Publications by authors named "Mannil M"

Article Synopsis
  • The study aimed to determine if clinical features can predict the success of meningioma surgery by differentiating between gross total resections (GTR) and subtotal resections (STR).
  • The researchers analyzed 23 clinical features in a group of 157 patients, comparing two methods: Simpson grading and postoperative operative tumor volume (POTV) for predicting surgical outcomes.
  • Their final decision tree model demonstrated strong predictive accuracy (mean AUC of 0.885), which can assist in surgical planning and determining the need for further treatment after surgery.
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encodes the mitochondrial coenzyme A (CoA) transporter localized at the inner mitochondrial membrane. SLC25A42 deficiency leads to a congenital disease with a heterogeneous clinical presentation, including myopathy, developmental delay, lactic acidosis, and encephalopathy. Twenty-one patients have been described so far.

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The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date.

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To evaluate whether incorporating CT perfusion imaging can significantly enhance diagnostic CT accuracy in stroke detection. Two 3rd-year residents (3rd of 5 years of residency) reviewed CT scans of 200 patients with suspected stroke, consisting of 104 patients with a proven stroke and a control group with 96 patients. They analyzed each patient in a blinded and randomized manner in two runs.

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Article Synopsis
  • Researchers wanted to see if using special technology to create 3D images of the lower back (lumbar spine) was better than regular 2D images for MRIs.!
  • They tested this on 53 patients, comparing 2D images with two different types of 3D images to see which had better quality in 8 different areas, like clarity and noise levels.!
  • The 3D images using the special technology were found to be as good or better than the 2D images, showing that we can get high-quality pictures without losing detail.!
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Background: It is difficult to distinguish between tumor progression (TP) and treatment-related abnormalities (TRA) in treated glioblastoma patients via conventional MRI, but this distinction is crucial for treatment decision making. Glioblastoma is known to exhibit an invasive growth pattern along white matter architecture and vasculature. This study quantified lesion development patterns in treated glioblastoma lesions and their relation to white matter microstructure to distinguish TP from TRA.

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Background And Purpose: Radiological features on magnetic resonance imaging (MRI) were attributed to oligodendroglioma, although the diagnostic accuracy in a real-world clinical setting remains partially elusive. This study investigated the accuracy and robustness of tumor heterogeneity and tumor border delineation on T2-weighted MRI to distinguish oligodendroglioma from astrocytoma.

Materials And Methods: Eight readers from three different specialties (radiology, neurology, neurosurgery) with varying levels of experience blindly rated 79 T2-weighted MR images of patients with either oligodendroglioma or astrocytoma.

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In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases.

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Introduction: Dynamic susceptibility contrast (DSC) perfusion weighted (PW)-MRI can aid in differentiating treatment related abnormalities (TRA) from tumor progression (TP) in post-treatment glioma patients. Common methods, like the 'hot spot', or visual approach suffer from oversimplification and subjectivity. Using perfusion of the complete lesion potentially offers an objective and accurate alternative.

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MRI diagnostics are important for adenomyosis, especially in cases with inconclusive ultrasound. This study assessed the potential of MRI-based radiomics as a novel tool for differentiating between uteri with and without adenomyosis. This retrospective proof-of-principle single-center study included nine patients with and six patients without adenomyosis.

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Objectives: Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase () mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the mutation status.

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Background: Delivering case-based collaborative learning (cCBL) at scale using technology that both presents the clinical problem authentically and seeks to foster quality group discussion is a challenge, especially argumentation which is critical for effective learning. The aim of this study was to investigate the presence of essential conditions to capitalize on a technology-enhanced cCBL scenario for teaching radiology and facilitating quality group discussion.

Methods: A questionnaire was administered to 114 fourth-year medical students who completed a technology-enhanced cCBL scenario for teaching neuroradiology.

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The mutated enzyme isocitrate dehydrogenase (IDH) 1 and 2 has been detected in various tumor entities such as gliomas and can convert α-ketoglutarate into the oncometabolite 2-hydroxyglutarate (2-HG). This neuro-oncologically significant metabolic product can be detected by MR spectroscopy and is therefore suitable for noninvasive glioma classification and therapy monitoring.This paper provides an up-to-date overview of the methodology and relevance of H-MR spectroscopy (MRS) in the oncological primary and follow-up diagnosis of gliomas.

