Dataset Information

Walk Your Talk: Real-World Adherence to Guidelines on the Use of MRI in Multiple Sclerosis.

ABSTRACT: (1) Although guidelines about the use of MRI sequences for Multiple Sclerosis (MS) diagnosis and follow-up are available, variability in acquisition protocols is not uncommon in everyday clinical practice. The aim of this study was to evaluate the real-world application of MS imaging guidelines in different settings to clarify the level of adherence to these guidelines. (2) Via an on-line anonymous survey, neuroradiologists (NR) were asked about MRI protocols and parameters routinely acquired when MS patients are evaluated in their center, both at diagnosis and follow-up. Furthermore, data about report content and personal opinions about emerging neuroimaging markers were also retrieved. (3) A total of 46 participants were included, mostly working in a hospital or university hospital (80.4%) and with more than 10 years of experience (47.9%). We found a relatively good adherence to the suggested MRI protocols regarding the use of T2-weighted sequences, although almost 10% of the participants routinely acquired 2D sequences with a slice thickness superior to 3 mm. On the other hand, a wider degree of heterogeneity was found regarding gadolinium administration, almost routinely performed at follow-up examination (87.0% of cases) in contrast with the current guidelines, as well as a low use of a standardized reporting system (17.4% of cases). (4) Although the MS community is getting closer to a standardization of MRI protocols, there is still a relatively wide heterogeneity among NR, with particular reference to contrast administration, which must be overcome to guarantee an adequate quality of patients' care in MS.

SUBMITTER: Tortora M

PROVIDER: S-EPMC8394408 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Walk Your Talk: Real-World Adherence to Guidelines on the Use of MRI in Multiple Sclerosis.

Tortora Mario M Tranfa Mario M D'Elia Anna Chiara AC Pontillo Giuseppe G Petracca Maria M Bozzao Alessandro A Caranci Ferdinando F Cervo Amedeo A Cosottini Mirco M Falini Andrea A Longo Marcello M Manara Renzo R Muto Mario M Porcu Michele M Roccatagliata Luca L Todeschini Alessandra A Saba Luca L Brunetti Arturo A Cocozza Sirio S Elefante Andrea A

Diagnostics (Basel, Switzerland) 20210721 8

(1) Although guidelines about the use of MRI sequences for Multiple Sclerosis (MS) diagnosis and follow-up are available, variability in acquisition protocols is not uncommon in everyday clinical practice. The aim of this study was to evaluate the real-world application of MS imaging guidelines in different settings to clarify the level of adherence to these guidelines. (2) Via an on-line anonymous survey, neuroradiologists (NR) were asked about MRI protocols and parameters routinely acquired wh ...[more]

PMID: 34441245

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Project description:BackgroundHippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task.ObjectiveOur aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS.MethodsWe proposed a deep learning algorithm called "HS-Net" to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility.ResultsThe HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method.ConclusionsOur computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.

Project description:ObjectiveImmune checkpoint inhibitors(ICIs) have improved survival and are increasingly used for cancer. However, ICIs use may be limited by immune-related adverse events (irAEs), such as ICI-induced diabetes mellitus(ICI-DM). The objective of the present study was to characterize ICI-DM patients and real-world adherence to guidelines.Research design and methodsThe present study was a retrospective review of electronic records of ICI-DM patients at the First Affiliated Hospital of Nanjing Medical University between July 2018 and October 2022.Results34.8% (8/23)patients monitored blood glucose in every treatment cycle. The proportion of patients with severe diabetic ketoacidosis(DKA) was lower in the tight glycemic monitoring group than the non-tight glycemic monitoring group (16.7% vs. 55.6%, p = 0.049). 78.3%(18/23) patients with hyperglycemia visited a non-endocrinologist first, but 95.7% of patients were then referred to an endocrinologist. Twenty patients were tested for distinguishing the etiology of hyperglycemia and 20% patients with positive glutamic acid decarboxylase antibody(GADA), 55% with C-peptide <3.33pmol/L. High screening rates for other ICI-induced endocrinopathies were observed and half of the patients with ICI-DM developed other endocrine gland irAEs, with the most common being thyroiditis. Moreover, five patients developed non-endocrine serious adverse events(SAEs). Twelve (52.2%) patients were withdrawn from ICI due to ICI-DM. The time to progression of tumor in ICI-DM patients in the continue and interruption group was longer than in the withdrawal group (333.5 ± 82.5 days vs. 183.1 ± 62.4 days, p = 0.161). Only 17.4% of ICI-DM patients were completely managed according to guidelines. Thus, the present study proposed a screening, diagnosis, and management algorithm for ICI-DM in real-world practice.ConclusionThe present study reported the largest number of ICI-DM cases described in a single institute, providing insight into real-world ICI-DM management guideline adherence and highlighting the clinical challenges in ICI-DM management.

Dataset Information

Walk Your Talk: Real-World Adherence to Guidelines on the Use of MRI in Multiple Sclerosis.

Publications

Walk Your Talk: Real-World Adherence to Guidelines on the Use of MRI in Multiple Sclerosis.

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