Value of Artificial Intelligence in Evaluating Lymph Node Metastases-Reference-Cited by-同舟云学术

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Published:2023-04-26 Issue:9 Volume:15 Page:2491
ISSN:2072-6694
Container-title:Cancers
language:en
Short-container-title:Cancers

Author:

Caldonazzi Nicolò¹,Rizzo Paola Chiara¹,Eccher Albino²^ORCID,Girolami Ilaria³^ORCID,Fanelli Giuseppe Nicolò⁴^ORCID,Naccarato Antonio Giuseppe⁴^ORCID,Bonizzi Giuseppina⁵,Fusco Nicola⁵⁶^ORCID,d’Amati Giulia⁷^ORCID,Scarpa Aldo¹^ORCID,Pantanowitz Liron⁸,Marletta Stefano¹⁹^ORCID

Affiliation:

1. Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy

2. Department of Pathology and Diagnostics, University and Hospital Trust of Verona, 37126 Verona, Italy

3. Department of Pathology, Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Provincial Hospital of Bolzano (SABES-ASDAA), 39100 Bolzano-Bozen, Italy

4. Division of Pathology, Department of Translational Research, New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy

5. Division of Pathology, IEO, Europefan Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy

6. Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy

7. Department of Radiology, Oncology and Pathology, Sapienza, University of Rome, 00185 Rome, Italy

8. Department of Pathology, University of Michigan, Ann Arbor, MI 48104, USA

9. Department of Pathology, Pederzoli Hospital, 37019 Peschiera del Garda, Italy

Abstract

One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.

Funder

European Union—NextGenerationEU through the Italian Ministry of University and Research

Publisher

MDPI AG

Subject

Cancer Research,Oncology

Link

https://www.mdpi.com/2072-6694/15/9/2491/pdf

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