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Table 2 Comparison with published gene sets. Accuracy of all published gene set models on the independent data set both when predicting stage and platin resistance ranked by stage accuracy. Gene sets have been named after the first author of the publication followed by a description of its relationship to patient outcome. References have been used when the same first author had multiple publications

From: Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

Gene set first author Description Stage accuracy (%) Platin accuracy (%)
Ouellet low malignant potential/borderline disease vs. invasive disease: tumour tissue 97.96 55.56
Hibbs Disease vs. normal or other tissues 95.92 66.67*
Spentzos Residual disease vs. complete response at second look surgery 93.88 37.78
Lu Disease vs. normal 93.88 48.89
Helleman Platin sensitivity vs. platin resistance: differential expression 93.88 44.44
Ouellet low malignant potential/borderline disease vs. invasive disease: primary cultures 91.84 53.33
Zhu Clear cell vs. serous histology 91.84 66.67*
Lancaster [14] Short-term vs. long-term survival 91.84 44.44
Helleman Platin sensitivity vs. platin resistance: 16-gene predictive model 91.84 46.67
Berchuck Short-term vs. long-term survival 91.84 55.56
Schwartz Clear cell vs. other histological types 91.84 46.67
Hartmann Early vs. late relapse after platin based chemotherapy 91.84 66.67*
Spentzos Short-term vs. long-term survival 87.76 66.67*
Lancaster [14] Disease vs. normal 79.59 66.67*
Roberts Platin sensitivity vs. platin resistance 75.51 42.22
Lancaster [20] Ovarian cancer tissue vs. metastatic tissue: 27-gene predictive model 71.43 60.00#
Lancaster [20] Ovarian cancer tissue vs. metastatic tissue: differential expression 57.14 66.67*
  1. *models predicting only the majority class (platin sensitive patients) on the independent data set
  2. #best platin model