Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods

• URL: http://arxiv.org/abs/2507.04881v1
• Date: Mon, 07 Jul 2025 11:11:55 GMT
• Title: Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
• Authors: Carmen Jimenez-Mesa, Yizhou Wan, Guilio Sansone, Francisco J. Martinez-Murcia, Javier Ramirez, Pietro Lio, Juan M. Gorriz, Stephen J. Price, John Suckling, Michail Mamalakis,
• Abstract summary: We propose a framework that integrates Explainable AI (XAI) with neuroimaging-based feature engineering for survival assessment.<n>Our findings suggest that survival is influenced by alterations in regions associated with cognitive and sensory functions.
• Score: 3.607831237046656
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Brain tumor resection is a complex procedure with significant implications for patient survival and quality of life. Predictions of patient outcomes provide clinicians and patients the opportunity to select the most suitable onco-functional balance. In this study, global features derived from structural magnetic resonance imaging in a clinical dataset of 49 pre- and post-surgery patients identified potential biomarkers associated with survival outcomes. We propose a framework that integrates Explainable AI (XAI) with neuroimaging-based feature engineering for survival assessment, offering guidance for surgical decision-making. In this study, we introduce a global explanation optimizer that refines survival-related feature attribution in deep learning models, enhancing interpretability and reliability. Our findings suggest that survival is influenced by alterations in regions associated with cognitive and sensory functions, indicating the importance of preserving areas involved in decision-making and emotional regulation during surgery to improve outcomes. The global explanation optimizer improves both fidelity and comprehensibility of explanations compared to state-of-the-art XAI methods. It effectively identifies survival-related variability, underscoring its relevance in precision medicine for brain tumor treatment.

Related papers

• SurgeryLSTM: A Time-Aware Neural Model for Accurate and Explainable Length of Stay Prediction After Spine Surgery [44.119171920037196]
  We develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery.<n>We compare traditional ML models with our developed model, SurgeryLSTM, a masked bidirectional long short-term memory (BiLSTM) with an attention.<n>Performance was evaluated using the coefficient of determination (R2) and key predictors were identified using explainable AI.
  arXiv  Detail & Related papers  (2025-07-15T01:18:28Z)
• MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning [52.231128973251124]
  We compare various strategies for predicting survival at the WSI and patient level.<n>The former treats each WSI as an independent sample, mimicking the strategy adopted in other works.<n>The latter comprises methods to either aggregate the predictions of the several WSIs or automatically identify the most relevant slide.
  arXiv  Detail & Related papers  (2025-03-29T11:14:02Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches [0.0]
  Brain tumors pose a serious health threat due to their rapid growth and potential for metastasis. This study aims to improve the efficiency and accuracy of brain tumor classification. Our approach combines state-of-the-art deep learning algorithms, including the Vision Transformer (ViT), Capsule Neural Network (CapsNet), and convolutional neural networks (CNNs) such as ResNet-152 and VGG16.
  arXiv  Detail & Related papers  (2024-10-31T07:28:06Z)
• Survival Prediction Across Diverse Cancer Types Using Neural Networks [40.392772795903795]
  Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies. Medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes. This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
  arXiv  Detail & Related papers  (2024-04-11T21:47:13Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma [1.880228463170355]
  Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with limited treatment options. This research proposes a workflow and deep learning-based segmentation models to automatically assess tumor-vessel involvement.
  arXiv  Detail & Related papers  (2023-10-01T10:39:38Z)
• Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer [37.55853672333369]
  This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients. The developed risk marker was the strongest predictor of overall survival among preoperative factors.
  arXiv  Detail & Related papers  (2023-08-01T12:46:02Z)
• Towards Understanding the Survival of Patients with High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms: An Investigation of Ensemble Feature Selection in the Prediction of Overall Survival [0.0]
  Ensemble feature selectors allow the user to identify such features in datasets with low sample sizes. RENT and UBayFS are capable of integrating expert knowledge a priori in the feature selection process. Our results demonstrate that both feature selectors allow accurate predictions, and that expert knowledge has a stabilizing effect on the feature set.
  arXiv  Detail & Related papers  (2023-02-20T17:08:03Z)
• COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [66.43957431843324]
  We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
  arXiv  Detail & Related papers  (2022-04-24T07:38:37Z)
• Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma [12.36330256366686]
  We propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction. A novel and problem-specific Feature-enhanced Auto-Encoder (FAE) is developed to enhance the representation of pairwise clinical modalities. The results demonstrate that the proposed approach can produce robust and clinically relevant MRI sub-regions and statistically significant survival predictions.
  arXiv  Detail & Related papers  (2021-08-21T02:47:59Z)
• Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients [13.263919134911237]
  We propose a machine learning framework to fine-tune the clustering algorithms and identify stable sub-regions for reliable clinical survival prediction. We incorporated the prior knowledge of intra-tumoral heterogeneity, by segmenting tumor sub-regions and extracting sub-regional features.
  arXiv  Detail & Related papers  (2020-12-05T20:29:53Z)

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.