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Figure 1. Visual computing for cohort-based radiation therapy (RT) prediction. A stylized 3D view of the predicted radiation plan of the current patient is placed centrally; top pale markers (front and back of eyes) receive the least radiation; tumors (black markers) receive the most. Additional RT views show the most similar patients under our novel T-SSIM measure, who contribute to the prediction; the most similar patient is currently highlighted (white) for comparison. A scatterplot (left) shows 4 clusters generated through the T-SSIM measure, with the current (cross) and comparison patient highlighted. A parallel-marker encoding (bottom) shows the predicted (blue cross) per-organ dose distribution within the context of the most similar patients; spatially collocated organs are in contiguous sections of the x-axis.Abstract-We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.

Hybrid virtual reality environments allow analysts to choose how much of the screen real estate they want to use for Virtual Reality (VR) immersion, and how much they want to use for displaying different types of 2D data. We present the use-based design and evaluation of an immersive visual analytics application for cosmological data that uses such a 2D/3D hybrid environment. The applications is a first-in-kind immersive instantiation of the Activity-Centered-Design theoretical paradigm, as well as a first documented immersive instantiation of a details-first paradigm based on scientific workflow theory. Based on a rigorous analysis of the user activities and on a details-first paradigm, the application was designed to allow multiple domain experts to interactively analyze visual representations of spatial (3D) and nonspatial (2D) cosmology data pertaining to dark matter formation. These hybrid data are represented at multiple spatiotemporal scales as time-aligned merger trees, pixel-based heatmaps, GPU-accelerated point clouds and geometric primitives, which can further be animated according to simulation data and played back for analysis. We have demonstrated this multi-scale application to several groups of lay users and domain experts, as well as to two senior domain experts from the Adler Planetarium, who have significant experience in immersive environments. Their collective feedback shows that this hybrid, immersive application can assist researchers in the interactive visual analysis of large-scale cosmological simulation data while overcoming navigation limitations of desktop visualizations.

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patients had their HPV clinically tested, there were significant discrepancies, with a sensitivity of the clinical tests of 71.7%, and a specificity of 33.3%. Immunohistochemistry with p16 (nZ159) performed better with a higher sensitivity of 91.8%, and a specificity of 33.3%. There were no differences in patient sex, age, stage, smoking status, or treatment modalities among the different HPV subtype groups. At a median follow up time of 20 months, the 2-year LRC for HPV16+, HPV16 +other, and HPVnon16+ was 92%, 88.9%, and 75.2% respectively (pZ0.026 for the comparison of HPV16+ and HPVnon16+). There were no significant differences in 2-year FFDM by subtype: 81.1% (HPV16+), 88.9% (HPV16+other), and 84.6% (HPVnon16+). Actuarial rates of 2-year OS were not significantly different: 81% for HPV16+, 100% for HPV16+other, and 68.2% for HPVnon16+ (pZ0.239 for HPV16+ vs HPVnon16+ comparison). Conclusion: In this prospective biomarker study, it appeared that clinical HPV testing may not be as reliable as p16 immunohistochemistry. Patients with HPV16+ detected in their tumors appear to have a better prognosis than those with only HPVnon16+. Further follow-up is necessary, but these data may have implications in patient selection for future staging or deescalation strategies.