BACKGROUND: PSMA PET has emerged as a \"gold standard\" imaging modality for assessing prostate cancer metastases. However, it is not universally available, and this limits its impact. In contrast, whole-body MRI is much more widely available but misses more lesions. This study aims to improve the interpretation of whole-body MRI by comparing false negative scans retrospectively to PSMA PET.
METHODS: This study was a retrospective sub-analysis of a prospectively collected database of patients who participated in a clinical trial of PSMA PET/MRI comparing PSMA PET and whole-body MRI from 2018-2021. Subjects whose separately read PSMA PET and MRI diagnostic reports showed discrepancies (\"false negative\" MRI cases) were selected for sub-analysis. The cases were reviewed by the same attending radiologist who originally read the scans. The radiologist noted specific features on MRI indicating metastatic disease that were initially missed.
RESULTS: Of 263 cases, 38 (14%) met the inclusion criteria and were reviewed. Six classes of mpMRI false negatives were identified: anatomically normal (18, 47%), atypical MRI appearance (6, 16%), mischaracterization (1, 3%), undercall (6, 16%), obscured (4, 11%), and no abnormality on MRI (3, 8%). Considering that the atypical and undercalled cases could have been adjusted in retrospect, and that 4 additional cases had positive lesions to the same extent and 11 further cases had disease confined to the pelvis, only 11 (4%) of the original 263 would have had disease outside of a conventional radiation treatment plan.
CONCLUSIONS: Notably, almost 50% of the cases, including most lymph node metastases, were anatomically normal using standard criteria. This suggests that current anatomic criteria for evaluating prostate cancer lymph node metastases are not ideal, and there is a need for improved criteria. In addition, 32% of cases involved some element of human interpretive error, and, therefore, improving reader training may lead to more accurate results.

Performance testing of gamma cameras and single photon computed tomography/computed tomography (SPECT/CT) systems is not subject to regulatory requirements across states and territories in Australia. Internationally recognised testing standards from organisations such as the National Electrical Manufacturers Association (NEMA) describe methodologies for recommended tests. However, variations exist in suggested quality control (QC) schedules from professional bodies such as the Australia and New Zealand Society of Nuclear Medicine (ANZSNM). In this study, a survey was conducted to benchmark current QC programs across a selected sample of eight standalone and networked Australian public hospitals. Vendor-specific flood-field uniformity (intrinsic or extrinsic/system) verification without photomultiplier (PMT) tuning and CT QC were performed at all sites. Weekly and monthly PMT tuning followed by intrinsic flood-field verifications were performed at most sites. At least half of the sites performed monthly centre of rotation (COR) offset verifications. SPECT/CT alignment calibrations and verifications were undertaken by service engineers at all sites, and periodic verifications were performed by local staff at varying frequencies. Variations were observed for other periodic QC tests such as spatial resolution and planar sensitivity. Similarly, variations were observed for tests specific to whole-body systems and SPECT systems. Most sites checked daily and periodic QC results against pass/fail criteria set by vendors. Additional analyses of the QC results, including trend analysis and periodic reviews, were not common practice. The lack of regulatory requirements is likely to have led to variations in QC tests that are generally either harder to perform or are more labour intensive.

OBJECTIVE: To introduce and evaluate a simple method for assessing joint inflammation and structural damage on whole-body MRI (WBMRI) in juvenile idiopathic arthritis (JIA), which is usable in clinical practice.
METHODS: The proposed system utilizes post-contrast Dixon WBMRI scans. Joints are assessed for synovitis (grade 0-2) and structural damage (present/absent) at 81 sites. The synovitis grading is based on features including above-normal intensity synovial enhancement, synovial hypertrophy, joint effusion, subarticular bone marrow oedema and peri-articular soft tissue oedema.This system was evaluated in a prospective study of 60 young people (47 patients with JIA and 13 controls with non-inflammatory musculoskeletal pain) who underwent a WBMRI. Three readers (blinded to diagnosis) independently reviewed all images and re-reviewed 20 individual scans. The intra- and inter-reader overall agreement (OA) and the intra- and inter-reader Gwet\'s agreement coefficients 2 (GAC2) were measured for the detection of a) participants with ≥1 joint with inflammation or structural damage and b) joint inflammation or structural damage for each joint.
RESULTS: The inter-reader OA for detecting patients with ≥1 joint with inflammation, defined as grade 2 synovitis (G2), and ≥1 joint with structural damage were 80% and 73%, respectively. The intra-reader OA for readers 1-3 was 80-90% and 75-90%, respectively. The inter-reader OA and GAC2 for joint inflammation (G2) at each joint were both ≥85% for all joints but were lower if grade 1 synovitis was included as positive.
CONCLUSIONS: The intra- and inter-reader agreements of this WBMRI assessment system are adequate for assessing objective joint inflammation and damage in JIA.

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

OBJECTIVE: To assess the frequency of joint inflammation detected by whole-body MRI (WBMRI) in young people (YP) with JIA and controls, and to determine the relationship between WBMRI-detected inflammation and clinical findings.
METHODS: YP aged 14-24 years, with JIA (patients) or arthralgia without JIA (controls), recruited from one centre, underwent a WBMRI scan after formal clinical assessment. Consensus between at least two of the three independent radiologists was required to define inflammation and damage on WBMRI, according to predefined criteria. YP with JIA were deemed clinically active as per accepted definitions. The proportions of YP with positive WBMRI scans for joint inflammation (one or more inflamed joint) as well as serum biomarkers were compared between active vs inactive JIA patients and controls.
RESULTS: Forty-seven YP with JIA (25 active and 22 inactive patients) and 13 controls were included. WBMRI detected joint inflammation in 60% (28/47) of patients with JIA vs 15% (2/13) of controls (difference: 44%, 95% CI 20%, 68%). More active than inactive JIA patients had WBMRI-detected inflammation [76% (19/25) vs 41% (9/22), difference: 35% (95% CI 9%, 62%)], and this was associated with a specific biomarker signature. WBMRI identified inflammation in one or more clinically inactive joint in 23/47 (49%) patients (14/25 active vs 9/22 inactive JIA patients).
CONCLUSIONS: WBMRI\'s validity in joint assessment was demonstrated by the higher frequency of inflammation in JIA patients vs controls, and in active vs inactive JIA patients. WBMRI found unsuspected joint inflammation in 49% YP with JIA, which needs further investigation of potential clinical implications.

Effective treatment for fibromyalgia (FM) is lacking and further treatment options are needed. Photobiomodulation therapy (PBMT) represents one potential treatment option. Whilst favourable findings have been reported using localised PBMT, no investigations have established the value of whole-body PBMT for the complete set of symptom domains in FM. A single-arm feasibility study was conducted in accordance with CONSORT (Consolidated Standards of Reporting Trials) guidelines. A non-probability sampling method was used to access individuals with FM. The primary outcome measure was identified as the Revised Fibromyalgia Impact Questionnaire (FIQR). Forty-nine participants were screened and twenty-one trial participants entered the trial. Nineteen participants completed the intervention (18 whole-body PBMT sessions over approximately six weeks). Descriptive statistics and qualitative analysis was undertaken to represent feasibility outcomes. Acceptability of the trial device and processes were established. Outcome measures towards efficacy data were guided by core and peripheral OMERACT (outcomes measures in rheumatological clinical trials) domains, utilising a combination of participant-reported and performance-based outcome measures. Data for the embedded qualitative component of the trial were captured by participant-reported experience measures and audio-recorded semi-structured interviews. Positive changes were observed for FM-specific quality of life, pain, tenderness, stiffness, fatigue, sleep disturbance, anxiety, depression and cognitive impairment. Patient global assessment revealed improvements at 6 weeks, with continued effect at 24 weeks. FM-specific quality of life at 24 weeks remained improved compared with baseline scores. The findings provided evidence to support a full-scale trial and showed promise regarding potential efficacy of this novel non-invasive treatment in an FM population.

Objective. Automated organ segmentation on CT images can enable the clinical use of advanced quantitative software devices, but model performance sensitivities must be understood before widespread adoption can occur. The goal of this study was to investigate performance differences between Convolutional Neural Networks (CNNs) trained to segment one (single-class) versus multiple (multi-class) organs, and between CNNs trained on scans from a single manufacturer versus multiple manufacturers.Methods. The multi-class CNN was trained on CT images obtained from 455 whole-body PET/CT scans (413 for training, 42 for testing) taken with Siemens, GE, and Phillips PET/CT scanners where 16 organs were segmented. The multi-class CNN was compared to 16 smaller single-class CNNs trained using the same data, but with segmentations of only one organ per model. In addition, CNNs trained on Siemens-only (N = 186) and GE-only (N = 219) scans (manufacturer-specific) were compared with CNNs trained on data from both Siemens and GE scanners (manufacturer-mixed). Segmentation performance was quantified using five performance metrics, including the Dice Similarity Coefficient (DSC).Results. The multi-class CNN performed well compared to previous studies, even in organs usually considered difficult auto-segmentation targets (e.g., pancreas, bowel). Segmentations from the multi-class CNN were significantly superior to those from smaller single-class CNNs in most organs, and the 16 single-class models took, on average, six times longer to segment all 16 organs compared to the single multi-class model. The manufacturer-mixed approach achieved minimally higher performance over the manufacturer-specific approach.Significance. A CNN trained on contours of multiple organs and CT data from multiple manufacturers yielded high-quality segmentations. Such a model is an essential enabler of image processing in a software device that quantifies and analyzes such data to determine a patient\'s treatment response. To date, this activity of whole organ segmentation has not been adopted due to the intense manual workload and time required.

