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Background/Objectives: To define and evaluate a radiation dose optimization process for chest computed tomography (CT) imaging. Methods: Data from unenhanced and enhanced chest CT acquisitions performed between June 2018 and January 2020 in adult patients were included in the study. Images were acquired on a Siemens SOMATOM ® Definition Edge CT. Dose values, including Dose.Length Product (DLP) and Volume CT Dose Index (CTDI vol ), were collected. Low doses (LDs, 25th percentiles), achievable doses (ADs, 50th percentiles), and diagnostic reference levels (DRLs, 75th percentiles) were calculated before and after parameter modifications. A process was defined and applied to patient data. For unenhanced chest CT, data were differentiated according to three groups: high dose (HD), optimized dose (OD), and ultra-low dose (ULD). Dosimetric changes between protocols were expressed as mean CTDI vol % (CI95%). A Mann and Whitney statistical test was used. The diagnostic quality score (DQS) of a subset of 70 randomly selected CT examinations was evaluated by one radiologist. The DQS was scored according to a three-point Likert scale: (1) poor (definite diagnosis impossible), (2) fair (evaluation of major findings possible), and (3) excellent (exact diagnosis possible). Results : Data were collected from 1929 patients. For unenhanced chest CT protocols, only one process loop was run. A dose comparison between the chest CT protocol before the use of the process and the three groups showed a decrease of -38.3% (9.7%) and -93.4% (24.2%) for OD and ULD, respectively, and an increase of +29.4% (4.7%) for HD. For the enhanced chest CT protocol, two optimization loops were performed, and they resulted in a mean dose reduction of -50.0% (2.6%) compared to the pre-optimization protocol. For all protocols, the DQS was greater than or equal to 2. Conclusions : We proposed a radiation dose optimization process for chest CT that could significantly reduce the dose without compromising diagnosis.

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%., (© 2023. Springer Nature Limited.)

Background: Neurodegeneration in the substantia nigra pars compacta (SNc) in parkinsonian syndromes may affect the nigral territories differently., Objective: The objective of this study was to investigate the regional selectivity of neurodegenerative changes in the SNc in patients with Parkinson's disease (PD) and atypical parkinsonism using neuromelanin-sensitive magnetic resonance imaging (MRI)., Methods: A total of 22 healthy controls (HC), 38 patients with PD, 22 patients with progressive supranuclear palsy (PSP), 20 patients with multiple system atrophy (MSA, 13 with the parkinsonian variant, 7 with the cerebellar variant), 7 patients with dementia with Lewy body (DLB), and 4 patients with corticobasal syndrome were analyzed. volume and signal-to-noise ratio (SNR) values of the SNc were derived from neuromelanin-sensitive MRI in the whole SNc. Analysis of signal changes was performed in the sensorimotor, associative, and limbic territories of the SNc., Results: SNc volume and corrected volume were significantly reduced in PD, PSP, and MSA versus HC. Patients with PSP had lower volume, corrected volume, SNR, and contrast-to-noise ratio than HC and patients with PD and MSA. Patients with PSP had greater SNR reduction in the associative region than HC and patients with PD and MSA. Patients with PD had reduced SNR in the sensorimotor territory, unlike patients with PSP. Patients with MSA did not differ from patients with PD., Conclusions: This study provides the first MRI comparison of the topography of neuromelanin changes in parkinsonism. The spatial pattern of changes differed between PSP and synucleinopathies. These nigral topographical differences are consistent with the topography of the extranigral involvement in parkinsonian syndromes. © 2022 International Parkinson and Movement Disorder Society., (© 2022 International Parkinson and Movement Disorder Society.)

Purpose: The aim of this study is to identify quantitative MR biomarkers in head and neck paragangliomas., Methods: The study was approved by an institutional review board. A retrospective review of patients with head and neck paragangliomas (HNPGL) evaluated by time-resolved MRA sequences between 2009 and 2019 was performed. A control group investigated during the same period was analyzed, including nerve sheath tumors and metastatic lymph nodes from squamous cell carcinomas or undifferentiated nasopharyngeal cancer (UCNT). A gold standard was obtained for all cases. Semi-quantitative parameters of enhancement were extracted from time-intensity curves on time-resolved MRA sequences and diffusion weighted imaging/DWI was assessed for each lesion., Results: Sixty head and neck paragangliomas (HNPGLs) were included from 50 patients. The control group consisted of 30 parapharyngeal space lesions (27 patients), which included nerve sheath tumors (n = 12) and metastatic lymph nodes (n = 18) from squamous cell carcinomas or UCNT. PGLs showed a shorter time-to-peak value compared to other groups, measured at 25.0 +/- 29 sec. The wash-in and wash-out ratios were also significantly higher for PGLs, respectively measured at 5.34 ± 2.99 (p < 0,001) and 1.24 ± 0.80 (p < 0.001). On DWI sequences, the mean ADC value for PGLs (1.17 ± 0.19 10^-3 mm2/s) was significantly different than the other tumor groups (p < 0.001). HNPGLs were clearly distinguishable from other tumors on classification with regression tree based on TTP and ADC values. These distinct group features were also consistent on principal component analysis., Conclusion: Our study identifies a multiparametric signature for disease subtyping, providing a strong impetus for switching from qualitative to quantitative analysis of deep soft-tissue tumors of the neck., (Copyright © 2021. Published by Elsevier B.V.)

