# Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

## TL;DR

This paper introduces a novel visual computing method using a topology-based spatial similarity measure, T-SSIM, to improve radiation therapy planning by analyzing patient cohorts and enabling better prediction and understanding of treatment outcomes.

## Contribution

The paper presents T-SSIM, a new topology-based spatial similarity measure, and a visual interface for radiation therapy prediction based on patient cohort analysis.

## Key findings

- Quantitative results on 165 patients demonstrate the method's effectiveness.
- Qualitative evaluation with domain experts confirms usability and insights.
- The approach enhances understanding of spatial data in radiation therapy planning.

## 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.