Abstract: Approximately 100,000 cases of Head and Neck Cancer (HNC) are diagnosed in the US annually. Patients are increasingly likely to survive, but often experience acute and long-term treatment-related side effects. Hence, great importance has been placed by clinicians on improving patient’s quality of life (QoL) and reducing symptom burden during treatment, whose cornerstone is radiation therapy. We introduce an interactive system which enables clinical and computational experts to visualize and assess symptom-related radiation oncology data, which could help clinicians create better treatment plans.
However, HNC patient cohort data are often large, multi-variate, and incomplete. Also, anatomical and dynamic temporal components influence the outcome of therapy, and the resulting patients’ QoL. We collected questionnaires from 157 patients treated for HNC at the MD Anderson Cancer Center, completed at multiple time points, regarding HNC symptoms. Each patient self-reported 28 symptoms on a 10-point scale ranging from “not present” to “as bad as you can imagine”. Demographic and diagnostic data were also gathered.
Approximately 100,000 cases of Head and Neck Cancer (HNC) are diagnosed in the US annually. Patients are increasingly likely to survive, but often experience acute and long term side effects [1]. Hence, great importance has been placed by clinicians on improving patient’s quality of life (QoL) and reducing symptom burden during treatment. We introduce an interactive system which enables clinical and computational experts to visualize and assess medical data. Using novel combinations of visual encodings, our system provides context for new patients based on patients with similar features and symptom evolution, which could help oncologists to create better treatment plans.
@article{floricelvisualizing,title={Visualizing Symptom Development During Head and Neck Cancer Treatment},author={Floricel, Carla and Wentzel, Andrew and Nipu, Md Nafiul and Kumar, Naveen and Canahuate, Guadalupe and Van Dijk, Lisanne and Fuller, C David and Marai, G Elisabeta},booktitle={IEEE Transactions on Visualization and Computer Graphics, Poster Session},year={2020},}