We report the case of a 72-year-old woman presenting with a progressive left peripheral facial paralysis and a facial canal mass extending through the stylomastoid foramen along the posterior edge of the parotid gland. On MRI, the early and intense enhancement was highly suggestive of paraganglioma but could not rule out a nonossifying hemangioma. Laboratory analysis showed normal plasma metanephrines. On F-FDOPA PET/CT, the mass exhibited a typical paraganglioma feature with a marked tumor uptake. Our case demonstrates that F-FDOPA plays a vital role in this rare entity and can avoid any further confirmatory invasive procedure.

Multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD) are the most common atypical parkinsonism (AP) syndromes. They are clinically characterized by varying combinations of levodopa-poorly responsive parkinsonism, motor, cerebellar, and other signs. They are associated with a wide spectrum of non-motor symptoms, including prominent cognitive impairment such as global cognitive deficits, memory, executive, attentional, visuospatial, language, and non-verbal reasoning dysfunctions. Within the APs, their cognitive functioning is distributed along a continuum from MSA with the least impaired cognitive profile (similar to Parkinson's disease) to PSP and CBD with the greatest decline in global cognitive and executive domains. Although their pathological hallmarks are different—MSA α-synucleinopathy, CBD, and PSP 4-repeat tauopathies—cognitive dysfunctions in APs show both overlaps and dissimilarities. They are often preceding and anticipate motor dysfunctions, finally contributing to reduced quality of life of patients and caregivers. The present paper will review the current evidence of the prevalence and type of cognitive impairment in these AP syndromes, their neuroimaging, pathogenic backgrounds, and current management options based on extensive literature research. Cognitive dysfunctions in APs are due to disruption of prefronto-subcortical and striato-thalamo-cortical circuitries and multiple essential brain networks. This supports the concept that they are brain network disorders due to complex pathogenic mechanisms related to the basic proteinopathies that are still poorly understood. Therefore, the pathophysiology and pathogenesis of cognitive impairment in APs deserve further elucidation as a basis for early diagnosis and adequate treatment of these debilitating comorbidities. [ABSTRACT FROM AUTHOR]

Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)—a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation. [ABSTRACT FROM AUTHOR]

Assessing imaging biomarker in the prodromal and early phases of Parkinson's disease (PD) is of great importance to ensure an early and safe diagnosis. In the last decades, imaging modalities advanced and are now able to assess many different aspects of neurodegeneration in PD. MRI sequences can measure iron content or neuromelanin. Apart from SPECT imaging with Ioflupane, more specific PET tracers to assess degeneration of the dopaminergic system are available. Furthermore, metabolic PET patterns can be used to anticipate a phenoconversion from prodromal PD to manifest PD. In this regard, it is worth mentioning that PET imaging of inflammation will gain significance. Molecular imaging of neurotransmitters like serotonin, noradrenaline and acetylcholine shed more light on non-motor symptoms. Outside of the brain, molecular imaging of the heart and gut is used to measure PD-related degeneration of the autonomous nervous system. Moreover, optical coherence tomography can noninvasively detect degeneration of retinal fibers as a potential biomarker in PD. In this review, we describe these state-of-the-art imaging modalities in early and prodromal PD and point out in how far these techniques can and will be used in the future to pave the way towards a biomarker-based staging of PD. [ABSTRACT FROM AUTHOR]

