Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review

Abstract

Background: Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.

Objective: To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.

Methods: A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.

Results: A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.

Conclusions: Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.

Keywords: Artificial intelligence; Immunity; Lymph node; Review; Segmentation.

Graphical abstract

Fig. 1

Schematic representation of immunologically stimulated lymph node with enlisted compartment-specific reactive patterns.

Fig. 2

Exemplary images taken from H&E stained lymph node from esophageal cancer patients. The figure illustrates the visually perceivable differences between immunologically stimulated (also called reactive) LNs. Figure A shows a lymph node with a large number of germinal centers (some are highlighted in asterisk), indicative of follicular hyperplasia; Figure B contains a metastatic lymph node with hyperplasia of paracortex and a lack of germinal centers.

Fig. 3

PRISMA flowchart illustrating the article screening and selection process implemented in this systematic search.

Fig. 4

Graphs illustrating the usage patterns of the datasets and models in selected studies. A: piechart with dataset distribution across the selected papers, B: piechart illustrating the model selection in the included articles. The total number of models is larger than the number of studies, as some studies trained more than one model. C: chronological chart of dataset usage focusing on inhouse/public dataset selection. The barchart illustrates the steady increase of studies using inhouse datasets.