AIMC Topic: Brain Neoplasms
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis.
Scientific reports
Aug 11, 2025
Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance Imaging (MRI) is pivo...
Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.
BMC medical imaging
Aug 11, 2025
BACKGROUND: Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weigh...
Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.
Scientific reports
Aug 8, 2025
Accurate detection of brain tumors remains a significant challenge due to the diversity of tumor types along with human interventions during diagnostic process. This study proposes a novel ensemble deep learning system for accurate brain tumor classi...
Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma.
Scientific reports
Aug 7, 2025
This study investigated the effects of feature augmentation, which uses generated images with specific imaging features, on the performance of isocitrate dehydrogenase (IDH) mutation prediction models in gliomas. A total of 598 patients were included...
Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma.
Scientific reports
Aug 7, 2025
The mortality rates have been increasing for glioma in adolescents and young adults (AYAs, aged 15-39 years). However, current biomarkers for clinical assessment in AYAs glioma are limited, prompting the urgent need for identifying ideal prognostic s...
Artificial intelligence algorithms for differentiating pseudoprogression from true progression in high-grade gliomas: A systematic review and meta-analysis.
Neurosurgical review
Aug 6, 2025
Differentiating pseudoprogression (PsP) from true progression (TP) in high-grade glioma (HGG) patients is still challenging and critical for effective treatment management. This meta-analysis evaluates the diagnostic accuracy of artificial intelligen...
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 ...
Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 4, 2025
PURPOSE: Accurate preoperative grading of gliomas is critical for therapeutic planning and prognostic evaluation. We developed a noninvasive machine learning model leveraging whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) b...
Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.
BMC medical imaging
Aug 4, 2025
BACKGROUND: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a co...
A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.
BMC medical imaging
Jul 31, 2025
This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas...
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