Artificial intelligence model predicts M2 macrophage levels and HCC prognosis with only globally labeled pathological images
2024

AI Model Predicts M2 Macrophage Levels and HCC Prognosis from Pathological Images

Sample size: 485 publication 10 minutes Evidence: moderate

Author Information

Author(s): Tian Huiyuan, Tian Yongshao, Li Dujuan, Zhao Minfan, Luo Qiankun, Kong Lingfei, Qin Tao

Primary Institution: Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital

Hypothesis

Can an artificial intelligence model predict M2 macrophage levels and the prognosis of hepatocellular carcinoma (HCC) using globally labeled pathological images?

Conclusion

The AI models effectively predicted M2 macrophage levels and HCC prognosis, suggesting a novel method for determining biomarker levels and forecasting prognosis without additional clinical tests.

Supporting Evidence

  • The AI model achieved an AUC of 0.73 in predicting M2 macrophage levels.
  • The predicted probabilities of M2 macrophage abundance were negatively associated with HCC prognosis.
  • Using Lasso regression, the study identified significant clinical variables for prognosis prediction.

Takeaway

Researchers created a computer program that can look at images of liver cancer and guess how many special immune cells are present, which helps predict how well patients will do.

Methodology

The study used a weakly supervised AI model combining Masked Autoencoders with ResNet-32t to analyze Whole Slide Images for predicting M2 macrophage levels and HCC prognosis.

Potential Biases

Potential biases may arise from the variability in slide quality and the limited number of pathological images used.

Limitations

The study faced challenges with the consistency and quality of pathological slides and had a relatively small number of images, necessitating larger datasets for more robust results.

Participant Demographics

The study included 132 HCC patients from Henan Provincial People’s Hospital and 353 from the TCGA database, with a median age of 61 years.

Statistical Information

P-Value

p=0.031

Confidence Interval

95% CI: 0.59-0.87

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.3389/fonc.2024.1474155

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