PRO-based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients

Eric A. Anyimadu, Yaohua Wang, Carla Floricel, Serageldin Kamel, Amy Catherine Moreno, Clifton D Fuller, Xinhua Zhang, G Marai, Guadalupe M. Canahuate

Keywords: Patient Reported Outcomes, Deep Learning, Patient Clustering, Regression, Survival Analysis, Xerostomia

TL;DR: Using PRO-based Stratification to Enhance Toxicity and Survival Predictions in Head and Neck Cancer Patients.

Abstract: Patient-Reported Outcomes (PRO) consist of information provided directly by the patients about their health status including symptom ratings. PROs are commonly used in clinical practice to support clinical decision-making and have recently been incorporated into machine learning models to improve risk prediction. In this work, we aim to evaluate whether the inclusion of a patient stratification based on 12-month post-treatment predicted Patient Reported Outcomes improves risk prediction of radiation-induced toxicity and overall survival for head and neck cancer patients. A bidirectional long-short term memory (Bi-LSTM) recurrent neural network was used to model the longitudinal PRO data and to predict symptom ratings 12 months post-treatment. Patients were stratified using hierarchical clustering over the LSTM-predicted data. A logistic regression model was trained to predict Xerostomia at 12 months and a Cox regression model to predict overall survival. Results show that the inclusion of symptom burden clusters derived from the predicted Patient Reported Outcomes improves radiation-induced toxicity and overall survival prediction for head and neck cancer patients.

Track: 11. General Track

Registration Id: 6DNM2GF6R4S

Submission Number: 190