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 419 participants, while there were 62 participants in the external validation cohort. The most accurate XGBoost model called SCHC model including age, platelet counts in platelet rich plasma samples (pPLT), plateletcrit in platelet rich plasma samples (pPCT), nodule size, and plateletcrit in whole blood samples (bPCT). In the development group, the SCHC model performed well in whole group and subgroups. Compared with VA, MC, BU model, the SCHC model had a significant improvement in reclassification as assessed by the NRI and IDI, and could bring the patients more benefits. For the external validation, the model performed not as well. The algorithm of SCHC, VA, MC, and BU model were first integrated using a web tool (http://i.uestc.edu.cn/SCHC). Conclusions: In this study, a platelet feature-based model could facilitate the discrimination of early-stage malignancy from benignancy patients, to ensure accurate diagnosis and optimal management. This research also indicated that common laboratory results also had the potential in diagnosing cancers.