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Article Synopsis
  • Distinguishing treatment-related abnormalities (TRA) from tumor progression (TP) in glioblastoma patients using conventional MRI is difficult due to similar imaging appearances.
  • The study evaluated the effectiveness of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values in differentiating TRA from TP in 76 post-treatment patients.
  • Results showed that although there were significant differences in mean ADC values between TP and TRA, there was considerable overlap, leading to moderate diagnostic accuracy (AUC of 0.71) and suggesting that ADC maps should not be used alone for diagnosis without considering their temporal changes.
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Purpose: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment.

Methods: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging.

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Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models.

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ATRX is an important molecular marker according to the 2021 WHO classification of adult-type diffuse glioma. We aim to predict the ATRX mutation status non-invasively using radiomics-based machine learning models on MRI and to determine which MRI sequence is best suited for this purpose. In this retrospective study, we used MRI images of patients with histologically confirmed glioma, including the sequences T1w without and with the administration of contrast agent, T2w, and the FLAIR.

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Article Synopsis
  • Superoxide dismutase-1 (SOD1) is an important antioxidant enzyme, and mutations in its gene can lead to amyotrophic lateral sclerosis (ALS) by causing toxic protein aggregation.
  • Researchers studied eight children with a specific mutation (p.C112Wfs*11) that resulted in SOD1 deficiency, finding that they experienced progressive motor neuron dysfunction and brain atrophy starting around 8 months of age.
  • Despite motor system deterioration, other organs showed normal integrity and resilience, indicating a unique vulnerability of the motor system to changes in SOD1 function.
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Synchronous or metachronous growth of multiple tumors (≥ 2) is found in up to 20% of meningioma patients. However, biological as well as histological features and prognosis are largely unexplored. Clinical and histological characteristics were retrospectively investigated in 95 patients harboring 226 multiple meningiomas (MMs) and compared with 135 cases of singular meningiomas (SM) using uni- and multivariate analyses.

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Petroleum is commonly used as a solvent, and primary intrathecal administration or secondary diffusion and subsequent clinical management has not been reported. We report the case of a male patient with intrathecal petroleum diffusion following accidental lumbar infiltration. After the onset of secondary myeloencephalopathy with coma and tetraparesis, continuous cranio-lumbar irrigation using an external ventricular and a lumbar drain was established.

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The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed.

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Background: The usefulness of 5-ALA-mediated fluorescence-guided resection (FGR) in meningiomas is controversial, and information on the molecular background of fluorescence is sparse. Methods: Specimens obtained during 44 FGRs of intracranial meningiomas were analyzed for the presence of tumor tissue and fluorescence. Protein/mRNA expression of key transmembrane transporters/enzymes involved in PpIX metabolism (ABCB6, ABCG2, FECH, CPOX) were investigated using immunohistochemistry/qPCR.

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Article Synopsis
  • - The study aims to predict the success of surgical resections of skull meningiomas using radiomics and machine learning by analyzing pre-treatment T1 post-contrast MR images from 138 patients.
  • - They employed semi-automatic image segmentation, extracted 107 radiomic features, and tested eight machine learning algorithms, achieving high accuracy (mean AUC of 0.901 for training data and 0.900 for test data).
  • - The model demonstrated strong predictive capabilities regarding the complete or subtotal resection of tumors based on factors like tumor location and shape, indicating effective use of machine learning for surgical planning.
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Objective: Our aim is to define the capabilities of radiomics in predicting pseudoprogression from pre-treatment MR images in patients diagnosed with high-grade gliomas using T1 non-contrast-enhanced and contrast-enhanced images.

Material & Methods: In this retrospective IRB-approved study, image segmentation of high-grade gliomas was semi-automatically performed using 3D Slicer. Non-contrast-enhanced T1-weighted images and contrast-enhanced T1-weighted images were used prior to surgical therapy or radio-chemotherapy.

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