Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer.

ABSTRACT:

Importance

Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging characteristics of both regions for predictions of p16+ OPSCC outcomes.

Objectives

To develop and validate a computed tomography (CT)-based deep learning classifier that integrates PT and LN features to predict outcomes in p16+ OPSCC and to identify patients with stage I disease who may derive added benefit associated with chemotherapy.

Design, setting, and participants

In this retrospective prognostic study, radiographic CT scans were analyzed of 811 patients with p16+ OPSCC treated with definitive radiotherapy or chemoradiotherapy from 3 indep

SUBMITTER: Song B 

PROVIDER: S-EPMC12046429 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature