AIMC Topic: Meningioma
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Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images.
PloS one
Aug 11, 2025
BACKGROUND: Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potentia...
Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches.
Scientific reports
Aug 6, 2025
Brain tumors are a significant challenge to human health as they impair the proper functioning of the brain and the general quality of life, thus requiring clinical intervention through early and accurate diagnosis. Although current state-of-the-art ...
Large language models for extraction of OPS-codes from operative reports in meningioma surgery.
Acta neurochirurgica
Jul 31, 2025
BACKGROUND: In the German medical billing system, surgical departments encode their procedures in OPS-codes. These OPS-codes have major impact on DRG grouping and thus mainly determine each case´s revenue. In our study, we investigate the ability of ...
Multiparameter MRI-based automatic segmentation and diagnostic models for the differentiation of intracranial solitary fibrous tumors and meningiomas.
Annals of medicine
Jul 8, 2025
BACKGROUND: Intracranial solitary fibrous tumors (SFTs) and meningiomas are meningeal tumors with different malignancy levels and prognoses. Their similar imaging features make preoperative differentiation difficult, resulting in high misdiagnosis ra...
Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.
BMC medical imaging
Jul 1, 2025
BACKGROUND: Meningioma consistency critically impacts surgical planning, as soft tumors are easier to resect than hard tumors. Current assessments of tumor consistency using MRI are subjective and lack quantitative accuracy. Integrating deep learning...
Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.
BMC medical imaging
May 3, 2025
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...
Adult
Retrospective Studies
Adolescent
Radiomics
Female
Meningeal Neoplasms
Adenoma
Male
Support Vector Machine
Aged
Sella Turcica
Diagnosis, Differential
Middle Aged
Magnetic Resonance Imaging
Young Adult
Craniopharyngioma
Meningioma
Pituitary Neoplasms
Humans
Central Nervous System Cysts
Machine Learning
Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.
Scientific reports
Apr 29, 2025
Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types-pituitary, meningioma, glioma, and normal-this ...
Deep learning for automated segmentation of brain edema in meningioma after radiosurgery.
BMC medical imaging
Apr 22, 2025
BACKGROUND: Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding brain tissue can lead to perifocal edema within a few months after the procedure. Volumetric assessmen...
MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 9, 2025
Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotat...
Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.
F1000Research
Mar 25, 2025
INTRODUCTION: Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies ...
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