ESTRO 2023 - Abstract Book

S308

Sunday 14 May 2023

ESTRO 2023

Conclusion The dual-pathway 3D-Resnet, 18-10 layers, can help HNSCC pre-treatment staging. Further external validation is needed to evaluate the model robustness. This work was supported in part by the National Science and Technology Council, Taiwan under Grant of NSTC 111-2634-F- 006-012 PD-0406 Unsupervised leaning of biometric features predicts metastatic head and neck cancer progression R.Z. Ye 1,3 , H. Bahig 2 , P. Wong 1 1 Princess Margaret Cancer Centre, Department of Radiation Medicine Program, Toronto, Canada; 2 Centre Hospitalier de l’Université de Montréal, Department of Radiation Oncology, Montréal, Canada; 3 Université de Sherbrooke, Division of Endocrinology, Department of Medicine, Sherbrooke, Canada Purpose or Objective The COVID pandemic accelerated the integration of virtual care and patient monitoring in cancer. In the age of big data, alternative and continuous patient monitoring holds tremendous promise. Within the context of a Phase I/II trial evaluating the combination of Durvalumab, Tremelimumab and SBRT for oligometastatic head and neck cancers (NCT03283605), we explore the role of patient biometry to detect alterations in circadian rhythm and their association with patient disease state and prognosis. Materials and Methods The first 26 (22 males and 4 females) subjects who accepted to wear a Fitbit Alta HR during the clinical trial were analysed. Patients were imaged and measured using RECIST 1.1 criteria every 3 months. For each hour, we used minute-to-minute calories, metabolic equivalent of task (MET), activity intensity, number of steps, and heart rate to compute the mean, minimum, 10th to 90th percentile, maximum, as well as the 1st, 2nd, and 3rd time-derivative of these variables. Time in hour was encoded as vectors (T_h):