In silico analysis suggests differential response to bevacizumab and radiation combination therapy in newly diagnosed glioblastoma

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type

The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.

Speed Switch in Glioblastoma Growth Rate due to Enhanced Hypoxia-Induced Migration

We analyze the wave speed of the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model that was previously created and applied to simulate the growth and spread of glioblastoma (GBM), a particularly aggressive primary brain tumor. We extend the PIHNA model by allowing for different hypoxic and normoxic cell migration rates and study the impact of these differences on the wave-speed dynamics. Through this analysis, we find key variables that drive the outward growth of the simulated GBM. We find a minimum tumor wave-speed for the model; this depends on the migration and proliferation rates of the normoxic cells and is achieved under certain conditions on the migration rates of the normoxic and hypoxic cells. If the hypoxic cell migration rate is greater than the normoxic cell migration rate above a threshold, the wave speed increases above the predicted minimum. This increase in wave speed is explored through an eigenvalue and eigenvector analysis of the linearized PIHNA model, which yields an expression for this threshold. The PIHNA model suggests that an inherently faster-diffusing hypoxic cell population can drive the outward growth of a GBM as a whole, and that this effect is more prominent for faster-proliferating tumors that recover relatively slowly from a hypoxic phenotype. The findings presented here act as a first step in enabling patient-specific calibration of the PIHNA model.

Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to

The number of publications on mathematical modeling of cancer is growing at an exponential rate, according to PubMed records, provided by the US National Library of Medicine and the National Institutes of Health. Seminal papers have initiated and promoted mathematical modeling of cancer and have helped define the field of mathematical oncology (Norton and Simon in J Natl Cancer Inst 58:1735-1741, 1977; Norton in Can Res 48:7067-7071, 1988; Hahnfeldt et al. in Can Res 59:4770-4775, 1999; Anderson et al. in Comput Math Methods Med 2:129-154, 2000. https://doi.org/10.1080/10273660008833042 ; Michor et al. in Nature 435:1267-1270, 2005. https://doi.org/10.1038/nature03669 ; Anderson et al. in Cell 127:905-915, 2006. https://doi.org/10.1016/j.cell.2006.09.042 ; Benzekry et al. in PLoS Comput Biol 10:e1003800, 2014. https://doi.org/10.1371/journal.pcbi.1003800 ). Following the introduction of undergraduate and graduate programs in mathematical biology, we have begun to see curricula developing with specific and exclusive focus on mathematical oncology. In 2018, 218 articles on mathematical modeling of cancer were published in various journals, including not only traditional modeling journals like the Bulletin of Mathematical Biology and the Journal of Theoretical Biology, but also publications in renowned science, biology, and cancer journals with tremendous impact in the cancer field (Cell, Cancer Research, Clinical Cancer Research, Cancer Discovery, Scientific Reports, PNAS, PLoS Biology, Nature Communications, eLife, etc). This shows the breadth of cancer models that are being developed for multiple purposes. While some models are phenomenological in nature following a bottom-up approach, other models are more top-down data-driven. Here, we discuss the emerging trend in mathematical oncology publications to predict novel, optimal, sometimes even patient-specific treatments, and propose a convention when to use a model to predict novel treatments and, probably more importantly, when not to.

Hybrid modeling frameworks of tumor development and treatment

Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments.

This article is characterized under:

Analytical and Computational Methods > Computational Methods

Translational, Genomic, and Systems Medicine > Therapeutic Methods

Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy

In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).

Effects of combined radiosurgery and temozolomide therapy on epidermal growth factor receptor and variant III in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a highly malignant and notably aggressive primary tumour. Variant III of the epidermal growth factor receptor (EGFRvIII) is one of the most common types of variants in GBM, and serves an important role in tumour invasion, proliferation and treatment resistance. In the present study, statistical analyses were performed on data from 57 patients with GBM, and polymerase chain reaction detection was conducted on the tumour tissues from 32 of these patients. The results indicated that the EGFRvIII mutation was significantly associated with tumour malignancy. Human GBMU87-EGFRvIII cell lines were cultured and treated with radiosurgery and temozolomide individually, or with combined radiosurgery and temozolomide treatment. In vitro and in vivo experimental methods were used to detect the expression levels of Ki-67 and EGFRvIII. As verified in the present study, the EGFRvIII mutation is positively correlated with the malignancy of tumours, and combined radiosurgery and temozolomide therapy may inhibit the invasion and proliferation abilities of U87-EGFRvIII more effectively than treatment alone.

Computational modeling in glioblastoma: from the prediction of blood-brain barrier permeability to the simulation of tumor behavior

The integrated in silico-in vitro-in vivo approaches have fostered the development of new treatment strategies for glioblastoma patients and improved diagnosis, establishing the bridge between biochemical research and clinical practice. These approaches have provided new insights on the identification of bioactive compounds and on the complex mechanisms underlying the interactions among glioblastoma cells, and the tumor microenvironment. This review focuses on the key advances pertaining to computational modeling in glioblastoma, including predictive data on drug permeability across the blood-brain barrier, tumor growth and treatment responses. Structure- and ligand-based methods have been widely adopted, enabling the study of dynamic and evolutionary aspects of glioblastoma. Their potential applications as predictive tools and the advantages over other well-known methodologies are outlined. Challenges regarding in silico approaches for predicting tumor properties are also discussed.

Clinical implications of in silico mathematical modeling for glioblastoma: a critical review

Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.

Molecularly Targeted Drugs Plus Radiotherapy and Temozolomide Treatment for Newly Diagnosed Glioblastoma: A Meta-Analysis and Systematic Review

Glioblastoma (GBM) is the most common primary malignant brain tumor that nearly always results in a bad prognosis. Temozolomide plus radiotherapy (TEM+RAD) is the most common treatment for newly diagnosed GBM. With the development of molecularly targeted drugs, several clinical trials were reported; however, the efficacy of the treatment remains controversial. So we attempted to measure the dose of the molecularly targeted drug that could improve the prognosis of those patients. The appropriate electronic databases (PubMed, MEDLINE, EMBASE, and the Cochrane Library) were searched for relevant studies. A meta-analysis was performed after determining which studies met the inclusion criteria. Six randomized, controlled trials (RCTs) were identified for this meta-analysis, comprising 2,637 GBM patients. The benefit of overall survival (OS) was hazard ratio (HZ), 0.936 [95% confidence interval (CI), 0.852–1.028]. The benefit with respect to progression-free survival (PFS) rate was HZ of 0.796 (95% CI, 0.701–0.903). OS benefit of cilengitide was HZ of 0.792 (95% CI, 0.642–0.977). The adverse effects higher than grade 3 were 57.7% in the experimental group and 44.1% in the placebo group (odds ratio, 1.679; 95% CI, 1.434–1.967). The addition of molecularly targeted drugs to TEM + RAD did not improve the OS of patients with GBM; however, it did improve PFS in patients treated by cilengitide who could not get improvement in OS. The rate of adverse effects was higher in the experimental group than in the placebo group.