10.6084/m9.figshare.9162686.v1
Sergey Klimov
Sergey
Klimov
Islam Miligy
Islam
Miligy
Arkadiusz Gertych
Arkadiusz
Gertych
Yi Jiang
Yi
Jiang
Michael Toss
Michael
Toss
Padmashree Rida
Padmashree
Rida
Ian Ellis
Ian
Ellis
Andrew Green
Andrew
Green
Uma Krishnamurti
Uma
Krishnamurti
Emad Rakha
Emad
Rakha
Ritu Aneja
Ritu
Aneja
Additional file 10: of A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk
Springer Nature
2019
DCIS
Digital image analysis
Prognosis
Machine learning
Recurrence prediction
Biomarker
2019-07-30 04:16:59
Journal contribution
https://springernature.figshare.com/articles/journal_contribution/Additional_file_10_of_A_whole_slide_image-based_machine_learning_approach_to_predict_ductal_carcinoma_in_situ_DCIS_recurrence_risk/9162686
Supplementary Figure S5. Schematic of the logic used to translate risk category of patient slides to patient risk. Patients who possessed multiple resection slides were put into a high-risk subgroup if any of their slides were classified as high-risk by the recurrence classifier. (PDF 328 kb)