AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling

• URL: http://arxiv.org/abs/2607.13877v1
• Date: Wed, 15 Jul 2026 14:26:14 GMT
• Title: AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling
• Abstract summary: We propose an AI-augmented adaptive digital twin (DT) framework for brain tumor evolution prediction and treatment scheduling.<n>The framework integrates an interpretable reaction--diffusion (RD) model, a 3D residual learning module for model-form correction, and patient-specific DT updating.
• Score: 13.239769357659412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy, making accurate long-term prediction challenging. We propose an AI-augmented adaptive digital twin (DT) framework for brain tumor evolution prediction and treatment scheduling. The framework integrates an interpretable reaction--diffusion (RD) model, a 3D residual learning module for model-form correction, patient-specific DT updating during recursive rollout, and model predictive control (MPC) for constrained chemotherapy and radiotherapy scheduling. Experiments on 387 synthetic tumor trajectories with 120-step evolution show that the baseline RD model captures tumor location and overall temporal behavior but underestimates heterogeneous tumor burden during long-horizon prediction. Hybrid RD--residual modeling reduces masked voxel-wise mean squared error by 84.3% and increases Dice overlap by 43.5% relative to the RD baseline under dense simulated observations. Online DT updating further reduces mean squared error by 45.9% and improves Dice overlap by 9.6% compared with the non-updated hybrid model. In MPC-based scheduling simulations, the updated DT controller reduces final tumor burden by 22.4% relative to a fixed treatment schedule under the terminal-burden objective. Together, these results demonstrate a unified framework for patient-specific initialization, mechanistic modeling, adaptive learning, and constrained treatment optimization. Although validated using patient-data-informed synthetic trajectories rather than clinical longitudinal data, the proposed framework establishes a foundation for future translation to real-world adaptive treatment planning.

Related papers

• D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities [54.87947751720332]
  We propose a novel segmentation model D3Seg to maintain stable performance under missing-modality settings.<n>The proposed model achieves approximately 1.5-2.0% Dice improvement on enhancing tumor (ET) and around 1.0% on tumor core (TC) across multiple missing modality configurations.
  arXiv  Detail & Related papers  (2026-05-21T09:55:42Z)
• Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma [0.0]
  Recent developments in artificial intelligence (AI) enable conditional multimodal image generation from clinical data.<n>In this study, we investigate AI-driven generation of follow-up MRI in patients with in- tracranial tumors.
  arXiv  Detail & Related papers  (2026-03-09T13:43:58Z)
• Brain-WM: Brain Glioblastoma World Model [56.45393372490125]
  We present Brain-WM, a pioneering GBM world model that unifies next-step treatment prediction and future MRI generation.<n>Brain-WM encodes latent dynamics into a shared latent space for joint autoregressive treatment prediction and flow-based future MRI generation.<n>Ultimately, Brain-WM offers a robust clinical sandbox for optimizing patient healthcare.
  arXiv  Detail & Related papers  (2026-03-08T09:54:08Z)
• Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis [0.815557531820863]
  Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology.<n>This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based Triplanar (2.5D) model for improved brain tumor segmentation.
  arXiv  Detail & Related papers  (2026-02-14T07:48:58Z)
• CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space [49.74032713886216]
  CLARITY is a medical world model that forecasts disease evolution directly within a structured latent space.<n>It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory.
  arXiv  Detail & Related papers  (2025-12-08T20:42:10Z)
• Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting [51.906871559732245]
  Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns.<n>Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and individualized 'digital-twin' forecasting.<n>We propose CNODE, a novel framework for continuous, individualized PD progression forecasting.
  arXiv  Detail & Related papers  (2025-11-06T20:16:33Z)
• 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)
• Multi-Task Diffusion Approach For Prediction of Glioma Tumor Progression [0.6978367196609415]
  Glioma is an aggressive brain malignancy that poses significant challenges for accurate evolution prediction.<n>In this paper, we present a multitask diffusion framework for time-agnostic, pixel-wise prediction of glioma progression.
  arXiv  Detail & Related papers  (2025-09-13T14:42:46Z)
• 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)
• Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models [18.222267022441734]
  Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates.<n> Partial differential equation-based models offer promising potential to enhance therapeutic outcomes.<n>We introduce an approach leveraging a neural forward solver with gradient-based optimization to significantly reduce calibration time.
  arXiv  Detail & Related papers  (2025-01-14T16:10:25Z)
• TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI [4.414541804340033]
  Temporally-Aware Diffusion Model (TADM) learns the distribution of structural changes in terms of intensity differences between scans. During training, we propose to leverage a pre-trained Brain-Age Estimator (BAE) to refine the model's training process. Our approach will benefit applications, such as predicting patient outcomes or improving treatments for patients.
  arXiv  Detail & Related papers  (2024-06-18T09:00:49Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy [12.05638699290782]
  We present a novel 3D sequence-to-sequence model based on Convolution Long Short Term Memory (ConvLSTM) It predicts future anatomical deformations and changes in gross tumor volume as well as critical OARs. We validated our model on two radiotherapy datasets.
  arXiv  Detail & Related papers  (2021-06-16T18:29:16Z)

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.