diagnostic accuracy and impact on patient management over the past decade. Several recent clinical trials and investigations regarding PSMA PET/CT have been ongoing in our country, and this examination is expected to become increasingly widespread in the future. This review explains the characteristics of PSMA PET/CT, its diagnostic capabilities and superiority over other modalities, the three proposed PSMA PET/CT interpretation criteria (the European Association of Nuclear Medicine [EANM], the Prostate Cancer Molecular Imaging Standardized Evaluation [PROMISE], and the PSMA Reporting and Data System [PSMA-RADS]), and the application of PSMA PET/CT to prostate cancer treatment (improvement of local control, irradiation of oligometastases, and salvage radiotherapy), incorporating actual clinical images and the latest findings., (© 2024. The Author(s).)

Background: Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). Multiple nomograms, which include magnetic resonance imaging (MRI) information, are available predict side-specific EPE. It is crucial that the accuracy of these nomograms is assessed with external validation to ensure they can be used in clinical practice to support medical decision-making., Methods: Data of prostate cancer (PCa) patients that underwent robot-assisted RP (RARP) from 2017 to 2021 at four European tertiary referral centers were collected retrospectively. Four previously developed nomograms for the prediction of side-specific EPE were identified and externally validated. Discrimination (area under the curve [AUC]), calibration and net benefit of four nomograms were assessed. To assess the strongest predictor among the MRI features included in all nomograms, we evaluated their association with side-specific EPE using multivariate regression analysis and Akaike Information Criterion (AIC)., Results: This study involved 773 patients with a total of 1546 prostate lobes. EPE was found in 338 (22%) lobes. The AUCs of the models predicting EPE ranged from 72.2% (95% CI 69.1-72.3%) (Wibmer) to 75.5% (95% CI 72.5-78.5%) (Nyarangi-Dix). The nomogram with the highest AUC varied across the cohorts. The Soeterik, Nyarangi-Dix, and Martini nomograms demonstrated fair to good calibration for clinically most relevant thresholds between 5 and 30%. In contrast, the Wibmer nomogram showed substantial overestimation of EPE risk for thresholds above 25%. The Nyarangi-Dix nomogram demonstrated a higher net benefit for risk thresholds between 20 and 30% when compared to the other three nomograms. Of all MRI features, the European Society of Urogenital Radiology score and tumor capsule contact length showed the highest AUCs and lowest AIC., Conclusion: The Nyarangi-Dix, Martini and Soeterik nomograms resulted in accurate EPE prediction and are therefore suitable to support medical decision-making., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Simple Summary: The peritoneum, a common metastatic site of colorectal cancer (CRC), has a high incidence and poor prognosis that makes it difficult to diagnose early. Peritoneal metastasis (PM) depends on the synergistic action of multiple molecules and the regulation of various components of the tumor microenvironment. A multidisciplinary combination approach is still recommended for treating the disease currently. Cytoreductive surgery (CRS) combined with intraperitoneal chemotherapy (IPC) may benefit patients with CRC-PM, but further clinical trials and higher-level evidence-based medical evidence are needed. Peritoneal metastasis (PM) is a common mode of distant metastasis in colorectal cancer (CRC) and has a poorer prognosis compared to other metastatic sites. The formation of PM foci depends on the synergistic effect of multiple molecules and the modulation of various components of the tumor microenvironment. The current treatment of CRC-PM is based on systemic chemotherapy. However, recent developments in local therapeutic modalities, such as cytoreductive surgery (CRS) and intraperitoneal chemotherapy (IPC), have improved the survival of these patients. This article reviews the research progress on the mechanism, characteristics, diagnosis, and treatment strategies of CRC-PM, and discusses the current challenges, so as to deepen the understanding of CRC-PM among clinicians. [ABSTRACT FROM AUTHOR]

High-grade serous ovarian cancer (HGSOC) is an aggressive disease with different clinical outcomes and poor prognosis. This could be due to tumor heterogeneity. The 18F-FDG PET radiomic parameters permit addressing tumor heterogeneity. Nevertheless, this has been not well studied in ovarian cancer. The aim of our work was to assess the prognostic value of pretreatment 18F-FDG PET radiomic features in patients with HGSOC. A review of 36 patients diagnosed with advanced HGSOC between 2016 and 2020 in our center was performed. Radiomic features were obtained from pretreatment 18F-FDGPET. Disease-free survival (DFS) and overall survival (OS) were calculated. Optimal cutoff values with receiver operating characteristic curve/median values were used. A correlation between radiomic features and DFS/OS was made. The mean DFS was 19.6 months and OS was 37.1 months. Total Lesion Glycolysis (TLG), GLSZM_ Zone Size Non-Uniformity (GLSZM_ZSNU), and GLRLM_Run Length Non-Uniformity (GLRLM_RLNU) were significantly associated with DFS. The survival-curves analysis showed a significant difference of DSF in patients with GLRLM_RLNU > 7388.3 versus patients with lower values (19.7 months vs. 31.7 months, p = 0.035), maintaining signification in the multivariate analysis (p = 0.048). Moreover, Intensity-based Kurtosis was associated with OS (p = 0.027). Pretreatment 18F-FDG PET radiomic features GLRLM_RLNU, GLSZM_ZSNU, and Kurtosis may have prognostic value in patients with advanced HGSOC. [ABSTRACT FROM AUTHOR]

