Machine learning based on clinico-biological features integrated ¹⁸F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung.

ABSTRACT:

Purpose

To develop and validate a clinico-biological features and ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC).

Methods

A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regress

SUBMITTER: Ren C 

PROVIDER: S-EPMC8113203 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature