Background/Objectives: The superscan pattern is a characteristic finding on bone scintigraphy, associated with a variety of metabolic bone diseases, malignancies, and other conditions. This pattern is characterized by a diffuse and intense uptake of radiotracer throughout the entire skeleton. Despite being a relatively rare finding, the superscan pattern can have significant clinical implications. Methods: This comprehensive review summarizes the available literature on the superscan pattern, focusing on its pathophysiology, clinical significance, and differential diagnoses. Relevant studies and case reports were analyzed to outline the diagnostic challenges associated with the interpretation of bone scintigraphy featuring the superscan pattern. Results: The literature highlights the clinical significance of the superscan pattern in various metabolic and oncologic conditions. Misinterpretation of this pattern can lead to diagnostic challenges, especially in distinguishing it from other pathologic conditions. Differential diagnosis remains crucial in the accurate interpretation and subsequent management of patients with this finding. Conclusions: This review provides a comprehensive overview of the superscan pattern on bone scintigraphy, aiming to assist clinicians in recognizing and managing this rare yet clinically important finding.

Background and Aims: Benign prostatic hyperplasia (BPH) is among the most common urologic conditions in elderly men, presenting with lower urinary tract symptoms. Prostatic artery embolization (PAE) is a recent interventional treatment for BPH.Methods: This prospective single‐center study reviewed the results of cases that underwent PAE for BPH from 2020 to 2022. Inclusion criteria were men with BPH refractory to pharmacologic treatment or those experiencing persistent adverse effects from medications, who were not candidates for surgical management. Assessments were made using ultrasonography for prostate volume and the International Prostate Symptom Score (IPSS) before and at 3 and 12 months after PAE. The clinical success rate of PAE was assessed according to the Cardiovascular and Interventional Radiological Society of Europe Standards of Practice. Recurrence of symptoms and complications after PAE were also recorded.Results: From a total of 46 included patients, clinical success was achieved in 93% at 3 months and 85% at 12 months. Complications occurred in eight (17.4%) cases, including five cases of erectile dysfunction and three cases of urinary tract infection. Eight (17.4%) cases had recurrent symptoms and required pharmacologic treatment, while seven (15.2%) needed surgery. The IPSS score dropped significantly at 3 months, with a mean difference of −16.2 (95% CI: −17.7 to −14.8) (p < 0.001), and at 12 months after PAE, with a mean difference of −13.5 (95% CI: −15.9 to −11.1) (p < 0.001). Prostate volume decreased significantly at 3 months, with a mean difference of −36.0 (95% CI: −43.0 to −29.0) (p < 0.001), and at 12 months, with a mean difference of −37.3 (95% CI: −43.7 to −31.0) (p < 0.001).Conclusion: The findings of this study indicate that PAE is an effective therapeutic approach for patients with BPH, demonstrated by the low rates of adverse events and the absence of major complications.

Objectives: Current study aimed to investigate radiomics features derived from two-center diffusion-MRI to differentiate benign and hepatocellular carcinoma (HCC) liver nodules. Methods: A total of 328 patients with 517 LI-RADS 2-5 nodules were included. MR images were retrospectively collected from 3T and 1.5T MRI vendors. Lesions were categorized into 242 benign and 275 HCC based on follow-up imaging for LR-2,3 and pathology results for LR4,5 nodules, and randomly divided into training (80%) and test (20%) sets. Preprocessing included resampling and normalization. Radiomics features were extracted from lesion volume-of-interest (VOI) on diffusion Images. Scanner variability was corrected using ComBat harmonization method followed by High-correlation filter, PCA filter, and LASSO to select important features. Best classifier model was selected by 10-fold cross-validation, and accuracy was assessed on the test dataset. Results: 1,434 features were extracted, and subsequent classifiers were constructed based on the 16 most important selected features. Notably, support-vector machine (SVM) demonstrated better performance in the test dataset in distinguishing between benign and HCC nodules, achieving an accuracy of 0.92, sensitivity of 0.94, and specificity of 0.86. Conclusions: Utilizing diffusion-MRI radiomics, our study highlights the performance of SVM, trained on lesions’ diffusivity characteristics, in distinguishing benign and HCC nodules, ensuring clinical potential. It is suggested that further evaluations be conducted on multi-center datasets to address harmonization challenges. Advances in knowledge: Integration of diffusion radiomics, for monitoring water restriction patterns as tumor histopathological index, with machine learning models demonstrates potential for achieving a reliable noninvasive method to improve the current diagnosis criteria.

We present a case of an otherwise healthy mid 30s male with right sided vision loss. CT and MR imaging demonstrated a giant serpentine aneurysm of the right anterior cerebral artery A1-A2 segments. The patient was successfully treated with craniotomy for intracranial A3-A3 bypass followed by endovascular coil embolization of the inflow vessel. We highlight the imaging findings, reviewing both the pertinent findings and potential diagnostic utility of arterial spin labeling, an emerging noninvasive, MRI technique to assess brain perfusion.

Alzheimer’s disease and related neurodegenerative disorders exhibit marked clinicobiological heterogeneity, posing challenges for accurate diagnosis, prognosis and therapeutic development. Using data from multiple cohorts of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we conducted comprehensive cross-sectional and longitudinal concordance analyses of dementia severity, CSF and PET amyloid and tau. We also examined the stability of biomarker positivity and cognitive status (stable, progressive, reversive and fluctuating) and their inter-relationships. The ability to separate methodological from biological sources of variability is relevant to understanding the robustness of the Amyloid/Tau/Neurodegeneration classification framework, which is particularly important in the era of disease-modifying therapies. Our data revealed both temporal and spatial discrepancies among variables used for classifying the level of cognitive impairment and between the methods used for classifying positive amyloid or tau status (CSF/PET). Methodological discrepancies in region-specific definitions, positivity thresholds and segmentation constraints contributed to PET inconsistencies. Additionally, biomarker status for CSF and PET positivity fluctuated over time, and there was no evidence of temporal precedence for either CSF or PET. There was pronounced spatiotemporal heterogeneity in tau-PET patterns, with implications for the clinical trajectory. Initial tau deposition in the entorhinal cortex, compared to non-limbic meta-temporal regions, was associated with more rapid cognitive decline and greater amyloid burden. Notably, a subset of mild cognitive impairment cases demonstrated diagnostic reversion to cognitively normal status and showed lower amyloid and tau burden and a higher prevalence of affective symptoms, possibly representing individuals without or at an early stage of Alzheimer’s pathology. Results underscore the complexity and variability of Alzheimer’s disease phenotypes and progression and provide a sanguine perspective on current diagnostic frameworks. Recognizing variability sources in biomarkers is particularly important when diagnosis relies on a single biomarker or cross-sectional data. More importantly, given the complex interplay of factors contributing to dementia and cognitive decline, deep learning and artificial intelligence-based algorithms are needed for precise and individualized diagnoses. Harmonized and standardized datasets with clearly defined variables, without excluding atypical cases, are needed for the effectiveness of such machine learning models. Therefore, investigating Alzheimer’s disease biomarker heterogeneity is critical towards maximizing the potential of artificial intelligence in integrating multi-dimensional clinical and biomarker data beyond human-centred interpretation. In this comprehensive work, we provide insights into the observed heterogeneities and their potential contributing factors. We aim to use this insight and continue this effort by harmonizing the conflicting data, making them suitable for diagnostic algorithms and sharing the harmonized dataset in the near future.