A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis

Neofytos Dimitriou, Ognjen Arandelovic, David James Harrison, Peter David Caie

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Abstract

Accurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage IIcolorectal cancer patients using the current TNM guidelines. The aim of the present work is to address this problem through the use of machine learning. In particular, we introduce a novel, data driven framework which makes use of a large number of diverse types of features, readily collected from immunofluorescence imagery. Its outstanding performance in predictingmortality in stage II patients (AUROC= 0:94), exceeds that of current clinical guidelines such as pT stage (AUROC= 0:65), and is demonstrated on a cohort of 173 colorectal cancer patients.
Original languageEnglish
Article number52
Number of pages9
Journalnpj Digital Medicine
Volume1
DOIs
Publication statusPublished - 2 Oct 2018

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