Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models

Towards Adaptive Radiotherapy through Development of Treatment Response Prediction

Despite modern treatment advances, overall survival (OS) remains poor for many cancers such as liver and brain. Cancer is a fundamentally heterogeneous and adaptable disease and therefore personalized adaptive treatment strategies may be a key towards improving OS. Radiotherapy, a commonly used cancer treatment technique which employs ionizing radiation to kill tumours, holds promise for delivering adaptive treatment. However, effective adaptation requires the ability to assess and predict tumour treatment response. Therefore development of treatment response prediction tools represents a critical first step towards improving patient outcomes via treatment adaptation. The overall goal of this thesis is to develop treatment response prediction methods with a view towards guiding adaptive radiotherapy. First, we investigated the relationship between radiation dose and local tumour control among patients with primary and metastatic colorectal liver tumours. We established and compared their dose-response relationships and found that 84 Gy and 95 Gy of radiation could provide 90% probabilities of 6-month local control for the primary and metastatic groups respectively. Tumour control most often cannot be improved simply through escalating the dose to the entire tumour due to increased risk of side effects. However, it may be possible to safely increase the dose to tumour sub-volumes. Therefore, the second and third contributions of this thesis involve development of image-based treatment response prediction methods which are needed to identify tumour sub-volumes where additional radiation should be deposited to improve tumour control. Our second contribution involved augmenting a voxel-based method known as parametric response mapping (PRM) to account for image registration error (IRE). The augmented PRM helped to quantify and visualize IRE-related variability. In our third contribution, we further generalized PRM to permit collective analysis of multi-parametric image data. The proposed method was applied to multi-parametric imaging from a patient cohort with glioblastoma and was found to predict OS ≥ 18 months (median OS) with a sensitivity and specificity of 90% and 78% respectively. In summary, these contributions provided some of the response assessment groundwork needed to guide adaptive RT. Image-based dose response relationships via the augmented and multi-parametric response maps will facilitate personalization and guidance of adaptive radiotherapy

Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review

Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Magnetic Resonance Guided Radiotherapy for Rectal Cancer: Expanding Opportunities for Non-Operative Management

Colorectal cancer is the third most common cancer in men and the second most common in women worldwide, and the incidence is increasing among younger patients. 30% of these malignancies arise in the rectum. Patients with rectal cancer have historically been managed with preoperative radiation, followed by radical surgery, and adjuvant chemotherapy, with permanent colostomies in up to 20% of patients. Beginning in the early 2000s, non-operative management (NOM) of rectal cancer emerged as a viable alternative to radical surgery in select patients. Efforts have been ongoing to optimize neoadjuvant therapy for rectal cancer, thereby increasing the number of patients potentially eligible to forgo radical surgery. Magnetic resonance guided radiotherapy (MRgRT) has recently emerged as a treatment modality capable of intensifying preoperative radiation therapy for rectal cancer patients. This technology may also predict which patients will achieve a complete response to preoperative therapy, thereby allowing for more appropriate selection of patients for NOM. The present work seeks to illustrate the potential role MRgRT could play in personalizing rectal cancer treatment thus expanding the role of NOM in rectal cancer. Keywords: adaptive radiation therapy; chemoradiation; quality of life; radiation therapy; rectal cance

Neuroimaging as a selection tool and endpoint in preclinical and clinical trials

Standard imaging in acute stroke enables the exclusion of non-stroke structural CNS lesions and cerebral haemorrhage from clinical and pre-clinical ischaemic stroke trials. In this review, the potential benefit of imaging (e.g., angiography and penumbral imaging) as a translational tool for trial recruitment and the use of imaging endpoints are discussed for both clinical and pre-clinical stroke research. The addition of advanced imaging to identify a “responder” population leads to reduced sample size for any given effect size in phase 2 trials and is a potentially cost-efficient means of testing interventions. In pre-clinical studies, technical failures (failed or incomplete vessel occlusion, cerebral haemorrhage) can be excluded early and continuous multimodal imaging of the animal from stroke onset is feasible. Pre- and post-intervention repeat scans provide real time assessment of the intervention over the first 4–6 h. Negative aspects of advanced imaging in animal studies include increased time under general anaesthesia, and, as in clinical studies, a delay in starting the intervention. In clinical phase 3 trial designs, the negative aspects of advanced imaging in patient selection include higher exclusion rates, slower recruitment, overestimated effect size and longer acquisition times. Imaging may identify biological effects with smaller sample size and at earlier time points, compared to standard clinical assessments, and can be adjusted for baseline parameters. Mechanistic insights can be obtained. Pre-clinically, multimodal imaging can non-invasively generate data on a range of parameters, allowing the animal to be recovered for subsequent behavioural testing and/or the brain taken for further molecular or histological analysis

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)