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