Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma.

ABSTRACT:

Importance

Given that early-stage oral cavity squamous cell carcinoma (OCSCC) has a high propensity for subclinical nodal metastasis, elective neck dissection has become standard practice for many patients with clinically negative nodes. Unfortunately, for most patients without regional metastasis, this risk-averse treatment paradigm results in unnecessary morbidity.

Objectives

To develop and validate predictive models of occult nodal metastasis from clinicopathological variables that were available after surgical extirpation of the primary tumor and to compare predictive performance against depth of invasion (DOI), the currently accepted standard.

Design, setting, and participants

This diagnostic modeling study collected clinicopathological variables retrospectively

SUBMITTER: Farrokhian N 

PROVIDER: S-EPMC9008495 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature