Additional file 27: of A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk Sergey Klimov Islam Miligy Arkadiusz Gertych Yi Jiang Michael Toss Padmashree Rida Ian Ellis Andrew Green Uma Krishnamurti Emad Rakha Ritu Aneja 10.6084/m9.figshare.9162806.v1 https://springernature.figshare.com/articles/journal_contribution/Additional_file_27_of_A_whole_slide_image-based_machine_learning_approach_to_predict_ductal_carcinoma_in_situ_DCIS_recurrence_risk/9162806 Supplementary Figure S17. Mean values for the continuous metrics obtained when using A) the class probability, or proportion of recurrence voting trees, using the original random forest model and B) the output of a random survival forest trained with the 8 selected features. The astrix (*) represents groups with significant (p <0.05) differences in averages. (PDF 233 kb) 2019-07-30 04:17:31 DCIS Digital image analysis Prognosis Machine learning Recurrence prediction Biomarker