PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma

• URL: http://arxiv.org/abs/2509.13360v1
• Date: Mon, 15 Sep 2025 13:23:23 GMT
• Title: PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma
• Authors: L. Zimmer, J. Weidner, M. Balcerak, F. Kofler, I. Ezhov, B. Menze, B. Wiestler,
• Abstract summary: We introduce PREDICT-GBM, a comprehensive integrated pipeline and dataset for modeling and evaluation.<n>This platform enables systematic benchmarking of state-of-the-art tumor growth models using an expert-curated clinical dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Glioblastoma is the most prevalent primary brain malignancy, distinguished by its highly invasive behavior and exceptionally high rates of recurrence. Conventional radiation therapy, which employs uniform treatment margins, fails to account for patient-specific anatomical and biological factors that critically influence tumor cell migration. To address this limitation, numerous computational models of glioblastoma growth have been developed, enabling generation of tumor cell distribution maps extending beyond radiographically visible regions and thus informing more precise treatment strategies. However, despite encouraging preliminary findings, the clinical adoption of these growth models remains limited. To bridge this translational gap and accelerate both model development and clinical validation, we introduce PREDICT-GBM, a comprehensive integrated pipeline and dataset for modeling and evaluation. This platform enables systematic benchmarking of state-of-the-art tumor growth models using an expert-curated clinical dataset comprising 255 subjects with complete tumor segmentations and tissue characterization maps. Our analysis demonstrates that personalized radiation treatment plans derived from tumor growth predictions achieved superior recurrence coverage compared to conventional uniform margin approaches for two of the evaluated models. This work establishes a robust platform for advancing and systematically evaluating cutting-edge tumor growth modeling approaches, with the ultimate goal of facilitating clinical translation and improving patient outcomes.

Related papers

• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics [13.83306392535935]
  Accurate prediction of tumor trajectories under standard-of-care (SoC) therapies remains a major unmet need in oncology.<n>We introduce Standard-of-Care Digital Twin (SoC-DT), a differentiable framework that unifies reaction-diffusion tumor growth models.<n>An implicit-explicit exponential time-differencing solver, IMEX-SoC, is also proposed, which ensures stability, positivity, and scalability in SoC treatment situations.
  arXiv  Detail & Related papers  (2025-09-29T04:14:11Z)
• Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth [2.1289132027940605]
  We train our model on the BraTS adult and pediatric glioma and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases.<n>Our framework generates realistic follow-up scans based on spatial similarity metrics.<n>It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth.
  arXiv  Detail & Related papers  (2025-09-11T16:52:09Z)
• A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer [54.58205672910646]
  RenalCLIP is a visual-language foundation model for characterization, diagnosis and prognosis of renal mass.<n>It achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer.
  arXiv  Detail & Related papers  (2025-08-22T17:48:19Z)
• Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning [50.16114813279694]
  We introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions.<n>MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models.<n>We propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan.
  arXiv  Detail & Related papers  (2025-06-02T23:50:40Z)
• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• Interactive Tumor Progression Modeling via Sketch-Based Image Editing [54.47725383502915]
  We propose SkEditTumor, a sketch-based diffusion model for controllable tumor progression editing.<n>By leveraging sketches as structural priors, our method enables precise modifications of tumor regions while maintaining structural integrity and visual realism.<n>Our contributions include a novel integration of sketches with diffusion models for medical image editing, fine-grained control over tumor progression visualization, and extensive validation across multiple datasets, setting a new benchmark in the field.
  arXiv  Detail & Related papers  (2025-03-10T00:04:19Z)
• Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks [0.0]
  We present a proof-of-concept for a mathematical model of GBL growth, enabling real-time prediction and patient-specific parameter identification.<n>A neural network surrogate learns the inverse mapping from tumor evolution to model parameters, achieving significant computational speed-up.
  arXiv  Detail & Related papers  (2024-12-04T18:46:05Z)
• Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization [3.666412718346211]
  We introduce a novel method that integrates data-driven and physics-based cost functions. We propose a unique discretization scheme that quantifies how well the learned distributions of tumor and brain tissues adhere to their respective growth and elasticity equations.
  arXiv  Detail & Related papers  (2024-09-30T15:36:14Z)
• MGH Radiology Llama: A Llama 3 70B Model for Radiology [50.42811030970618]
  This paper presents an advanced radiology-focused large language model: MGH Radiology Llama.<n>It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2.<n>Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.
  arXiv  Detail & Related papers  (2024-08-13T01:30:03Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction [4.850774880198265]
  We propose a graph encoder that utilizes a bipartite Graph Convolutional Neural network (GCN) combined with Neural Ordinary Differential Equations (Neural-ODEs) We first show that the methodology is able to discover a tumor dynamic model that significantly improves upon an empirical model. Our findings indicate that the methodology holds significant promise and offers potential applications in pre-clinical settings.
  arXiv  Detail & Related papers  (2023-10-02T06:39:08Z)
• Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction [0.5667953366862138]
  We present a novel end-to-end network capable of future predictions of tumor masks and multi-parametric magnetic resonance images (MRI) of how the tumor will look at any future time points for different treatment plans.<n>Our approach is based on cutting-edge diffusion probabilistic models and deep-segmentation neural networks.
  arXiv  Detail & Related papers  (2023-09-11T12:12:52Z)

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.