The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients.

ABSTRACT:

Background and purpose

Deep learning (DL) models can extract prognostic image features from pre-treatment PET/CT scans. The study objective was to explore the potential benefits of incorporating pathologic lymph node (PL) spatial information in addition to that of the primary tumor (PT) in DL-based models for predicting local control (LC), regional control (RC), distant-metastasis-free survival (DMFS), and overall survival (OS) in oropharyngeal cancer (OPC) patients.

Materials and methods

The study included 409 OPC patients treated with definitive (chemo)radiotherapy between 2010 and 2022. Patient data, including PET/CT scans, manually contoured PT (GTVp) and PL (GTVln) structures, clinical variables, and endpoints, were collected. Firstly, a DL-based method was employed to

SUBMITTER: Ma B 

PROVIDER: S-EPMC11880716 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature