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. 2014 May 1;18(5):310–323. doi: 10.1089/omi.2013.0084

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Copyright 2014, Mary Ann Liebert, Inc.

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FIG. 6. — ROC curves of the 36 gene panel and MGMT methylation profiles. (a) The ROC curves are based on training dataset methylation profiles on 36 gene panel and MGMT. The area under the curve (AUC) of MGMT is 0.800 (95% CI, 0.644 to 0.909), and 36 gene panel is better at predicting patient outcomes than MGMT in AUC of 0.833 (95% CI, 0.682 to 0.932). (b) For the validation dataset, the performance of 36 gene panel methylation profiles was assessed AUC equal to 0.877 (95% CI, 0.735 to 0.969) to be much better at predicting patient outcomes than that of MGMT 0.565 (95% CI, 0.388 to 0.731). (c) When the 36 gene panel was adjusted for gender (AUC=0.887, 95%CI, 0.746 to 0.965), it still has better performance at predicting patient outcomes than the adjusted MGMT profile (AUC=0.756, 95%CI, 0.595 to 0.878). (d) After adjusting for age, the 36 gene panel was assessed AUC equal to 0.947 based on the ROC curve training dataset (95% CI, 0.932 to 1.000) which was better than the AUC for MGMT (AUC=0.936, 95% CI, 0.811 to 0.989).

FIG. 6. — ROC curves of the 36 gene panel and MGMT methylation profiles. (a) The ROC curves are based on training dataset methylation profiles on 36 gene panel and MGMT. The area under the curve (AUC) of MGMT is 0.800 (95% CI, 0.644 to 0.909), and 36 gene panel is better at predicting patient outcomes than MGMT in AUC of 0.833 (95% CI, 0.682 to 0.932). (b) For the validation dataset, the performance of 36 gene panel methylation profiles was assessed AUC equal to 0.877 (95% CI, 0.735 to 0.969) to be much better at predicting patient outcomes than that of MGMT 0.565 (95% CI, 0.388 to 0.731). (c) When the 36 gene panel was adjusted for gender (AUC=0.887, 95%CI, 0.746 to 0.965), it still has better performance at predicting patient outcomes than the adjusted MGMT profile (AUC=0.756, 95%CI, 0.595 to 0.878). (d) After adjusting for age, the 36 gene panel was assessed AUC equal to 0.947 based on the ROC curve training dataset (95% CI, 0.932 to 1.000) which was better than the AUC for MGMT (AUC=0.936, 95% CI, 0.811 to 0.989).