Multiple system atrophy (MSA), corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP) are the most common atypical parkinsonisms. These adult-onset and lethal neurodegenerative disorders of unknown etiology are clinically characterized by varying combinations of autonomic, levodopa-poorly responsive parkinsonsm, motor, non-motor, cerebellar syndromes, behavioral, cognitive and other neuropsychiatric disorders. Although their pathological hallmarks are different-MSA α-synucleinopathy, CBD and PSP 4-repeat (4R) tauopathies-their neuropsychiatric disturbances include anxiety, depression, agitations, attention-executive dysfunctions, less often compulsive and REM sleep behavior disorders (RBD), which may contribute to disease progression and reduced quality of life (QoL) of patients and caregivers. The present paper reviews the prevalence and type of neuropsychiatric profile in these atypical parkinsonian syndromes, their neuroimaging, and pathogenic backgrounds based on extensive literature research. MSA patients show anxiety, apathy (depression), initial RBD, attentional and executive dysfunction; PSP patients present with apathy, depression, disinhibition, and to a lesser extent, anxiety and agitation; CBD patients are featured by executive and visuospatial dysfunctions, irritability, alien limb phenomena, sleep and language disorders. Neuropsychiatric disorders in these syndromes are often similar, due to disruption of prefronto-subcortical (limbic) and striato-thalamo-cortical circuitries or default mode and attention network disorder. This supports the concept that they are brain network disorders due to complex pathogenic mechanisms related to the basic proteinopathies that are still poorly understood. Psychotic symptoms, hallucinations and delusions are rare. Neuropsychiatric changes in these disorders are often premature and anticipate motor dysfunctions; their assessment and further elucidation of their pathogenesis are warranted as a basis for early diagnosis and adequate treatment of these debilitating comorbidities., Competing Interests: Declarations. Conflict of interests: The author declares that he has no conflict of interest., (© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Parkinson's disease (PD) is an age‐related progressive neurodegenerative disorder characterized by both motor and non‐motor symptoms resulting from the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and noradrenergic neurons in the locus coeruleus (LC). The current diagnosis of PD primarily relies on motor symptoms, often leading to diagnoses in advanced stages, where a significant portion of SNpc dopamine neurons has already succumbed. Therefore, the identification of imaging biomarkers for early‐stage PD diagnosis and disease progression monitoring is imperative. Recent studies propose that neuromelanin‐sensitive magnetic resonance imaging (NM‐MRI) holds promise as an imaging biomarker. In this review, we summarize the latest findings concerning NM‐MRI characteristics at various stages in patients with PD and those with atypical parkinsonism. In conclusion, alterations in neuromelanin within the LC are associated with non‐motor symptoms and prove to be a reliable imaging biomarker in the prodromal phase of PD. Furthermore, NM‐MRI demonstrates efficacy in differentiating progressive supranuclear palsy (PSP) from PD and multiple system atrophy with predominant parkinsonism. The spatial patterns of changes in the SNpc can be indicative of PD progression and aid in distinguishing between PSP and synucleinopathies. We recommend that patients with PD and individuals at risk for PD undergo regular NM‐MRI examinations. This technology holds the potential for widespread use in PD diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate whether multiparametric magnetic resonance imaging (MRI) including dynamic contrast-enhanced (DCE) and diffusion weighted (DW) MRI can differentiate pleomorphic adenoma (PA) from schwannoma in the parapharyngeal space., Methods: Forty-six patients with pathologically proven PAs and 47 schwannomas in the parapharyngeal space were enrolled. All patients underwent conventional MRI, and DW-MRI and DCE-MRI were performed in 30 and 33 patients, respectively. Fisher's exact, Mann-Whitney-U tests and Independent samples t-test were used to compare variables between PAs and schwannomas. Multivariate logistic regression analysis was used to examine the diagnostic performance of MRI parameters., Results: The PAs usually show lobulation sign, posterior displacement of ICA and attached to the parotid gland deep leaf, while bird beak configuration, anterior displacement of ICA and involvement of foramen jugular were more commonly seen in the schwannomas(all p < 0.001). The washout rate of PAs was found to be higher than that of schwannomas (p = 0.035), whereas no significance was found in the other DCE-MRI parameters and in ADCs(p > 0.05). Using a combination of conventional MRI features including lobulation sign, bird beak configuration, direction of internal carotid artery(ICA) displacement and attached to the parotid gland in multivariate logistic regression analysis, sensitivity, specificity, and accuracy in differential diagnosis of PAs and schwannomas were 97.8%, 91.5% and 94.6%, respectively., Conclusion: Conventional MRI can effectively differentiate PAs from schwannomas in the parapharyngeal space with a high diagnostic accuracy. The DCE-MRI and DWI have limited added diagnostic value to conventional MRI in the differential diagnosis., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Simple Summary: Pheochromocytomas and paragangliomas are rare neural-crest-derived tumors with variable prognosis. In an era of tailored treatment strategies, medical imaging plays a crucial role in the surgeries, image-guided procedures, chemotherapies, immunotherapies, and radionuclide therapies involved in the diagnosis and treatment of these tumors. Medical imaging helps to confirm the diagnosis, guide surgical resection, assess metastatic staging, and select patients for specific therapies. In this step-by-step review, we perform a comprehensive analysis of recent imaging modalities developed for pheochromocytomas and paragangliomas by covering their content and terminology as well as discussing the future implications of artificial intelligence. In this comprehensive review, we aimed to discuss the current state-of-the-art medical imaging for pheochromocytomas and paragangliomas (PPGLs) diagnosis and treatment. Despite major medical improvements, PPGLs, as with other neuroendocrine tumors (NETs), leave clinicians facing several challenges; their inherent particularities and their diagnosis and treatment pose several challenges for clinicians due to their inherent complexity, and they require management by multidisciplinary teams. The conventional concepts of medical imaging are currently undergoing a paradigm shift, thanks to developments in radiomic and metabolic imaging. However, despite active research, clinical relevance of these new parameters remains unclear, and further multicentric studies are needed in order to validate and increase widespread use and integration in clinical routine. Use of AI in PPGLs may detect changes in tumor phenotype that precede classical medical imaging biomarkers, such as shape, texture, and size. Since PPGLs are rare, slow-growing, and heterogeneous, multicentric collaboration will be necessary to have enough data in order to develop new PPGL biomarkers. In this nonsystematic review, our aim is to present an exhaustive pedagogical tool based on real-world cases, dedicated to physicians dealing with PPGLs, augmented by perspectives of artificial intelligence and big data. [ABSTRACT FROM AUTHOR]

Accurate and timely diagnosis of atypical parkinsonian syndromes (APS) remains a challenge. Especially early in the disease course, the clinical manifestations of the APS overlap with each other and with those of idiopathic Parkinson's disease (PD). Recent advances in magnetic resonance imaging (MRI) technology have introduced promising imaging modalities to aid in the diagnosis of APS. Some of these MRI modalities are also included in the updated diagnostic criteria of APS. Importantly, MRI is safe for repeated use and more affordable and accessible compared to nuclear imaging. These advantages make MRI tools more appealing for diagnostic purposes. As the MRI field continues to advance, the diagnostic use of these techniques in APS, alone or in combination, are expected to become commonplace in clinical practice. [ABSTRACT FROM AUTHOR]

Rasagiline mesylate (RSM) is a hydrophilic drug with poor oral bioavailability (36%) because of hepatic first-pass metabolism. The present study focuses on delivering RSM directly to the brain through its inclusion within transferosomal in situ gel administered through the intranasal (IN) route. Transferosomes were formed by the thin-film hydration method with the aid of Design-Expert® software by varying the edge activator (EA) type in the absence or presence of cholesterol. By desirability calculations, the optimum formulation was composed of phosphatidylcholine and sodium deoxycholate as an EA (5:1% w/w) with no cholesterol. The optimum formulation was 198.63 ± 34.98 nm in size and displayed an entrapment efficiency of 95.73 ± 0.09%. Transmission electron microscopy revealed discrete and spherical vesicles. Optimized transferosomes were further incorporated into an in situ gel composed of 0.5% pectin, 15% Pluronic® F-127, and 5% Pluronic® F-68 and tested for the in vivo performance. The systemic as well as brain kinetics were assessed in rats by comparing the IN-administered in situ gel to the IV aqueous solution. The optimum in situ gel showed safety and biocompatibility on rats' nasal mucosa with enhanced brain bioavailability (131.17%). Drug targeting efficiency and direct transport percentage indices (304.53% and 67.16%, respectively) supported successful brain targeting offering direct nose-to-brain drug delivery. [ABSTRACT FROM AUTHOR]

Objectives: To elucidate the differences between pleomorphic adenomas and schwannomas occurring in the parapharyngeal space by histogram analyses of apparent diffusion coefficient (ADC) values measured with diffusion‐weighted MRI. Methods: This retrospective study included 29 patients with pleomorphic adenoma and 22 patients with schwannoma arising in the parapharyngeal space or extending into the parapharyngeal space from the parotid region. Using pre‐operative MR images, ADC values of tumor lesions showing the maximum diameter were measured. The regions of interest for ADC measurement were placed by contouring the tumor margin, and the histogram metrics of ADC values were compared between pleomorphic adenomas and schwannomas regarding the mean, skewness, and kurtosis by Wilcoxon's rank sum test. Subsequent to the primary analysis which included all lesions, we performed two subgroup analyses regarding b‐values and magnetic field strength used for MRI. Results: The mean ADC values did not show significant differences between pleomorphic adenomas and schwannomas for the primary and subgroup analyses. Schwannomas showed higher skewness (p = 0.0001) and lower kurtosis (p = 0.003) of ADC histograms compared with pleomorphic adenomas in the primary analysis. Skewness was significantly higher in schwannomas in all the subgroup analyses. Kurtosis was consistently lower in schwannomas but did not reach statistical significance in one subgroup analysis. Conclusions: Skewness and kurtosis showed significant differences between pleomorphic adenomas and schwannomas occupying the parapharyngeal space, but the mean ADC values did not. Our results suggest that the skewness and kurtosis of ADC histograms may be useful in differentiating these two parapharyngeal tumors. [ABSTRACT FROM AUTHOR]