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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Value of Artificial Intelligence in Evaluating Lymph Node Metastases.

Nicolò Caldonazzi¹, Paola Chiara Rizzo¹, Albino Eccher²

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

|May 13, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) shows promise for automatically detecting lymph node (LN) metastases in digital pathology whole slide images (WSIs). This AI application could enhance accuracy and efficiency in cancer staging, aiding daily pathology practice.

Keywords:

artificial intelligence digital pathology lymph nodes metastases

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Area of Science:

Digital pathology
Artificial intelligence in oncology
Cancer metastasis research

Background:

Lymph node (LN) metastasis is a critical prognostic factor in cancer staging.
Manual evaluation of lymph nodes for metastases is time-consuming and prone to errors.
Digital pathology and AI offer potential for automated analysis of whole slide images (WSIs).

Purpose of the Study:

To systematically review the literature on AI implementation for detecting metastases in lymph node WSIs.
To assess the current state and potential of AI tools in analyzing lymph node status.
To evaluate the accuracy and applicability of AI in identifying metastatic tissue in LNs.

Main Methods:

Systematic literature search conducted in PubMed and Embase databases.
Inclusion of studies applying AI techniques for automatic analysis of lymph node status.
Categorization of relevant articles based on AI accuracy in LN evaluation.

Main Results:

A total of 23 studies were included from 4584 retrieved articles.
Published data indicate that AI is a promising tool for detecting LN metastases.
AI demonstrates proficiency for integration into daily pathology practice.

Conclusions:

AI application in detecting lymph node metastases is highly promising.
AI tools can significantly improve the efficiency and accuracy of cancer staging.
The findings support the proficient employment of AI in routine pathology workflows.