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Review
. 2023 Apr 26;15(9):2491.
doi: 10.3390/cancers15092491.

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Nicolò Caldonazzi  1 , Paola Chiara Rizzo  1 , Albino Eccher  2 , Ilaria Girolami  3 , Giuseppe Nicolò Fanelli  4 , Antonio Giuseppe Naccarato  4 , Giuseppina Bonizzi  5 , Nicola Fusco  5   6 , Giulia d'Amati  7 , Aldo Scarpa  1 , Liron Pantanowitz  8 , Stefano Marletta  1   9
Affiliations
Review

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

Nicolò Caldonazzi et al. Cancers (Basel). .

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.

Keywords: artificial intelligence; digital pathology; lymph nodes; metastases.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Compared WSIs of frozen (a) and permanent sections (b,c) of a sentinel lymph node with a micrometastasis from breast carcinoma showing up only at deeper section examination of formalin-fixed paraffin-embedded material (c). Reproduced with permission from Girolami I et al. [3] (original magnification ×25 (a) and ×50 (b)).
Figure 2
Figure 2
Flow diagram of the screening and exclusion articles according to PRISMA’s guidelines [11].
See this image and copyright information in PMC

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