Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma.

ABSTRACT:

Background

We aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC).

Methods

A total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASS

SUBMITTER: Wu X 

PROVIDER: S-EPMC8421978 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature