Artificial intelligence model predicts M2 macrophage levels and HCC prognosis with only globally labeled pathological images

Artificial intelligence model predicts M2 macrophage levels and HCC prognosis with only globally labeled pathological images

Background and aimsThe levels of M2 macrophages are significantly associated with the prognosis of hepatocellular carcinoma (HCC), however, current detection methods in clinical settings remain challenging. Our study aims to develop a weakly supervised artificial intelligence model using globally la...

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Bibliographic Details
Main Authors: Huiyuan Tian, Yongshao Tian, Dujuan Li, Minfan Zhao, Qiankun Luo, Lingfei Kong, Tao Qin
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Oncology
Subjects:
deep learning
masked autoencoders
computational pathology
liver cancer
tumor microenvironment
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2024.1474155/full
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Summary:Background and aimsThe levels of M2 macrophages are significantly associated with the prognosis of hepatocellular carcinoma (HCC), however, current detection methods in clinical settings remain challenging. Our study aims to develop a weakly supervised artificial intelligence model using globally labeled histological images, to predict M2 macrophage levels and forecast the prognosis of HCC patients by integrating clinical features.MethodsCIBERSORTx was used to calculate M2 macrophage abundance. We developed a slide-level, weakly-supervised clustering method for Whole Slide Images (WSIs) by integrating Masked Autoencoders (MAE) with ResNet-32t to predict M2 macrophage abundance.ResultsWe developed an MAE-ResNet model to predict M2 macrophage levels using WSIs. In the testing dataset, the area under the curve (AUC) (95% CI) was 0.73 (0.59-0.87). We constructed a Cox regression model showing that the predicted probabilities of M2 macrophage abundance were negatively associated with the prognosis of HCC (HR=1.89, p=0.031). Furthermore, we incorporated clinical data, screened variables using Lasso regression, and built the comprehensive prediction model that better predicted prognosis. (HR=2.359, p=0.001).ConclusionOur models effectively predicted M2 macrophage levels and HCC prognosis. The findings suggest that our models offer a novel method for determining biomarker levels and forecasting prognosis, eliminating additional clinical tests, thereby delivering substantial clinical benefits.
ISSN:2234-943X

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