Learning patient-specific parameters for a diffuse interface glioblastoma model from neuroimaging data

Parameters in mathematical models for glioblastoma multiforme (GBM) tumour growth are highly patient specific. Here we aim to estimate parameters in a Cahn-Hilliard type diffuse interface model in an optimised way using model order reduction (MOR) based on proper orthogonal decomposition (POD). Based on snapshots derived from finite element simulations for the full order model (FOM) we use POD for dimension reduction and solve the parameter estimation for the reduced order model (ROM). Neuroimaging data are used to define the highly inhomogeneous diffusion tensors as well as to define a target functional in a patient specific manner. The reduced order model heavily relies on the discrete empirical interpolation method (DEIM) which has to be appropriately adapted in order to deal with the highly nonlinear and degenerate parabolic PDEs. A feature of the approach is that we iterate between full order solves with new parameters to compute a POD basis function and sensitivity based parameter estimation for the ROM problems. The algorithm is applied using neuroimaging data for two clinical test cases and we can demonstrate that the reduced order approach drastically decreases the computational effort

Localization and parcellation of the supplementary motor area using functional magnetic resonance imaging in frontal tumor patients

Neurosurgery is an effective method for prolonging life and improving outcomes for patients with brain tumors. However, this option bears the risk of damaging areas of eloquent cortex, areas associated with motor and language tasks that, when lesioned, will result in a functional deficit for the patient. Functional magnetic resonance imaging (fMRI) is a valuable tool in the localization of eloquent cortex for preoperative neurosurgical planning. Through use of this modality of functional neuroimaging, the neurosurgeon can adjust the surgical trajectory to incur the least amount of damage to sites of functional activity. The supplementary motor area (SMA) is one such site of eloquent cortex that must be visualized preoperatively due to the risk of postoperative deficit with lesions in this area. However, due to both the effects of tumor pathology and naturally occurring interindividual variability, the SMA’s location and functional fingerprint can be highly variable. We present a study in which patients with frontal tumor (n=46) underwent task-based fMRI for motor and language network mapping. The patient-specific functional data were normalized and evaluated using ROI analysis to illustrate group-level activation patterns within the SMA during the language and motor tasks. The results illustrate a distinct pattern of activation including a rostro-caudal organization of language and motor activation, overlapping extent cluster volumes throughout the two functional subdivisions of the SMA, the pre-SMA and SMA proper, and discrete activation foci

Brain Tumor Imaging and Treatment Effects. Imaging findings and cognitive function in glioblastoma patients.

AbstractBackground: Glioblastoma is the most common malignant brain tumor. Operation with maximal resection, if feasible, otherwise biopsy followed by radiotherapy and chemotherapy with temozolomide is standard therapy. The prognosis remains poor, with median overall survival being 15 months despite therapy. Improved monitoring and treatment response assessment will be important when seeking to improve treatment efficacy and patient quality of life.Aims: The present work sought to follow newly diagnosed glioblastoma patients by imaging and clinical monitoring. Specific aims were to study the impact of surgical resection degree on prognosis and the effects of currently used therapies, including arc-based rotation radiotherapy, longitudinally. Aims were also to study radiological parameters with advanced magnetic resonance imaging (MRI) as well as patient neurological and cognitive functions in order to early identify prognostic factors. Material and methods: In paper I, volumetric assessment by quantitative and subjective methods was retrospectively studied from pre- and postoperative MRI in glioblastoma patients undergoing tumor resection. Influence of extent of resection of contrast enhanced tumor on progression-free survival and overall survival was analyzed, measured as relative extent of resection (EOR) and absolute residual tumor volume (RTV). In the present MRI brain tumor study, patients newly diagnosed with glioblastoma undergoing treatment with arc-based radiotherapy were studied longitudinally over a one-year period and constituted the patient cohort of papers II-IV, using advanced MRI, including diffusion-weighted imaging sequences. Microstructural changes in non-tumorous brain structures, including white matter (corpus callosum, centrum semiovale) and the limbic system (hippocampus, amygdala), were assessed by diffusion tensor imaging (DTI) during and after irradiation. By parametric response mapping (PRM) changes of mean diffusivity (MD) in tumor regions were analyzed as MD-PRM. Baseline examinations were compared with examinations 3 weeks into radiotherapy voxel-wise, analyzing the MD-difference as prediction of therapy response and survival. Clinical parameters were monitored from start of radiotherapy up to one year and included correlation of cognition, measured by the computerized test-battery CNS-vital signs (CNS-VS), with therapy and disease progression.Results: Quantitative volumetric measurements, especially residual tumor volume of ≤1,6 mL, showed prognostic significance for longer progression-free and overall survival. The quantitative volumetric method was superior in reproducibility compared to conventional estimation. MD-PRM demonstrated that in patients only undergoing diagnostic biopsy MD-PRM, changes indicated prognostic specificity for treatment response at 8 months. Significant longitudinal DTI changes were only observed in the body of the corpus callosum during and up to one year from radiotherapy. Evaluation of cognitive performance in glioblastoma patients using cognitive test scores by CNS-VS at baseline were in lower-average or low, compared to standard test average in 4 main domains: executive function, visual and verbal memory and complex attention. Cognitive function remained stable without further deterioration during one year follow up after radiotherapy was initiated. Better cognitive function at therapy begin correlated with longer progression-free and overall survival. Conclusion: Quantitative volumetric assessment has prognostic impact on glioblastoma patients progression-free and overall survival in favor of gross total resection. MD-PRM could not predict treatment response as assessed in the entire patient cohort, but may have predictive value in biopsied patients. Longitudinal monitoring up to one year after initiated radiotherapy did not reveal any major changes, neither in microstructural changes by diffusion tensor imaging (DTI) parameters, nor in patients cognitive function, indicating less neurotoxicity by arc-based radiotherapy

Imaging Based Prediction of Pathology in Adult Diffuse Glioma with Applications to Therapy and Prognosis

The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known. In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the underlying biological heterogeneity via imaging. This provides an approach to answer questions about how tissue heterogeneity can affect prognosis. We estimated the local pathology within tumors using imaging data and stereotactically precise biopsy samples from an ongoing clinical imaging trial. From this data, we trained a random forest model to reliably predict tumor grade, proliferation, cellularity, and vascularity, representing tumor aggressiveness. We then made voxel-wise predictions to map the tumor heterogeneity and identify high-grade malignancy disease. Next, we used the previously trained models on a cohort of 1,850 glioma patients who previously underwent surgical resection. High contrast enhancement, proliferation, vascularity, and cellularity were associated with worse prognosis even after controlling for clinical factors. Patients that had substantial reduction in cellularity between preoperative and postoperative imaging (i.e. due to resection) also showed improved survival. We developed a clinically implementable model for predicting pathology and prognosis after surgery based on imaging. Results from imaging pathology correlations enhance our understanding of disease extent within glioma patients and the relationship between residual estimated pathology and outcome helps refine our knowledge of the interaction of tumor heterogeneity and prognosis

Coupling solid and fluid stresses with brain tumour growth and white matter tract deformations in a neuroimaging-informed model

Brain tumours are among the deadliest types of cancer, since they display a strong ability to invade the surrounding tissues and an extensive resistance to common therapeutic treatments. It is therefore important to reproduce the heterogeneity of brain microstructure through mathematical and computational models, that can provide powerful instruments to investigate cancer progression. However, only a few models include a proper mechanical and constitutive description of brain tissue, which instead may be relevant to predict the progression of the pathology and to analyse the reorganization of healthy tissues occurring during tumour growth and, possibly, after surgical resection. Motivated by the need to enrich the description of brain cancer growth through mechanics, in this paper we present a mathematical multiphase model that explicitly includes brain hyperelasticity. We find that our mechanical description allows to evaluate the impact of the growing tumour mass on the surrounding healthy tissue, quantifying the displacements, deformations, and stresses induced by its proliferation. At the same time, the knowledge of the mechanical variables may be used to model the stress-induced inhibition of growth, as well as to properly modify the preferential directions of white matter tracts as a consequence of deformations caused by the tumour. Finally, the simulations of our model are implemented in a personalized framework, which allows to incorporate the realistic brain geometry, the patient-specific diffusion and permeability tensors reconstructed from imaging data and to modify them as a consequence of the mechanical deformation due to cancer growth

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing