Tissue Classification and Diagnosis of Colorectal Cancer Histopathology Images Using Deep Learning Algorithms. Is the Time Ripe for Clinical Practice Implementation?

Overview

Journal Prz Gastroenterol

Publisher Termedia

Specialty Gastroenterology

Date 2024 Apr 4

PMID 38572457

Authors

David Dimitris Chlorogiannis

Georgios-Ioannis Verras

Vasiliki Tzelepi

Anargyros Chlorogiannis

Anastasios Apostolos

Konstantinos Kotis

Christos-Nikolaos Anagnostopoulos

Andreas Antzoulas

Spyridon Davakis

Michail Vailas

Dimitrios Schizas

Francesk Mulita

Affiliations

Soon will be listed here.

Abstract

Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.

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