Visual analysis of tumor control models for prediction of radiotherapy response

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling

PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support

Radiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain. We present PREVIS: a visual analytics tool for (i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and (ii) the assessment of treatment strategies, with respect to the anticipated changes. Records of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS, to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component Analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient's organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy. We present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers

Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool

Functional imaging techniques provide radiobiological information that can be included into tumour control probability (TCP) models to enable individualized outcome predictions in radiotherapy. However, functional imaging and the derived radiobiological information are influenced by uncertainties, translating into variations in individual TCP predictions. In this study we applied a previously developed analytical tool to quantify dose and TCP uncertainty bands when initial cell density is estimated from MRI-based apparent diffusion coefficient maps of eleven patients. TCP uncertainty bands of 16% were observed at patient level, while dose variations bands up to 8 Gy were found at voxel level for an iso-TCP approach.Comp Graphics & Visualisatio