Value of Artificial Intelligence in Evaluating Lymph Node Metastases.

artificial intelligence digital pathology lymph nodes metastases

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
26 Apr 2023
Historique:
received: 30 03 2023
revised: 23 04 2023
accepted: 25 04 2023
medline: 13 5 2023
pubmed: 13 5 2023
entrez: 13 5 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37173958
pii: cancers15092491
doi: 10.3390/cancers15092491
pmc: PMC10177013
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Subventions

Organisme : European Union - NextGenerationEU through the Italian Ministry of University and Research under PNRR - M4C2-I1.3 Project PE_00000019 "HEAL ITALIA"
ID : B33C22001030006

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Auteurs

Nicolò Caldonazzi (N)

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

Paola Chiara Rizzo (PC)

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

Albino Eccher (A)

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

Ilaria Girolami (I)

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

Giuseppe Nicolò Fanelli (GN)

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

Antonio Giuseppe Naccarato (AG)

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

Giuseppina Bonizzi (G)

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

Nicola Fusco (N)

Division of Pathology, IEO, Europefan Institute of Oncology IRCCS, University of Milan, 20122 Milan, Italy.
Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy.

Giulia d'Amati (G)

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

Aldo Scarpa (A)

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

Liron Pantanowitz (L)

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

Stefano Marletta (S)

Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy.
Department of Pathology, Pederzoli Hospital, 37019 Peschiera del Garda, Italy.

Classifications MeSH