BACKGROUND: Antibodies that inhibit the programmed cell death protein 1 (PD-1) receptor offer a significant survival benefit, potentially cure (i.e., durable disease-free survival following treatment discontinuation), a substantial proportion of patients with advanced melanoma. Most patients however fail to respond to such treatment or acquire resistance. Previously, we reported that baseline total metabolic tumour volume (TMTV) determined by whole-body [18F]FDG PET/CT was independently correlated with survival and able to predict the futility of treatment. Manual delineation of [18F]FDG-avid lesions is however labour intensive and not suitable for routine use. A predictive survival model is proposed based on automated analysis of baseline, whole-body [18F]FDG images.
METHODS: Lesions were segmented on [18F]FDG PET/CT using a deep-learning approach and derived features were investigated through Kaplan-Meier survival estimates with univariate logrank test and Cox regression analyses. Selected parameters were evaluated in multivariate Cox survival regressors.
RESULTS: In the development set of 69 patients, overall survival prediction based on TMTV, lactate dehydrogenase levels and presence of brain metastases achieved an area under the curve of 0.78 at one year, 0.70 at two years. No statistically significant difference was observed with respect to using manually segmented lesions. Internal validation on 31 patients yielded scores of 0.76 for one year and 0.74 for two years.
CONCLUSIONS: Automatically extracted TMTV based on whole-body [18F]FDG PET/CT can aid in building predictive models that can support therapeutic decisions in patients treated with immune-checkpoint blockade.

Settleable atmospheric particulate matter (SeAPM) containing a mixture of metals, including metallic nanoparticles, has increased throughout the world, and caused environmental and biota contamination. The metal bioconcentration pattern in Nile tilapia (Oreochromis niloticus) was evaluated during a 30-day exposure to 1 g L-1 SeAPM and assessed the human health risk from consuming fish fillets (muscle) based on the estimated daily intake (EDI). SeAPM was collected surrounding an iron ore processing and steel industrial complex in Vitória city (Espírito Santo, Brazil) area. Water samples were collected daily for physicochemical analyses, and every 3 days for multi-elemental analyses. Metal bioconcentrations were determined in the viscera and fillet of fish every 3 days. The elements B, Al, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Rb, Sr, Ag, Cd, Pb, Hg, Ba, Bi, W, Ti, Zr, Y, La, Nb, and Ce were analyzed in SeAPM, water, and fish using inductively coupled plasma mass spectrometry. The metal concentration in SeAPM-contaminated water was higher than in control water. Most metals bioconcentrated preferentially in the fish viscera, except for the Hg and Rb, which bioconcentrated mostly in the fillet. The bioconcentration pattern was Fe > Al > Mn > Pb > V > La > Ce > Y > Ni > Se > As > W > Bi in the viscera; it was higher than the controls throughout the 30-day exposure. Ti, Zr, Nb, Rb, Cd, Hg, B, and Cr showed different bioconcentration patterns. The Zn, Cu, Sr, Sn, Ag, and Ta did not differ from controls. The differences in metal bioconcentration were attributed to diverse metal bioavailability in water and the dissimilar ways fish can cope with each metal, including inefficient excretion mechanisms. The EDI calculation indicated that the consumption of the studied fish is not safe for children, because the concentrations of As, La, Zr, and Hg exceed the World Health Organization\'s acceptable daily intake for these elements.

BACKGROUND: Static [18F]FDG-PET/CT is the imaging method of choice for the evaluation of indeterminate lung lesions and NSCLC staging; however, histological confirmation of PET-positive lesions is needed in most cases due to its limited specificity. Therefore, we aimed to evaluate the diagnostic performance of additional dynamic whole-body PET.
METHODS: A total of 34 consecutive patients with indeterminate pulmonary lesions were enrolled in this prospective trial. All patients underwent static (60 min p.i.) and dynamic (0-60 min p.i.) whole-body [18F]FDG-PET/CT (300 MBq) using the multi-bed-multi-timepoint technique (Siemens mCT FlowMotion). Histology and follow-up served as ground truth. Kinetic modeling factors were calculated using a two-compartment linear Patlak model (FDG influx rate constant = Ki, metabolic rate = MR-FDG, distribution volume = DV-FDG) and compared to SUV using ROC analysis.
RESULTS: MR-FDGmean provided the best discriminatory power between benign and malignant lung lesions with an AUC of 0.887. The AUC of DV-FDGmean (0.818) and SUVmean (0.827) was non-significantly lower. For LNM, the AUCs for MR-FDGmean (0.987) and SUVmean (0.993) were comparable. Moreover, the DV-FDGmean in liver metastases was three times higher than in bone or lung metastases.
CONCLUSIONS: Metabolic rate quantification was shown to be a reliable method to detect malignant lung tumors, LNM, and distant metastases at least as accurately as the established SUV or dual-time-point PET scans.