A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [¹⁸F]FDG-PET/CT parameters.

ABSTRACT:

Background

In patients with non-small cell lung cancer (NSCLC), accuracy of [¹⁸F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment.

Methods

Monocentric retrospective analysis of pretherapeutic [¹⁸F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1

SUBMITTER: Rogasch JMM 

PROVIDER: S-EPMC10199849 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature