Recent advances of pathomics in colorectal cancer diagnosis and prognosis

Abstract

Colorectal cancer (CRC) is one of the most common malignancies, with the third highest incidence and the second highest mortality in the world. To improve the therapeutic outcome, the risk stratification and prognosis predictions would help guide clinical treatment decisions. Achieving these goals have been facilitated by the fast development of artificial intelligence (AI) -based algorithms using radiological and pathological data, in combination with genomic information. Among them, features extracted from pathological images, termed pathomics, are able to reflect sub-visual characteristics linking to better stratification and prediction of therapeutic responses. In this paper, we review recent advances in pathological image-based algorithms in CRC, focusing on diagnosis of benign and malignant lesions, micro-satellite instability, as well as prediction of neoadjuvant chemoradiotherapy and the prognosis of CRC patients.

Keywords: artificial intelligence; colorectal cancer; deep learning; machine learning; pathomics.

Figure 1

The pathomics workflow. Firstly, after collecting and scanning pathological images, the ROI (region of interest) is manually or automatically labeled. Secondly, deep learning features (low-level, mid-level, and high-level features) and hand-crafted features (morphology, texture, statistics, and other features) are extracted from these images through a series of images pre-processing such as ROI segmentation, gridding, tile extraction, and color normalization. Finally, meaningful features are analyzed by machine learning or deep learning algorithms and classified or predicted according to different tasks.