eISSN: 1897-4317
ISSN: 1895-5770
Gastroenterology Review/Przegląd Gastroenterologiczny
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4/2023
vol. 18
 
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abstract:
Review paper

Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?

David Dimitris Chlorogiannis
1
,
Georgios-Ioannis Verras
2
,
Vasiliki Tzelepi
3
,
Anargyros Chlorogiannis
4
,
Anastasios Apostolos
5
,
Konstantinos Kotis
6
,
Christos-Nikolaos Anagnostopoulos
6
,
Andreas Antzoulas
2
,
Michail Vailas
7
,
Dimitrios Schizas
7
,
Francesk Mulita
2

1.
Department of D/I Radiology, Patras General Hospital, Patras, Greece
2.
Department of Surgery, General University Hospital of Patras, Patras, Greece
3.
Department of Pathology, School of Medicine, University of Patras, Patras, Greece
4.
Karolinska Institutet, Stockholm, Sweden
5.
First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
6.
Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
7.
Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
Gastroenterology Rev 2023; 18 (4): 353–367
Online publish date: 2023/08/07
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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 in silico 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.
keywords:

colorectal cancer, artificial intelligence, deep learning algorithms, surgical practice

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