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目的建立用于诊断结直肠癌患者Dukes分期的分类树模型。方法用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术检测32例Dukes B期、24例Dukes C期和26例Dukes D期结直肠癌患者血清差异蛋白,用BioMaker Pattern软件在学习模式下建立用于判断结直肠癌患者Dukes分期的分类树模型,并用该模型在双盲模式下对随机选取的30例结直肠癌血清标本(Dukes B、C、D期各10例)进行检测以验证其诊断的准确性。结果在捕获的31个差异蛋白中,建立了以14个差异蛋白组成的分类树模型,该模型在学习模式下的诊断准确率为89．0%(73/ 82),在双盲模式下的诊断准确率为76．7%(23/30)。结论该分类树模型对判断结直肠癌患者Dukes分期有一定的诊断价值,可以为结直肠癌患者治疗方案的选择提供依据。
Objective To establish a classification tree model for the diagnosis of Dukes staging in patients with colorectal cancer. Methods Serum differential proteins from 32 patients with Dukes B, 24 Dukes C and 26 Dukes D patients with colorectal cancer were detected by surface enhanced laser desorption / ionization time of flight mass spectrometry (SELDI-TOF-MS). BioMaker Pattern software Model was established to determine the classification of colorectal cancer patients with Dukes stage classification tree model and the use of the model in double-blind mode of 30 randomly selected colorectal cancer serum samples (Dukes B, C, D of 10 cases) were detected To verify the accuracy of its diagnosis. Results Among the 31 differential proteins captured, a classification tree model with 14 differential proteins was established. The diagnostic accuracy of the model in learning mode was 89.0% (73/82). In the double-blind mode The diagnostic accuracy was 76.7% (23/30). Conclusion The classification tree model has certain diagnostic value in judging Dukes staging of colorectal cancer patients, which can provide evidence for the selection of treatment options for patients with colorectal cancer.