Introduction: Radiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients. Methods: Patients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC). Results: The CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p<0.05). However, validation AUCs for LNI and ECE prediction were not significant (0.57 and 0.63, respectively). Conventional PET metrics-based models had comparable AUCs for LNI (0.59, p>0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, p<0.05). In general, Combat harmonization improved external validation AUCs (-0.03 to +0.18). Conclusion: In internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to certain imaging modalities. To address this issue, medical image synthesis can generate one modality from another, but many existing models struggle with high-quality image synthesis when multiple slices are present in the dataset. This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices. The model integrates a dual contrast loss term with the CycleGAN loss to ensure that the synthesized images are distinguishable from the source domain. Additionally, an attention mechanism is incorporated into the generators to extract informative features from both channel and spatial domains. To improve performance when dealing with multiple slices, the K-means algorithm is used to cluster the dataset into K groups, and each group is used to train a separate ADC-cycleGAN. Experimental results demonstrate that the proposed ADC-cycleGAN model produces comparable samples to other state-of-the-art generative models, achieving the highest PSNR and SSIM values of 19.04385 and 0.68551, respectively. We publish the code at https://github.com/JiayuanWang-JW/ADC-cycleGAN. [ABSTRACT FROM AUTHOR]

Simple Summary: The huge armamentarium of currently available theragnostic modalities allows a novel approach to prostate cancer from imaging to therapy. Clinical examination is the starting-point, then radiology and nuclear medicine are often needed to define the illness grading to set up the best therapeutic strategy. Prostate cancer care horizons are opening with the aid of nuclear medicine, which takes advantage of the technological ascendancy of prostate-specific membrane antigen-based imaging and therapy and is currently evolving with machine-learning approaches. We have focused our review on the current state, on the advancements, and on the future prospects of nuclear medicine modalities that could change prostate cancer's standard of care. Prostate cancer is the most frequent epithelial neoplasia after skin cancer in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. Prostate cancer imaging includes several radiological modalities, ranging from ultrasonography, computed tomography (CT), and magnetic resonance to nuclear medicine hybrid techniques such as single-photon emission computed tomography (SPECT)/CT and positron emission tomography (PET)/CT. Innovation in radiopharmaceutical compounds has introduced specific tracers with diagnostic and therapeutic indications, opening the horizons to targeted and very effective clinical care for patients with prostate cancer. The aim of the present review is to illustrate the current knowledge and future perspectives of nuclear medicine, including stand-alone diagnostic techniques and theragnostic approaches, in the clinical management of patients with prostate cancer from initial staging to advanced disease. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients. Methods: One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA). Results: The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539–0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619–0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit. Conclusions: Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients. Key Points: • Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention. [ABSTRACT FROM AUTHOR]

To investigate the use of kinetic parameters derived from direct Patlak reconstructions of [68Ga]Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) to predict the histological grade of malignancy of the primary tumor of patients with prostate cancer (PCa). Thirteen patients (mean age 66 ± 10 years) with a primary, therapy-naïve PCa (median PSA 9.3 [range: 6.3–130 µg/L]) prior radical prostatectomy, were recruited in this exploratory prospective study. A dynamic whole-body [68Ga]Ga-PSMA-11 PET/CT scan was performed for all patients. Measured quantification parameters included Patlak slope (Ki: absolute rate of tracer consumption) and Patlak intercept (Vb: degree of tracer perfusion in the tumor). Additionally, the mean and maximum standardized uptake values (SUVmean and SUVmax) of the tumor were determined from a static PET 60 min post tracer injection. In every patient, initial PSA (iPSA) values that were also the PSA level at the time of the examination and final histology results with Gleason score (GS) grading were correlated with the quantitative readouts. Collectively, 20 individual malignant prostate lesions were ascertained and histologically graded for GS with ISUP classification. Six lesions were classified as ISUP 5, two as ISUP 4, eight as ISUP 3, and four as ISUP 2. In both static and dynamic PET/CT imaging, the prostate lesions could be visually distinguished from the background. The average values of the SUVmean, slope, and intercept of the background were 2.4 (±0.4), 0.015 1/min (±0.006), and 52% (±12), respectively. These were significantly lower than the corresponding parameters extracted from the prostate lesions (all p < 0.01). No significant differences were found between these values and the various GS and ISUP (all p > 0.05). Spearman correlation coefficient analysis demonstrated a strong correlation between static and dynamic PET/CT parameters (all r ≥ 0.70, p < 0.01). Both GS and ISUP grading revealed only weak correlations with the mean and maximum SUV and tumor-to-background ratio derived from static images and dynamic Patlak slope. The iPSA demonstrated no significant correlation with GS and ISUP grading or with dynamic and static PET parameter values. In this cohort of mainly high-risk PCa, no significant correlation between [68Ga]Ga-PSMA-11 perfusion and consumption and the aggressiveness of the primary tumor was observed. This suggests that the association between SUV values and GS may be more distinctive when distinguishing clinically relevant from clinically non-relevant PCa. [ABSTRACT FROM AUTHOR]

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories. [ABSTRACT FROM AUTHOR]

Introduction: The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction. Methods: A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([18F]FDG) were used for training and validation, and 17 patients were used for testing (6 [18F]PSMA-1007 and 11 [68Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance. Results: In the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and fiveclass Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUVmax) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%. Conclusion: The proposed method reduces the voxel-based error and SUVmax errors in bone lesions when compared to the four- and five-class Dixon-based AC models. [ABSTRACT FROM AUTHOR]