Clinical utility of artificial intelligence assistance in histopathologic review of lymph node metastasis for gastric adenocarcinoma

Evaluation of a Deep Learning Model for Metastatic Squamous Cell Carcinoma Prediction From Whole Slide Images

CONTEXT.—

Squamous cell carcinoma (SCC) is a histologic type of cancer that exhibits various degrees of keratinization. Identifying lymph node metastasis in SCC is crucial for prognosis and treatment strategies. Although artificial intelligence (AI) has shown promise in cancer prediction, applications specifically targeting SCC are limited.

OBJECTIVE.—

To design and validate a deep learning model tailored to predict metastatic SCC in radical lymph node dissection specimens using whole slide images (WSIs).

DESIGN.—

Using the EfficientNetB1 architecture, a model was trained on 6587 WSIs (2413 SCC and 4174 nonneoplastic) from several hospitals, encompassing esophagus, head and neck, lung, and skin specimens. The training exclusively relied on WSI-level labels without annotations. We evaluated the model on a test set consisting of 541 WSIs (41 SCC and 500 nonneoplastic) of radical lymph node dissection specimens.

RESULTS.—

The model exhibited high performance, with receiver operating characteristic curve areas under the curve between 0.880 and 0.987 in detecting SCC metastases in lymph nodes. Although true positives and negatives were accurately identified, certain limitations were observed. These included false positives due to germinal centers, dust cell aggregations, and specimen-handling artifacts, as well as false negatives due to poor differentiation.

CONCLUSIONS.—

The developed artificial intelligence model presents significant potential in enhancing SCC lymph node detection, offering workload reduction for pathologists and increasing diagnostic efficiency. Continuous refinement is needed to overcome existing challenges, making the model more robust and clinically relevant.

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

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.

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.

Applications of artificial intelligence in digital pathology for gastric cancer

Gastric cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a positive outcome. The integration of artificial intelligence in the pathology field is increasingly widespread, including histopathological images analysis. In recent years, the application of digital pathology technology emerged as a potential solution to enhance the understanding and management of gastric cancer. Through sophisticated image analysis algorithms, artificial intelligence technologies facilitate the accuracy and sensitivity of gastric cancer diagnosis and treatment and personalized therapeutic strategies. This review aims to evaluate the current landscape and future potential of artificial intelligence in transforming gastric cancer pathology, so as to provide ideas for future research.

Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators

Background

Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improve individualized treatment. The specific efficacy of several lymph node indicators in predicting distant metastasis (DM) and patient prognosis in MDGA remains obscure.

Methods

We collected data from MDGA patients from the SEER database from 2010 to 2019. Additionally, we collected data from MDGA patients in China. We used nine machine learning algorithms to predict DM. Subsequently, we used Cox regression analysis to determine the risk factors affecting overall survival (OS) and cancer-specific survival (CSS) in DM patients and constructed nomograms. Furthermore, we used logistic regression and Cox regression analyses to assess the specific impact of six lymph node indicators on DM incidence and patient prognosis.

Results

We collected data from 5,377 MDGA patients from the SEER database and 109 MDGC patients from hospitals. T stage, N stage, tumor size, primary site, number of positive lymph nodes, and chemotherapy were identified as independent risk factors for DM. The random forest prediction model had the best overall predictive performance (AUC = 0.919). T stage, primary site, chemotherapy, and the number of regional lymph nodes were identified as prognostic factors for OS. Moreover, T stage, number of regional lymph nodes, primary site, and chemotherapy were also influential factors for CSS. The nomograms showed good predictive value and stability in predicting the 1-, 3-, and 5-year OS and CSS in DM patients. Additionally, the log odds of a metastatic lymph node and the number of negative lymph nodes may be risk factors for DM, while the regional lymph node ratio and the number of regional lymph nodes are prognostic factors for OS.

Conclusion

The random forest prediction model accurately identified high-risk populations, and we established OS and CSS survival prediction models for MDGA patients with DM. Our hospital samples demonstrated different characteristics of lymph node indicators in terms of distant metastasis and prognosis.