Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model.

ABSTRACT:

Background

Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis.

Methods

There were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of su

SUBMITTER: Huang TT 

PROVIDER: S-EPMC10496246 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature