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

arXiv: https://arxiv.org/abs/1907.05919 · HTML: https://mlsanity.com/paper/1907.05919/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy](https://mlsanity.com/paper/2108.02817/) (2021)
- [DASS Good: Explainable Data Mining of Spatial Cohort Data](https://mlsanity.com/paper/2304.04870/) (2023)

## Abstract (arXiv)

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.