Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the prediction of spatial distributions of tumor cells. However, this requires estimating patient-specific parameters of the model from clinical data, which is a challenging inverse problem due to limited temporal data and the limited time between imaging and diagnosis. This work proposes a method that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific parameters of a reaction-diffusion partial differential equation (PDE) model of GBM growth from a single 3D structural MRI snapshot. PINNs embed both the data and the PDE into a loss function, thus integrating theory and data. Key innovations include the identification and estimation of characteristic non-dimensional parameters, a pre-training step that utilizes the non-dimensional parameters and a fine-tuning step to determine the patient specific parameters. Additionally, the diffuse-domain method is employed to handle the complex brain geometry within the PINN framework. The method is validated on both synthetic and patient datasets, showing promise for personalized GBM treatment through parametric inference within clinically relevant timeframes.

Validating the predictions of mathematical models describing tumor growth and treatment response

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient's cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them.

A review of deep learning for brain tumor analysis in MRI

Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and opportunities.

Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling

PURPOSE

We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) time.

METHODS

An established reaction-diffusion model was employed to simulate the spatiotemporal evolution of cancerous regions in T1-MRI images of GBM patients. The effects of the parameters of this model on the simulated tumor borders were quantified and the optimal values were used to estimate the distribution of infiltrative cells (CTVmodel). Radiomics and deep learning models were examined to predict the OS time by analyzing the GTV, clinical CTV, and CTVmodel.

RESULTS

The study involves 126 subjects for model development and 62 independent subjects for testing. Evaluation of the proposed approach demonstrates comparable predictive power for OS time that is achieved with the clinically defined CTV. Specifically, for the binary survival prediction, short vs. long time, the proposed CTVmodelresulted in improvements of prognostic power, in terms of AUROC, both for the validation (0.77 from 0.75) and the testing (0.73 from 0.71) set. Quantitative comparisons for three-class prediction and survival regression models exhibited a similar trend of comparable performance.

CONCLUSION

The findings highlight the potential of biophysical modeling for estimating and incorporating the spread of infiltrating cells into CTV delineation. Further clinical investigations are required to validate the clinical efficacy.

Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patients

Background and purpose

Diffusion tensor imaging (DTI) has been proposed to guide the anisotropic expansion from gross tumor volume to clinical target volume (CTV), aiming to integrate known tumor spread patterns into the CTV. This study investigate the potential of using a DTI atlas as an alternative to patient-specific DTI for generating anisotropic CTVs.

Materials and Methods

The dataset consisted of twenty-eight newly diagnosed glioblastoma patients from a Danish national DTI protocol with post-operative T1-contrast and DTI imaging. Three different DTI atlases, spatially aligned to the patient images using deformable image registration, were considered as alternatives. Anisotropic CTVs were constructed to match the volume of a 15 mm isotropic expansion by generating 3D distance maps using either patient- or atlas-DTI as input to the shortest path solver. The degree of CTV anisotropy was controlled by the migration ratio, modeling tumor cell migration along the dominant white matter fiber direction extracted from DTI. The similarity between patient- and atlas-DTI CTVs was analyzed using the Dice Similarity Coefficient (DSC), with significance testing according to a Wilcoxon test.

Results

The median (range) DSC between anisotropic CTVs generated using patient-specific and atlas-based DTI was 0.96 (0.93-0.97), 0.96 (0.93-0.97), and 0.95 (0.93-0.97) for the three atlases, respectively (p > 0.01), for a migration ratio of 10. The results remained consistent over the range of studied migration ratios (2 to 100).

Conclusion

The high degree of similarity between all anisotropic CTVs indicates that atlas-DTI is a viable replacement for patient-specific DTI for incorporating fiber direction into the CTV.

Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development

Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.

The influence of anisotropy on the clinical target volume of brain tumor patients

Objective.Current radiotherapy guidelines for glioma target volume definition recommend a uniform margin expansion from the gross tumor volume (GTV) to the clinical target volume (CTV), assuming uniform infiltration in the invaded brain tissue. However, glioma cells migrate preferentially along white matter tracts, suggesting that white matter directionality should be considered in an anisotropic CTV expansion. We investigate two models of anisotropic CTV expansion and evaluate their clinical feasibility.Approach.To incorporate white matter directionality into the CTV, a diffusion tensor imaging (DTI) atlas is used. The DTI atlas consists of water diffusion tensors that are first spatially transformed into local tumor resistance tensors, also known as metric tensors, and secondly fed to a CTV expansion algorithm to generate anisotropic CTVs. Two models of spatial transformation are considered in the first step. The first model assumes that tumor cells experience reduced resistance parallel to the white matter fibers. The second model assumes that the anisotropy of tumor cell resistance is proportional to the anisotropy observed in DTI, with an 'anisotropy weighting parameter' controlling the proportionality. The models are evaluated in a cohort of ten brain tumor patients.Main results.To evaluate the sensitivity of the model, a library of model-generated CTVs was computed by varying the resistance and anisotropy parameters. Our results indicate that the resistance coefficient had the most significant effect on the global shape of the CTV expansion by redistributing the target volume from potentially less involved gray matter to white matter tissue. In addition, the anisotropy weighting parameter proved useful in locally increasing CTV expansion in regions characterized by strong tissue directionality, such as near the corpus callosum.Significance.By incorporating anisotropy into the CTV expansion, this study is a step toward an interactive CTV definition that can assist physicians in incorporating neuroanatomy into a clinically optimized CTV.