Leveraging hybrid biomarkers in clinical endpoint prediction.

ABSTRACT:

Background

Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60 and 80%. An accurate prognosis assessment is required for making effective clinical decisions.

Methods

We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component an

SUBMITTER: Saad M 

PROVIDER: S-EPMC7538849 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature