Ruiling Zu1, Sisi Yu2, Guishu Yang3, Yiman Ge4, Dongsheng Wang1, Li Zhang1, Xiaoyu Song1, Yao Deng1, Qiao He1, Kaijiong Zhang1, Jian Huang5, Huaichao Luo6. 1. Department of clinical laboratory, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. 2. Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. 3. Department of clinical laboratory, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; College of Clinical Medicine, Southwest Medical University, Luzhou, Sichuan, China. 4. Department of clinical laboratory, Chengdu University of Traditional Chinese Medicine Affiliated Hospital, Chengdu, Sichuan, China. 5. Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address: hj@uestc.edu.cn. 6. Department of clinical laboratory, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address: luo1987cc@163.com.
Abstract
OBJECTIVES: To determine whether the integration platelet features in blood and platelet rich plasma can establish a model to diagnose lung cancer and colon cancer, even differentiate lung malignancy from lung benign diseases. METHODS:245 individuals including 159 lung cancer and 86 normal participants were divided into the training cohort and testing cohort randomly. Then, 32 colon cancers, 37 lung cancers, and 21 benign patients were enrolled into validate cohort. The whole blood and corresponding platelet rich plasma (PRP) samples from all participants were prospectively collected, and the platelet features were determined. The features which are statistically significant at the univariate analysis in the training cohort and reported significant features were entered the diagnostic model. A receiver operator characteristic (ROC) curve was drawn to evaluate the accuracy of the model in each cohort. RESULTS: In the training cohort, multiple platelet features were significantly different in lung cancer patients, including MPV in whole blood, MPV, and platelet count in PRP and platelet recovery rate (PRR). For the training cohort, the diagnostic model for lung cancer performed well (AUC = 0.92). The probability distribution of lung cancers and controls in testing cohort were also separated well by the diagnostic model (AUC = 0.79). The diagnostic model for colon cancer also performed well (AUC = 0.79). The model also has a potential value in differentiating the lung malignancy from the benign (AUC = 0.69). CONCLUSION: The PRR was first raised and used in the detection of lung cancer. This study identified a diagnostic model based on PRR and other platelet features in whole blood and PRP samples with the potential to distinguish patients with lung cancer or colon cancer from healthy controls. The model could also be used to distinguish between lung cancer from the benign disease.
RCT Entities:
OBJECTIVES: To determine whether the integration platelet features in blood and platelet rich plasma can establish a model to diagnose lung cancer and colon cancer, even differentiate lung malignancy from lung benign diseases. METHODS: 245 individuals including 159 lung cancer and 86 normal participants were divided into the training cohort and testing cohort randomly. Then, 32 colon cancers, 37 lung cancers, and 21 benign patients were enrolled into validate cohort. The whole blood and corresponding platelet rich plasma (PRP) samples from all participants were prospectively collected, and the platelet features were determined. The features which are statistically significant at the univariate analysis in the training cohort and reported significant features were entered the diagnostic model. A receiver operator characteristic (ROC) curve was drawn to evaluate the accuracy of the model in each cohort. RESULTS: In the training cohort, multiple platelet features were significantly different in lung cancerpatients, including MPV in whole blood, MPV, and platelet count in PRP and platelet recovery rate (PRR). For the training cohort, the diagnostic model for lung cancer performed well (AUC = 0.92). The probability distribution of lung cancers and controls in testing cohort were also separated well by the diagnostic model (AUC = 0.79). The diagnostic model for colon cancer also performed well (AUC = 0.79). The model also has a potential value in differentiating the lung malignancy from the benign (AUC = 0.69). CONCLUSION: The PRR was first raised and used in the detection of lung cancer. This study identified a diagnostic model based on PRR and other platelet features in whole blood and PRP samples with the potential to distinguish patients with lung cancer or colon cancer from healthy controls. The model could also be used to distinguish between lung cancer from the benign disease.