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Proceedings Paper

Towards automatic patient selection for chemotherapy in colorectal cancer trials
Author(s): Alexander Wright; Derek Magee; Philip Quirke; Darren E. Treanor
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Paper Abstract

A key factor in the prognosis of colorectal cancer, and its response to chemoradiotherapy, is the ratio of cancer cells to surrounding tissue (the so called tumour:stroma ratio). Currently tumour:stroma ratio is calculated manually, by examining H&E stained slides and counting the proportion of area of each. Virtual slides facilitate this analysis by allowing pathologists to annotate areas of tumour on a given digital slide image, and in-house developed stereometry tools mark random, systematic points on the slide, known as spots. These spots are examined and classified by the pathologist. Typical analyses require a pathologist to score at least 300 spots per tumour. This is a time consuming (10- 60 minutes per case) and laborious task for the pathologist and automating this process is highly desirable. Using an existing dataset of expert-classified spots from one colorectal cancer clinical trial, an automated tumour:stroma detection algorithm has been trained and validated. Each spot is extracted as an image patch, and then processed for feature extraction, identifying colour, texture, stain intensity and object characteristics. These features are used as training data for a random forest classification algorithm, and validated against unseen image patches. This process was repeated for multiple patch sizes. Over 82,000 such patches have been used, and results show an accuracy of 79%, depending on image patch size. A second study examining contextual requirements for pathologist scoring was conducted and indicates that further analysis of structures within each image patch is required in order to improve algorithm accuracy.

Paper Details

Date Published: 20 March 2014
PDF: 8 pages
Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410A (20 March 2014); doi: 10.1117/12.2043220
Show Author Affiliations
Alexander Wright, Univ. of Leeds (United Kingdom)
Derek Magee, Univ. of Leeds (United Kingdom)
Philip Quirke, Univ. of Leeds (United Kingdom)
Darren E. Treanor, Univ. of Leeds (United Kingdom)
Leeds Teaching Hospitals NHS Trust (United Kingdom)


Published in SPIE Proceedings Vol. 9041:
Medical Imaging 2014: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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