Development of diagnostic model of lung cancer based on multiple tumor markers and data mining

作者：Wang, Zhaoxian; Feng, Feifei; Zhou, Xiaoshan; Duan, Liju; Wang, Jing; Wu, Yongjun^*; Wang, Na^*

来源：Oncotarget, 2017, 8(55): 94793-94804.

DOI：10.18632/oncotarget.21935

• lung cancer
• decision tree
• ANN
• diagnostic model
• tumor marker

Objective: To develop early intelligent discriminative model of lung cancer and evaluate the efficiency of diagnosis value. @@@ Methods: Based on the genetic polymorphism profile of CYP1A1-rs1048943, GSTM1, mEH-rs1051740, XRCC1-rs1799782 and XRCC1-rs25489 and the methylations of p16 and RASSF1A gene, and the length of telomere in the peripheral blood from 200 lung cancer patients and 200 health persons, the discriminative model was established through decision tree and ANN technique. @@@ Results: ACU of the discriminative model based on multiple tumour markers increased by about 10%; The accuracy rate of decision tree model and ANN model for testing set were 93.00% and 89.62% respectively. The ROC analysis showed the decision tree model's AUC is 0.929 (0.894 similar to 0.964), the ANN model's AUC is 0.894 (0.853 similar to 0.935). However, the classify accuracy rate and AUC of Fisher discriminatory analysis model are all about 0.7. @@@ Conclusion: The early intelligent discriminative model of lung cancer based on multiple tumor markers and data mining techniques has a higher accuracy rate and might be useful for early diagnosis of lung cancer.

• 出版日期2017-11-7
• 单位郑州大学