A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data.

ABSTRACT: Objectives: As the pulmonary nodules were hard to be discriminated as benignancy or malignancy only based on imageology, a prospective and observational real-world research was devoted to develop and validate a predictive model for managing the diagnostic challenge. Methods: This study started in 2018, and a predictive model was constructed using eXtreme Gradient Boosting (XGBoost) based on computed tomographic, clinical, and platelet data of all the eligible patients. And the model was evaluated and compared with other common models using ROC curves, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and net benefit (NB). Subsequently, the model was validated in an external cohort. Results: The development group included 41

SUBMITTER: Zu R 

PROVIDER: S-EPMC9174863 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature