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

David Dimitris Chlorogiannis; Georgios-Ioannis Verras; Vasiliki Tzelepi; Anargyros Chlorogiannis; Anastasios Apostolos; Konstantinos Kotis; Christos-Nikolaos Anagnostopoulos; Andreas Antzoulas; Michail Vailas; Dimitrios Schizas; Francesk Mulita

doi:10.5114/pg.2023.130337

eISSN: 1897-4317
ISSN: 1895-5770

Gastroenterology Review/Przegląd Gastroenterologiczny

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4/2023
vol. 18

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Artykuł przeglądowy

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

Department of D/I Radiology, Patras General Hospital, Patras, Greece
Department of Surgery, General University Hospital of Patras, Patras, Greece
Department of Pathology, School of Medicine, University of Patras, Patras, Greece
Karolinska Institutet, Stockholm, Sweden
First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
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

DOI: https://doi.org/10.5114/pg.2023.130337

Data publikacji online: 2023/08/07

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AMA

Chlorogiannis D, Verras G, Tzelepi V, et al. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?. Gastroenterology Review/Przegląd Gastroenterologiczny. 2023;18(4):353-367. doi:10.5114/pg.2023.130337.

APA

Chlorogiannis, D., Verras, G., Tzelepi, V., Chlorogiannis, A., Apostolos, A.,  & Kotis, K. et al. (2023). Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?. Gastroenterology Review/Przegląd Gastroenterologiczny, 18(4), 353-367. https://doi.org/10.5114/pg.2023.130337

Chicago

Chlorogiannis, David Dimitris, Georgios-Ioannis Verras, Vasiliki Tzelepi, Anargyros Chlorogiannis, Anastasios Apostolos, Konstantinos Kotis,  and Christos-Nikolaos Anagnostopoulos et al. 2023. "Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?". Gastroenterology Review/Przegląd Gastroenterologiczny 18 (4): 353-367. doi:10.5114/pg.2023.130337.

Harvard

Chlorogiannis, D., Verras, G., Tzelepi, V., Chlorogiannis, A., Apostolos, A., Kotis, K., Anagnostopoulos, C., Antzoulas, A., Vailas, M., Schizas, D.,  and Mulita, F. (2023). Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?. Gastroenterology Review/Przegląd Gastroenterologiczny, 18(4), pp.353-367. https://doi.org/10.5114/pg.2023.130337

MLA

Chlorogiannis, David Dimitris et al. "Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?." Gastroenterology Review/Przegląd Gastroenterologiczny, vol. 18, no. 4, 2023, pp. 353-367. doi:10.5114/pg.2023.130337.

Vancouver

Chlorogiannis D, Verras G, Tzelepi V, Chlorogiannis A, Apostolos A, Kotis K et al. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?. Gastroenterology Review/Przegląd Gastroenterologiczny. 2023;18(4):353-367. doi:10.5114/pg.2023.130337.

Metryki PlumX:

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.

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