Determining the grade of colon cancer from tissue slides is a routine part of the pathological analysis. In the case of colorectal adenocarcinoma (CRA), grading is partly determined by morphology and degree of formation of glandular structures. Achieving consistency between pathologists is difficult due to the subjective nature of grading assessment. An objective grading using computer algorithms will be more consistent, and will be able to analyse images in more detail. In this paper, we measure the shape of glands with a novel metric that we call the Best Alignment Metric (BAM). We show a strong correlation between a novel measure of glandular shape and grade of the tumour. We used shape specific parameters to perform a two-class classification of images into normal or cancerous tissue and a threeclass classification into normal, low grade cancer, and high grade cancer. The task of detecting gland boundaries, which is a prerequisite of shape-based analysis, was carried out using a deep convolutional neural network designed for segmentation of glandular structures. A support vector machine (SVM) classifier was trained using shape features derived from BAM. Through cross-validation, we achieved an accuracy of 97% for the two-class and 91% for three-class classification. Colorectal cancer is one of the most common cancers and the fourth most common cause of cancer related deaths 1 . In 2012, it accounted for approximately 10% of the cancer cases recorded worldwide and was the third and second most common cancer in men and women respectively 1 . Adenocarcinomas originate from epithelial cells 2 and account for over 90% of colorectal tumours. Determining the grade of cancer from colorectal tissue slides is a routine part of the pathological analysis and is one of the potentially useful parameters for deciding the treatment plan. Grading is currently carried out on the basis of the degree of glandular differentiation/formation which a tumour shows. For example well-differentiated tumours are predominantly glandular (see Fig. 1(d) and (e)) while in poorly differentiated tumours, the epithelial cells forming the gland boundary will diffuse irregularly, making it challenging to locate the boundary of individual glands (see Fig. 1(f) and (g) ). The structure of a normal gland is shown in Fig. 1(c) . Glands, also termed tubules or crypts in the literature, are three-dimensional testtube-like structures. Normal glands occur in a well-organised fashion, whose appearance, after slicing through the tube to obtain a tissue section, is elliptical, or possibly circular, depending on the angle that the section makes to the tube. High grade adenocarcinomas, however, show large variations in the degree of gland formation as well as in the morphology of these glands. Achieving consistency between pathologists is difficult due to the subjective nature of grading assessment and the fact that many tumours show varying patterns of differentiation. In addition to inter-and intra-observer differences, this process is time-consuming for pathologists. Despite this variability, tumour grade has been shown to have clinical and prognostic significance 2-5 . Currently, there is no accepted standard grading system. However, most of the systems stratify tumours into four grades (or combinations of them): grade 1 (well differentiated), grade 2 (moderately differentiated), grade 3 (poorly differentiated) and grade 4 (undifferentiated). In most of the studies focusing on the prognostic importance of grading, several grades have been merged to form a two-tiered grading system as follows: the combination of well and moderately differentiated (grades 1 and 2) is defined as
In Awan et al., a DCNN was used to mark gland boundaries and based on the glandular shape, a two-class and three-class classification model for colorectal adenocarcinoma using histology image was designed REF .
Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images
Scientific Reports
Scientific Reports
Computer Science Medicine