Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation

Glioblastoma are known to infiltrate the brain parenchyma instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In clinical practice, a uniform margin is applied to account for microscopic spread of disease. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. A retrospective study involving 10 glioblastoma patients has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioblastoma, and may therefore provide a tool to make target delineation more objective and automated

Imaging practice in low-grade gliomas among European specialized centers and proposal for a minimum core of imaging

Objective: Imaging studies in diffuse low-grade gliomas (DLGG) vary across centers. In order to establish a minimal core of imaging necessary for further investigations and clinical trials in the field of DLGG, we aimed to establish the status quo within specialized European centers. Methods: An online survey composed of 46 items was sent out to members of the European Low-Grade Glioma Network, the European Association of Neurosurgical Societies, the German Society of Neurosurgery and the Austrian Society of Neurosurgery. Results: A total of 128 fully completed surveys were received and analyzed. Most centers (n=96, 75%) were academic and half of the centers (n=64, 50%) adhered to a dedicated treatment program for DLGG. There were national differences regarding the sequences enclosed in MRI imaging and use of PET, however most included T1 (without and with contrast, 100%), T2 (100%) and TIRM or FLAIR (20, 98%). DWI is performed by 80% of centers and 61% of centers regularly performed PWI.ConclusionA minimal core of imaging composed of T1 (w/wo contrast), T2, TIRM/FLAIR, PWI and DWI could be identified. All morphologic images should be obtained in a slice thickness of 3mm. No common standard could be obtained regarding advanced MRI protocols and PET. Importance of the study: We believe that our study makes a significant contribution to the literature because we were able to determine similarities in numerous aspects of LGG imaging. Using the proposed minimal core of imaging in clinical routine will facilitate future cooperative studies

Functional Imaging of Malignant Gliomas with CT Perfusion

The overall survival of patients with malignant gliomas remains dismal despite multimodality treatments. Computed tomography (CT) perfusion is a functional imaging tool for assessing tumour hemodynamics. The goals of this thesis are to 1) improve measurements of various CT perfusion parameters and 2) assess treatment outcomes in a rat glioma model and in patients with malignant gliomas. Chapter 2 addressed the effect of scan duration on the measurements of blood flow (BF), blood volume (BV), and permeability-surface area product (PS). Measurement errors of these parameters increased with shorter scan duration. A minimum scan duration of 90 s is recommended. Chapter 3 evaluated the improvement in the measurements of these parameters by filtering the CT perfusion images with principal component analysis (PCA). From computer simulation, measurement errors of BF, BV, and PS were found to be reduced. Experiments showed that CT perfusion image contrast-to-noise ratio was improved. Chapter 4 investigated the efficacy of CT perfusion as an early imaging biomarker of response to stereotactic radiosurgery (SRS). Using the C6 glioma model, we showed that responders to SRS (surviving \u3e 15 days) had lower relative BV and PS on day 7 post-SRS when compared to controls and non-responders (P \u3c 0.05). Relative BV and PS on day 7 post-SRS were predictive of survival with 92% accuracy. Chapter 5 examined the use of multiparametric imaging with CT perfusion and 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) to identify tumour sites that are likely to correlate with the eventual location of tumour progression. We developed a method to generate probability maps of tumour progression based on these imaging data. Chapter 6 investigated serial changes in tumour volumetric and CT perfusion parameters and their predictive ability in stratifying patients by overall survival. Pre-surgery BF in the non-enhancing lesion and BV in the contrast-enhancing lesion three months after radiotherapy had the highest combination of sensitivities and specificities of ≥ 80% in predicting 24 months overall survival. iv Optimization and standardization of CT perfusion scans were proposed. This thesis also provided corroborating evidence to support the use of CT perfusion as a biomarker of outcomes in patients with malignant gliomas

Convection enhanced delivery in the setting of high‐grade gliomas

Development of effective treatments for high-grade glioma (HGG) is hampered by (1) the blood–brain barrier (BBB), (2) an infiltrative growth pattern, (3) rapid development of therapeutic resistance, and, in many cases, (4) dose-limiting toxicity due to systemic exposure. Convection-enhanced delivery (CED) has the potential to significantly limit systemic toxicity and increase therapeutic index by directly delivering homogenous drug concentrations to the site of disease. In this review, we present clinical experiences and preclinical developments of CED in the setting of high-grade gliomas

Engineered Extracellular Vesicles: Processing and testing of cell-derived Exos

In recent decades, endogenous nanocarrier-exosomes have received considerable scientific interest as drug delivery systems. The unique proteo-lipid architecture allows the crossing of various natural barriers and protects Exosomes cargo from degradation in the bloodstream. However, the presence of this bilayer membrane as well as their endogenous content make production and loading of exogenous molecules challenging. In the present work, we will investigate how to promote the manipulation of cellular. And vesicles curvature by a high pressure microfluidic system as ground-breaking method for both Exosomes production and encapsulation. First of all, we exploited this approach to isolate the Exosomes derived by Glioblastoma U87-MG tumoral cell line. An increased yield and purity of Exosomes have been obtained. Furthermore, we proposed a complete protein-profiling comparing traditional isolation method in cell medium and isolation by Dynamic High-Pressure Homogenization. To validate our approach for Exosomes encapsulation, we tested in vitro the prodrug Irinotecan (IRI) in U87-MG Exosomes. As a result, we obtained a high EE, up to 45%, comparable to the principal industrial methodologies used for polymer nanoparticles, shortening the processing time for the encapsulation to several days to 1 hr, improving the drug uptake, and entirely avoiding the use of permeabilization enhancers. Also, this new approach has been tested on Doxorubicin and validated on a different cell lines and 3D cells model. Finally, we performed in vitro preliminary analysis to further understand Exosomes fate and nanobiointeraction with biological environment

Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection

Gliomas are the most frequent primary brain tumors in adults. Glioma change detection aims at finding the relevant parts of the image that change over time. Although Deep Learning (DL) shows promising performances in similar change detection tasks, the creation of large annotated datasets represents a major bottleneck for supervised DL applications in radiology. To overcome this, we propose a combined use of weak labels (imprecise, but fast-to-create annotations) and Transfer Learning (TL). Specifically, we explore inductive TL, where source and target domains are identical, but tasks are different due to a label shift: our target labels are created manually by three radiologists, whereas our source weak labels are generated automatically from radiology reports via NLP. We frame knowledge transfer as hyperparameter optimization, thus avoiding heuristic choices that are frequent in related works. We investigate the relationship between model size and TL, comparing a low-capacity VGG with a higher-capacity ResNeXt model. We evaluate our models on 1693 T2-weighted magnetic resonance imaging difference maps created from 183 patients, by classifying them into stable or unstable according to tumor evolution. The weak labels extracted from radiology reports allowed us to increase dataset size more than 3-fold, and improve VGG classification results from 75% to 82% AUC. Mixed training from scratch led to higher performance than fine-tuning or feature extraction. To assess generalizability, we ran inference on an open dataset (BraTS-2015: 15 patients, 51 difference maps), reaching up to 76% AUC. Overall, results suggest that medical imaging problems may benefit from smaller models and different TL strategies with respect to computer vision datasets, and that report-generated weak labels are effective in improving model performances. Code, in-house dataset and BraTS labels are released.Comment: This work has been submitted as Original Paper to a Journa

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