  Digital twin models are of high interest to Head and Neck Cancer (HNC)
oncologists, who have to navigate a series of complex treatment decisions that
weigh the efficacy of tumor control against toxicity and mortality risks.
Evaluating individual risk profiles necessitates a deeper understanding of the
interplay between different factors such as patient health, spatial tumor
location and spread, and risk of subsequent toxicities that can not be
adequately captured through simple heuristics. To support clinicians in better
understanding tradeoffs when deciding on treatment courses, we developed DITTO,
a digital-twin and visual computing system that allows clinicians to analyze
detailed risk profiles for each patient, and decide on a treatment plan. DITTO
relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver
personalized risk of both long-term and short-term disease outcome and toxicity
risk for HNC patients. Based on a participatory collaborative design alongside
oncologists, we also implement several visual explainability methods to promote
clinical trust and encourage healthy skepticism when using our system. We
evaluate the efficacy of DITTO through quantitative evaluation of performance
and case studies with qualitative feedback. Finally, we discuss design lessons
for developing clinical visual XAI applications for clinical end users.
