Triple A syndrome: An unusual association with plurihormonal pituitary neuroendocrine tumour.

Triple A syndrome is a rare genetic condition that can manifest in alacrima, achalasia, adrenal insufficiency, and commonly neurological disorders. We report on a patient with Triple A syndrome who underwent extensive workup for hyperhidrosis, subsequently found to have a pituitary neuroendocrine tumour causing acromegaly. Histopathology revealed an unusual plurihormonal PitNET of dual cell lineage. Previous studies have described tissue-specific expression of the AAAS gene in the cerebellum, pituitary gland, adrenal gland among other structures. This may explain the rare, reported disease phenotypes associated with Triple A syndrome and suggest need for early brain imaging.

Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging.

Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.

PURPOSE: To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS: PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS: Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION: This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.

Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

BACKGROUND: Molecular characterization of glioma has implications for prognosis, treatment planning, and prediction of treatment response. Current histopathology is limited by intratumoral heterogeneity and variability in detection methods. Advances in computational techniques have led to interest in mining quantitative imaging features to noninvasively detect genetic mutations. OBJECTIVE: To evaluate the diagnostic accuracy of machine learning (ML) models in molecular subtyping gliomas on preoperative magnetic resonance imaging (MRI). METHODS: A systematic search was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify studies up to April 1, 2020. Methodological quality of studies was assessed using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS)-2. Diagnostic performance estimates were obtained using a bivariate model and heterogeneity was explored using metaregression. RESULTS: Forty-four original articles were included. The pooled sensitivity and specificity for predicting isocitrate dehydrogenase (IDH) mutation in training datasets were 0.88 (95% CI 0.83-0.91) and 0.86 (95% CI 0.79-0.91), respectively, and 0.83 to 0.85 in validation sets. Use of data augmentation and MRI sequence type were weakly associated with heterogeneity. Both O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation and 1p/19q codeletion could be predicted with a pooled sensitivity and specificity between 0.76 and 0.83 in training datasets. CONCLUSION: ML application to preoperative MRI demonstrated promising results for predicting IDH mutation, MGMT methylation, and 1p/19q codeletion in glioma. Optimized ML models could lead to a noninvasive, objective tool that captures molecular information important for clinical decision making. Future studies should use multicenter data, external validation and investigate clinical feasibility of ML models.

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. METHODS: We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model's accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. RESULTS: The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. CONCLUSION: The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients.

Spontaneous ventricular tachyarrhythmias in ß2-adrenoceptor transgenic mice in relation to cardiac interstitial fibrosis.

Myocardial fibrosis is regarded as a pivotal proarrhythmic substrate, but there have been no comprehensive studies showing a correlation between the severity of fibrosis and ventricular tachyarrhythmias (VTAs). Our purpose was to document this relationship in a transgenic (TG) strain of mice with fibrotic cardiomyopathy. TG mice with cardiac overexpression of ß2-adrenoceptors (ß2-AR mice) and non-TG (NTG) littermates were studied at 4-12 mo of age. VTA was quantified by ECG telemetry. The effect of pharmacological blockade of ß2-ARs on VTA was examined. Myocardial collagen content was determined by hydroxyproline assay. NTG and TG mice displayed circadian variation in heart rate, which was higher in TG mice than in NTG mice (P <0.05). Frequent spontaneous ventricular ectopic beats (VEBs) and ventricular tachycardia (VT) were prominent in TG mice but not present in NTG mice. The frequency of VEB and VT episodes in TG mice increased with age (P < 0.01). Ventricular collagen content was greater in TG mice than in NTG mice (P <0.001) and correlated with age (r = 0.71, P < 0.01). The number of VEBs or VT episodes correlated with age (r = 0.83 and r = 0.73) and the content of total or cross-linked collagen (r = 0.62∼0.66, all P <0.01). While having no effect in younger ß2-TG mice, ß2-AR blockade reduced the frequency of VTA in old ß2-TG mice with more severe fibrosis. In conclusion, ß2-TG mice exhibit interstitial fibrosis and spontaneous onset of VTA, becoming more severe with aging. The extent of cardiac fibrosis is a major determinant for both the frequency of VTA and proarrhythmic action of ß2-AR activation.