Extracapsular extension identification for head and neck cancer using multi-scale 3D deep neural network

Abstract

Extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with head and neck squamous cell carcinoma (HNSCC). ECE occurs when metastatic tumor cells within the lymph node break through the nodal capsule into surrounding tissues. It is crucial to identify the occurrence of ECE as it changes staging and management for the patients. Current clinical ECE detection relying on radiologists' visual identification is extremely labor-intensive, time-consuming, and error-prone, and consequently, pathologic confirmation is required. Therefore, we aim to perform ECE identification automatically introducing a novel 3D deep neural network (DNN) with multi-scale input to analyze the presence or absence of ECE and correlate that with gold standard histopathological findings. Both local and global features are extracted. The experimental tests show that our proposed model is capable for ECE classification. The test results are enhanced with performance visualization.