A System for Xerostomia Risk Classification after Head & Neck Cancer Radiotherapy

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Summary Radiotherapy (RT) is a major component in the treatment of most head and neck cancer (HNC) cases. During irradiation, sensitive regions such as the salivary glands can sustain injury, resulting in xerostomia (dry mouth). This side effect is common and can significantly reduce quality of life during and post-treatment. The focus of this application is prediction during treatment planning of whether patients will suffer high-grade xerostomia (NCI CTCAE Grade 2-3) at the time of their first post-treatment follow-up visit, typically 3-6 months after RT (prevalence is approximately 40%). Predictions will enable clinicians to carry out treatment planning with improved knowledge of the likelihood of high-grade xerostomia development and allow better-informed and more timely anticipation of consequences such as eating difficulty. In this Phase 1 project, Oncospace Inc. will develop a Classification and Regression Tree (CART) prediction model using over 1200 complete HNC patient records. Associations between high-grade xerostomia and a wide range of dosimetric, clinical and demographic features will be automatically discovered and the features with the strongest associations will populate the nodes of a decision tree. The terminal leaf nodes will each contain the probability of high-grade xerostomia for the subset of patients in that node. In addition, leaf nodes will be assigned binary class labels designating a high- or low risk of high-grade xerostomia. This type of model provides transparency and interpretability, which are beneficial for clinical acceptance and for demonstration of safety to regulatory agencies. The software will be built using the Microsoft Azure cloud architecture and be deployed via a Software as a Service (SaaS) model. There are three distinct aims of this project: 1. Populate Oncospace Inc.’s Microsoft Azure CosmosDB database with data licensed from Johns Hopkins University, including steps such as patient de-identification, data curation, and additional dataset feature engineering 2. Perform CART modeling and test model accuracy, using separate training and test datasets and a variety of performance metrics, including sensitivity, specificity, AUC, and F1-score. 3. Design a clinically acceptable risk classification strategy and a user interface (UI) to communicate model results. Expert input from a team of UI consultants and three radiation oncologists will be an integral part of the development, testing, and evaluation processes. The successful completion of these aims will demonstrate the clinical and commercial feasibility of a xerostomia prediction model for HNC. Further development in Phase 2 will include deeper model personalization via incorporation of advanced image features (radiomics), as well as validation of model generalizability and commercial viability via the curation and use in model building of data from other institutions. Oncospace, formed in 2018, is uniquely positioned to carry out this work as the team includes the creators of the Pinnacle radiation therapy planning system, Tomotherapy radiation treatment delivery system, and HealthMyne Quantitative Imaging Decision Support platform. Oncospace has close clinical collaboration with Johns Hopkins University (JHU) for clinical feedback, validation and initial deployment. Oncospace has licensed three patents and subscription to complete patient treatment records for over 6,000 radiation oncology patients from JHU. The company has won the Microsoft Innovation Acceleration Award for its innovative platform to deliver AI-enabled healthcare solutions to the radiation oncology community. Page 1 of 1