Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of survival of groups of patients treated similarly but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome. We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard of care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically-based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumor’s growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross validation technique. This relationship predicts the tumor size post therapy to within inter-observer tumor volume uncertainty. The results of this study suggest that a mathematical model can create a virtual in silico tumor with the same growth kinetics as a particular patient and can not only predict treatment response in individual patients in vivo but also provide a basis for evaluation of response in each patient to any given therapy.

Background and Purpose Positron emission tomography (PET) imaging with [F-18] fluoromisonidazole (FMISO) has been validated as a hypoxic tracer [1, 2]. Head and neck cancer exhibits hypoxia, inducing aggressive biologic traits that impart resistance to treatment. Delivery of modestly higher radiation doses to tumors with stable areas of chronic hypoxia can improve tumor control [3]. Advanced radiation treatment planning (RTP) and delivery techniques such as Intensity Modulated Radiation Therapy (IMRT) can deliver higher doses to a small volume without increasing morbidity. We investigated the utility of co-registered FMISO-PET and CT images to develop clinically feasible RTPs with higher tumor control probabilities (TCP). Methods FMISO-PET images were used to determine hypoxic sub-volumes for boost planning. Example plans were generated for ten of the patients in the study who exhibited significant hypoxia. We created an IMRT plan for each patient with a simultaneous integrated boost (SIB) to the hypoxic sub-volumes. We also varied the boost for two patients. Results A significant (mean 17%, median 15%) improvement in TCP is predicted when the modest additional boost dose to the hypoxic sub-volume is included. Conclusions Combined FMISO-PET imaging and IMRT planning permits delivery of higher doses to hypoxic regions, increasing the predicted TCP (mean 17%) without increasing expected complications.

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient's disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [18F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.

Increasing evidence supports the role of the tumor microenvironment in modulating cancer behavior. Tissue hypoxia, an important and common condition affecting the tumor microenvironment, is well established as a resistance factor in radiotherapy. Increasing evidence points to the ability of hypoxia to induce the expression of gene products, which confer aggressive tumor behavior and promote broad resistance to therapy. These factors suggest that determining the presence or absence of tumor hypoxia is important in planning cancer therapy. Recent advances in PET hypoxia imaging, conformal radiotherapy, and imaging-directed radiotherapy treatment planning now make it possible to perform hypoxia-directed radiotherapy. We review the biological aspects of tumor hypoxia and PET imaging approaches for measuring tumor hypoxia, along with methods for conformal radiotherapy and image-guided treatment, all of which provide the underpinnings for hypoxia-directed therapy. As a case example, we review emerging data on PET imaging of hypoxia to direct radiotherapy.