A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma.

ABSTRACT:

Importance

Recommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence.

Objective

To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with th

SUBMITTER: Jones GD 

PROVIDER: S-EPMC7758824 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature