  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.
