FoundBioNet: A Foundation-Based Model for IDH Genotyping of Glioma from Multi-Parametric MRI

• URL: http://arxiv.org/abs/2508.06756v1
• Date: Sat, 09 Aug 2025 00:08:10 GMT
• Title: FoundBioNet: A Foundation-Based Model for IDH Genotyping of Glioma from Multi-Parametric MRI
• Authors: Somayeh Farahani, Marjaneh Hejazi, Antonio Di Ieva, Sidong Liu,
• Abstract summary: We propose a Foundation-based Biomarker Network (FoundBioNet) to noninvasively predict IDH mutation status from multi-parametric MRI.<n>Our model was trained and validated on a diverse, multi-center cohort of 1705 glioma patients from six public datasets.<n>Our model achieved AUCs of 90.58%, 88.08%, 65.41%, and 80.31% on independent test sets from EGD, TCGA, Ivy GAP, RHUH, and UPenn.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate, noninvasive detection of isocitrate dehydrogenase (IDH) mutation is essential for effective glioma management. Traditional methods rely on invasive tissue sampling, which may fail to capture a tumor's spatial heterogeneity. While deep learning models have shown promise in molecular profiling, their performance is often limited by scarce annotated data. In contrast, foundation deep learning models offer a more generalizable approach for glioma imaging biomarkers. We propose a Foundation-based Biomarker Network (FoundBioNet) that utilizes a SWIN-UNETR-based architecture to noninvasively predict IDH mutation status from multi-parametric MRI. Two key modules are incorporated: Tumor-Aware Feature Encoding (TAFE) for extracting multi-scale, tumor-focused features, and Cross-Modality Differential (CMD) for highlighting subtle T2-FLAIR mismatch signals associated with IDH mutation. The model was trained and validated on a diverse, multi-center cohort of 1705 glioma patients from six public datasets. Our model achieved AUCs of 90.58%, 88.08%, 65.41%, and 80.31% on independent test sets from EGD, TCGA, Ivy GAP, RHUH, and UPenn, consistently outperforming baseline approaches (p <= 0.05). Ablation studies confirmed that both the TAFE and CMD modules are essential for improving predictive accuracy. By integrating large-scale pretraining and task-specific fine-tuning, FoundBioNet enables generalizable glioma characterization. This approach enhances diagnostic accuracy and interpretability, with the potential to enable more personalized patient care.

Related papers

• R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression [63.97617759805451]
  Early detection of Alzheimer's disease requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility.<n>We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting.<n>R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition, subjective memory concerns, mild cognitive impairment, and AD.
  arXiv  Detail & Related papers  (2025-12-22T02:54:10Z)
• Squeezed-Eff-Net: Edge-Computed Boost of Tomography Based Brain Tumor Classification leveraging Hybrid Neural Network Architecture [0.7829352305480285]
  This work proposes a hybrid deep learning model based on SqueezeNet v1 which is a lightweight model, and EfficientNet-B0, which is a high-performing model.<n>The framework was trained and tested only on publicly available Nickparvar Brain Tumor MRI dataset.
  arXiv  Detail & Related papers  (2025-12-08T07:37:30Z)
• UAM: A Unified Attention-Mamba Backbone of Multimodal Framework for Tumor Cell Classification [1.529342790344802]
  We introduce a Unified Attention-Mamba backbone for cell-level classification using radiomics features.<n>We propose a multimodal UAM framework that jointly performs cell-level classification and image segmentation.
  arXiv  Detail & Related papers  (2025-11-21T16:18:55Z)
• CECT-Mamba: a Hierarchical Contrast-enhanced-aware Model for Pancreatic Tumor Subtyping from Multi-phase CECT [14.2491883349628]
  This paper introduces an automatic way to combine the multi-phase CECT data to discriminate between pancreatic tumor subtypes.<n> Specifically, we propose a dual hierarchical contrast-enhanced-aware Mamba module incorporating two novel spatial and temporal sampling sequences.<n>The experimental results on an in-house dataset of 270 clinical cases achieve an accuracy of 97.4% and an AUC of 98.6% in distinguishing between pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (PNETs)
  arXiv  Detail & Related papers  (2025-09-16T07:48:11Z)
• impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction [75.43342771863837]
  We introduce impuTMAE, a novel transformer-based end-to-end approach with an efficient multimodal pre-training strategy.<n>It learns inter- and intra-modal interactions while simultaneously imputing missing modalities by reconstructing masked patches.<n>Our model is pre-trained on heterogeneous, incomplete data and fine-tuned for glioma survival prediction using TCGA-GBM/LGG and BraTS datasets.
  arXiv  Detail & Related papers  (2025-08-08T10:01:16Z)
• Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data [36.92842246372894]
  Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN) is a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples.<n>By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability.
  arXiv  Detail & Related papers  (2025-03-29T02:14:05Z)
• Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma [0.2796197251957244]
  Multi-Task S-UNETR (MTSUNET) model is a novel foundation-based framework built on the BrainSegFounder model.<n>It simultaneously performs glioma segmentation, histological subtyping and neuroimaging subtyping.<n>It shows significant potential for advancing noninvasive, personalized glioma management by improving predictive accuracy and interpretability.
  arXiv  Detail & Related papers  (2025-03-10T01:27:09Z)
• Classification of lung cancer subtypes on CT images with synthetic pathological priors [41.75054301525535]
  Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images. We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
  arXiv  Detail & Related papers  (2023-08-09T02:04:05Z)
• MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network [0.18374319565577152]
  Isocitrate dehydrogenase mutation and 1p/19q codeletion status are important prognostic markers for glioma. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status.
  arXiv  Detail & Related papers  (2022-10-07T18:46:39Z)
• Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types [41.61294299606317]
  Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images.
  arXiv  Detail & Related papers  (2022-04-07T07:02:32Z)
• Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN [40.50404819220093]
  A novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of Alzheimer's disease (AD) MP-GAN achieves superior performance compared with the existing methods.
  arXiv  Detail & Related papers  (2021-11-25T03:24:52Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.