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Foundation Model and Multi-Instance Learning-Based Framework for Predicting Lymphovascular Invasion in Prostate

Qingyuan Zheng1,2,3,4, Haonan Mei5,6,7,8, Dan Wang5,6,7,8

  • 1Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China. zqy710890394@whu.edu.cn.

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Summary
This summary is machine-generated.

An AI framework accurately predicts lymphovascular invasion (LVI) in prostate cancer (PCa) from whole-slide images (WSIs). This tool offers interpretable insights and aids precision pathology decisions.

Keywords:
Artificial intelligenceBiomarker predictionLymphovascular invasionMulti instance learningProstate cancer

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Area of Science:

  • Pathology
  • Artificial Intelligence
  • Genomics

Background:

  • Lymphovascular invasion (LVI) is a critical prognostic factor in prostate cancer (PCa).
  • Accurate prediction of LVI is essential for patient management.
  • Current methods for LVI detection can be challenging.

Purpose of the Study:

  • To develop and validate an AI-based framework for predicting LVI in PCa.
  • Leverage multi-instance learning (MIL) and foundation models for WSI analysis.
  • Ensure accurate and interpretable LVI prediction.

Main Methods:

  • Implemented a weakly supervised deep-learning pipeline using clustering-constrained attention MIL.
  • Analyzed H&E-stained WSIs from two independent cohorts (RHWU and TCGA).
  • Utilized pretrained encoders (UNI-v2, CONCH, ResNet-50) and attention heatmaps for interpretability.

Main Results:

  • Achieved strong predictive performance, with UNI-v2 yielding AUCs of 0.839 (RHWU) and 0.854 (TCGA).
  • Attention heatmaps identified high-risk regions with specific histopathologic features.
  • Transcriptomic analysis revealed DEGs linked to mitotic and immune pathways in LVI-positive cases.

Conclusions:

  • Developed a robust and interpretable AI framework for LVI prediction in PCa from WSIs.
  • The AI model demonstrated high accuracy and provided biologically meaningful insights.
  • The framework shows potential for clinical translation as a decision-support tool in precision pathology.