Glioma Diagnosis and Treatment

Neueste Paper

• Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification

  Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl, Sandeep Kaushik, Marion Smits, Stefan Klein · 1. Oktober 2026

  Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a multi-task Deep Learning framework for MRI-based glioma diagnosis that performs tumor segmentation and predicts IDH mutation status, 1p/19q co-deletion sta…

• Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas

  Md Zahangir Alom, Quynh T. Tran, Breuer Alexandar, Brent A. Orr · 29. September 2026

  DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational models typically analyze methylation and copy number variation (CNV) data separately, failing to capture the complementary information their integration …

• CrossScale-GLIO: Topology-Preserving Vision-Language Alignment of MRI and Whole-Slide Histopathology for Diffuse Glioma

  Yantong Liu, Zheyu Zhang, Runpeng Liu, Mu Xitang, Seong-Yoon Shin, Hyun-Ae Lee · 25. September 2026

  Magnetic resonance imaging and histopathology observe the same glioma at radically different scales. We present CrossScale-GLIO, a visual multimodal framework that represents MRI as a tumor-habitat graph and histology as a cell-niche graph, then aligns them with a structure-aware optimal transport o…

• An Accurate and Interpretable Hyper Graph Neural Network for GBM Survival Prediction

  Mushahid Intesum · 23. September 2026

  Survival prediction for glioblastoma multiforme (GBM) demands models that are both accurate and interpretable, yet existing approaches treat these objectives as com- peting, where performant models sacrifice transparency, while interpretable models accept degraded predictive power. We argue that thi…

• Brain Metastases Segmentation for BraTS 2026 Task 1: A Multi-Architecture Comparison

  Mahdi Islam, Musarrat Tabassum · 22. September 2026

  Brain metastases are the most common intracranial malignancy, occurring in roughly 30% of patients with primary solid tumors and carrying a median survival near 5.9 months. Automated segmentation is critical for treatment planning and volumetric monitoring, but metastases are frequently small, numer…

• De-GAN - Dynamic Parameter Tuned GAN for 3D Medical Image Segmentation: A Step Towards Generalisation

  Zoha Usama, Azadeh Alavi · 16. September 2026

  Brain tumor segmentation remains difficult because enhancing tumor (ET) has low contrast and overlaps surrounding tissue, while scanner and site variation causes domain shift. We propose DE-GAN, a contrast-enhancing conditional GAN that combines input-adaptive dynamic convolutions, style-aware featu…

• A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data

  Yinong Wang (Joyce), Jianwen Chen (Joyce), Zhou Chen (Joyce), Shuwen Kuang (Joyce), Haoning Jiang (Joyce), Yanzhao Shi (Joyce), Huichun Yuan (Joyce), Yan-ran (Joyce), Wang, Bing Wang, Lei Wu, Bin Tang, Li Meng, Baihua Luo, Bin Zhou, Wei Ding, Weiming Zhong, Wei Hou, Yuanbing Chen, Zhiping Wan, Wei Wang, Zhenkun Xiao, Wenwu Wan, Allen He, Yuyin Zhou, Longbo Zhang, Feifei Wang, Zhixiong Liu, Michael Iv, Xuan Gong, Liangqiong Qu · 16. September 2026

  Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapping imaging features across tumor types, inter-observer variability, and the extensive training required for expertise. We aimed to develop an MRI-base…

• Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma

  Yeonjae Jung, Minwoo Shin · 14. September 2026

  Post-treatment MRI in patients with glioma provides serial observations for updating patient-specific tumor-state proxy estimates, but variable appearances and trajectories complicate forecasting. We formulate forecasting as an observation-aware digital-twin update in which an intermediate observati…

• Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026

  Haobin Liu, Xin Wang · 11. September 2026

  Brain metastases exhibit high inter-lesion variability in size, enhancement pattern, and post-treatment appearance, making volumetric segmentation of both pre- and post-treatment cases the central challenge of the BraTS 2026 Task 1 (Brain Metastases). We build a pragmatic pipeline on a 5-fold nnU-Ne…

• CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

  Alexandre G. Leclercq, Noémie N. Moreau, Hugo Audebert, Andros Nassar, Thomas Cochin, Thomas Leleu, Loïc Le Henaff, Alexis Desmonts, Yoann Poirier, Aurélie Dubru, Laura Guillemette, Pascal Lecoeur, Kévin Lemasson, Cyril Jaudet, Sébastien Bougleux, Romain Hérault, Carole Brunaud, Samuel Valable, Dinu Stefan, Charlotte Raboutet, Alain Batalla, Joëlle Lacroix, Roman Rouzier, Aurélien Corroyer-Dulmont · 19. August 2026

  Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging datasets with comprehensive clinical and treatment data are essential to support the development of reproducible computational methods for treatment…

• Heterogeneity-Aware Deep Learning for Tumour Classification from Multiparametric MRI

  Yue Xia, Euijoon Ahn, Tian Xia, Yuan Yuan, Michael Fulham, Jinman Kim · 19. August 2026

  Intra-tumoural heterogeneity (ITH) reflects spatial variation in tumour biology and is an important determinant of tumour behaviour, prognosis, and treatment response. Radiomics and deep learning have shown promise for tumour classification from multiparametric MRI (mp-MRI), but radiomics relies on …

• How Good are Foundation Models in Longitudinal MRI Disease Progression Reasoning?

