ArticleIEEE transactions on visualization and computer graphics2020
Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration.
Article in IEEE transactions on visualization and computer graphics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator.... IEEE Workshop on Visual Analytics in Healthcare. IEEE Workshop on Visual Analytics in Healthcare · 2025Article
- FlavorCharter: Development of a Smartphone App for Quantifying Food Flavor.Proceedings ... ICDM workshops. IEEE International Conference on Data Mining · 2025Article
- PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients.IEEE journal of biomedical and health informatics · 2025Article
- DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.IEEE transactions on visualization and computer graphics · 2025Article
- Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining.IEEE transactions on visualization and computer graphics · 2024Article
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- DASS Good: Explainable Data Mining of Spatial Cohort Data.Computer graphics forum : journal of the European Association for Computer Graphics · 2023Article
- Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool.Frontiers in oncology · 2023Article
- Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad.Journal of medical Internet research · 2022Article
- A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium · 2022Article
- THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.IEEE transactions on visualization and computer graphics · 2022Article
- Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021Article
- Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features.Scientific reports · 2021Article
- Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes.Proceedings. International Database Engineering and Applications Symposium · 2021Article
- Identifying Symptom Clusters Through Association Rule Mining.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021Article
- Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2020Article
- Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Article
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Authors and funding
9 authors.
Funding
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.
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