使用日本对地观测卫星(advanced land observing satellite,ALOS)2008—2011年的影像数据,解译云南现状植被分布.使用分类回归树模型(classification and regression trees,CART)预测云南主要森林植被的潜在分布区,估算云南森林植被的...使用日本对地观测卫星(advanced land observing satellite,ALOS)2008—2011年的影像数据,解译云南现状植被分布.使用分类回归树模型(classification and regression trees,CART)预测云南主要森林植被的潜在分布区,估算云南森林植被的碳储量和固碳潜力.结果显示:云南省林地总面积是2.0×107hm2,森林覆盖率为52.49%,主要森林植被碳储量为871.14 Tg;不同森林植被碳储量及固碳潜力不同,碳储量较高的是季风常绿阔叶林、暖温性针叶林和暖热性针叶林,分别为205.42、172.72 Tg和137.78 Tg,而固碳潜力较大的是暖热性针叶林、暖温性针叶林和温凉性针叶林,分别为788.53、119.00 Tg和156.78 Tg,分别是现实碳储量的5.7倍、2.1倍和0.91倍;云南主要森林植被总固碳潜力为1 321.52 Tg,约为现实碳储量的1.52倍.总体上,云南省针叶林的固碳潜力远大于阔叶林,适当的人为干扰可提高暖热性针叶林的固碳潜力,暖热性针叶林具有较强的清洁发展机制(clean development mechanism,CDM)潜力,云南省主要森林植被在整体上是一个碳汇.展开更多
Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the genera...Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data.Methods: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees(CART) algorithm. Receiver operating characteristic(ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening.Results: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers(age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve(AUC) of the CART model was 0.88 [95%confidence interval(95% CI), 0.87-0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2%(95% CI, 58.1%-66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value(1.6%) than fecal immunochemical test(0.3%).Conclusions: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.展开更多
基金supported by funding from Beijing Municipal Science & Technology Commission, Clinical Application and Development of Capital Characteristic (No. Z161100000516003)National Natural Science Foundation of China (No. 31871266)
文摘Objective: Challenges remain in current practices of colorectal cancer(CRC) screening, such as low compliance,low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data.Methods: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees(CART) algorithm. Receiver operating characteristic(ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening.Results: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers(age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve(AUC) of the CART model was 0.88 [95%confidence interval(95% CI), 0.87-0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2%(95% CI, 58.1%-66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value(1.6%) than fecal immunochemical test(0.3%).Conclusions: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.