  Wafa Al Ghallabi, Ritesh Thawkar, Sara Ghaboura, Omkar Thawakar, Numan Saeed, Dana Al Nuaimi, Ajnas Alkatheeri, Salman Khan, Fahad Shahbaz Khan · 14. August 2026

  Magnetic Resonance Imaging (MRI) interpretation is fundamental to clinical decision-making, requiring radiologists to integrate multi-view anatomical planes across sequential timepoints while precisely localizing interval changes. However, existing vision-language benchmarks remain confined to singl…

• Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty

  Riya Deepak Shet, Le Zhang · 14. August 2026

  Deep networks segment brain tumours accurately in-distribution, but can fail silently when the input differs from their training data. That risk is central to clinical deployment and is the premise of the BraTS-GoAT generalizability task. We ask not only how well a model segments, but whether its un…

• Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model

  Zach Eidex, Yu-nong Lin, Mojtaba Safari, Sean Pitroda, Ralph Weichselbaum, Zhen Tian, Xiaofeng Yang · 7. August 2026

  Background: Accurate glioma subregion delineation is important for radiotherapy planning and longitudinal monitoring, but manual contour correction is time-consuming. Models such as nnU-Net may generalize imperfectly and lack clinician-directed text correction. Purpose: We investigated adapting a th…

• Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation

  Sina Wendrich, Lukas Förner, Zoe Reinke, Kartikay Tehlan, Ansgar Berlis, Michael Frühwald, Matthias Wagner, Thomas Wendler · 4. August 2026

  Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying spatial resolutions across time points. We propose a patient-specific conditional implicit neural repres…

• A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging

  Diego J. Torrej\'on, Luna Y. Hern\'andez, Javier S\'anchez · 3. August 2026

  Automatic brain tumor segmentation from magnetic resonance imaging (MRI) has become a fundamental task in computer-assisted diagnosis, treatment planning, and disease monitoring. Although numerous deep learning architectures have recently been proposed, objective comparisons remain challenging becau…

• Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling

  Mahmuda Akter Sristy, Md Al-Mahfuz Chowdhury, Momota Ahsana Meem, Sajid Ahamed, Kazi Irfan Subhan · 30. Juli 2026

  Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft brain tissue, yet common diagnostic methods rely on static imaging that cannot capture tumor growth, tissue displacement, or changes in stiffness over time. Deep learning models for this task typical…

• Shape-Based Inductive Bias for Glioma Grading from Tumor Contours

  Puneet Velidi, Michelle F. Miranda, Farouk Nathoo, Ashery Mbilinyi, C\'edric Beaulac · 30. Juli 2026

  Glioma grading from tumor contours is often treated as a pixel problem even when the signal of interest is shape. We align closed contours with a functional shape-alignment framework, separate global deformation from residual Fourier shape, and organize these quantities as frequency-ordered tokens. …

• Segmentation Robustness and Predictive Utility in Glioblastoma Radiomics: Evidence for a Trade-off in Survival Modelling

  Mariya Miteva, Maria Nisheva-Pavlova · 28. Juli 2026

  Radiomic biomarkers derived from magnetic resonance imaging (MRI) have been widely investigated as non-invasive tools for tumor characterization and prognostic modeling in glioblastoma (GBM). However, their clinical translation remains limited, in part due to sensitivity to tumor segmentation variab…

• GLI-AL: A Multi-Modal Glioma MRI Label Resource with Unified Anatomy-Lesion Labels

  Xingyu Xiang, Shuang Hao, Fan Wang, Jianhua Ma, Chunfeng Lian · 27. Juli 2026

  Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definition focuses on tumor subregions and does not systematically represent coexisting white matter hyperintensities (WMH). In joint segmentation settings, such unlabeled abnormalities intr…

• Multi-LLM Collaborative MRI Report Generation for Visual Instruction Tuning in Brain Oncology

  Sinyoung Ra, Jonghun Kim, Hyunjin Park · 17. Juli 2026

  Recent advances in large language models (LLMs) and their extension to vision-language models (VLMs) have made it easier to combine text and images for tasks such as report generation. Existing VLMs in medicine typically focus on 2D images (chest X-rays), and their extension to 3D imaging has been d…

• A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation

  Paulo R. Ferreira Jr., Lucas Coutinho Freitas, La\'is dos Santos Gon\c{c}alves, William Borges Domingues, Lucas Petitemberte de Souza, Mariana B. Michalowski, Vinicius F. Campos · 3. Juli 2026

  NA methylation profiling has become a powerful approach for central nervous system (CNS) tumor classification, yet important challenges remain regarding cross-cohort transferability, methodological correctness, and robust multiclass evaluation. In this work, we propose a novel and methodologically r…

• Physics-Grounded Disentangled Flow Modeling for Brain Disease Progression Trajectory

  Jun Wang, Peirong Liu · 30. Juni 2026

  Forecasting longitudinal brain lesion evolution is critical for disease monitoring and treatment planning. Existing approaches typically learn a direct mapping from a baseline image to a future observation, without explicitly modeling the physical mechanisms underlying the lesion progression. Such a…

• TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment

  Alia Tarek, Hamsa Saberr, Hamza Elghonemy, Youssef Afify, Tamer Basha, Omair Shahzad Bhatti, Abdulrahman M. Selim, Hasan Md Tusfiqur Alam Daniel Sonntag · 30. Juni 2026

  Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO. However, most deep learning methods predict response labels directly from imaging features, which limits clinical inspection, verification, an…

• Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

  Salman Shaik, Truong Thanh Hung Nguyen, Hung Cao · 25. Juni 2026

  Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framework for data-efficient, few-shot 3D volumetric MRI synthesis. ALDM utilizes a two…