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Table 2 Classification accuracy metrics for each of the four experiments. From left to right: Low ODx Low mBR vs. High ODx Low mBR, Low ODx vs. High ODx, Low ODx vs. Intermediate and High ODx, and Low and Intermediate ODx vs. High ODx. Data for each experiment includes the AUC, best patch Voting Accuracy results, and the optimal feature ranking and classifier used to achieve the optimized patch voting accuracy results. All experiments conducted with 3-fold cross-validation

From: Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer

Experiment LL vs. HH L vs. H L vs. Int. and H L and Int. vs. H
Number of Patients 37 75 125 111
AUC 0.81 0.69 0.58 0.6
AUC STDev 0.08 0.05 0.03 0.06
Patch Voting Accuracy 82% 80% 60% 86%
Best Feat. Ranking for Patch voting MRMR-MID PCA-VIP Ranksum MRMR-MID
Best Classifier for Patch voting LDA Random Forest Random Forest Random Forest