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Related Experiment Video

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Intraplaque haemorrhage quantification and molecular characterisation using attention based multiple instance

Francesco Cisternino1, Yipei Song2,3, Tim S Peters4

  • 1Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy.

Medrxiv : the Preprint Server for Health Sciences
|March 17, 2025
PubMed
Summary

Intraplaque haemorrhage (IPH) detection in atherosclerotic plaques is automated using machine learning, improving cardiovascular event prediction. This digital pathology approach accurately quantifies IPH, revealing molecular drivers of plaque instability and major adverse cardiovascular events.

Keywords:
AtherosclerosisIntraplaque haemorrhagecardiovascular diseasegeneticsmachine learningmultiple instance learning

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Area of Science:

  • Cardiovascular Pathology
  • Digital Pathology
  • Machine Learning in Medicine

Background:

  • Intraplaque haemorrhage (IPH) is a key indicator of atherosclerotic plaque vulnerability and adverse cardiovascular events.
  • Manual quantification of IPH in histological images is subjective and prone to interobserver variability.
  • Accurate IPH assessment is crucial for understanding plaque instability and predicting patient outcomes.

Purpose of the Study:

  • To develop and validate an automated, machine learning-based framework for detecting and quantifying IPH in atherosclerotic plaques.
  • To compare the performance of different histological stains for IPH quantification.
  • To integrate digital pathology with molecular data to characterize IPH and its association with clinical outcomes.

Main Methods:

  • An attention-based additive multiple instance learning (MIL) framework was developed using whole-slide images from the Athero-Express biobank (2,595 patients).
  • Nine distinct histological stains, including Haematoxylin and Eosin (H&E), were evaluated for IPH detection.
  • Ensemble models combining H&E with CD68 or Verhoeff-Van Gieson (EVG) elastic fibers staining were explored.
  • IPH area was derived from MIL-derived attention scores, and molecular pathways were analyzed using single-cell transcriptomics.

Main Results:

  • The developed MIL framework accurately detected and quantified IPH, outperforming manual scoring.
  • Haematoxylin and Eosin (H&E) staining showed high performance (AUROC = 0.86), significantly improved by combining with CD68 or EVG (AUROC = 0.92).
  • IPH presence and area were identified as the strongest predictors of preoperative symptoms and major adverse cardiovascular events (MACE).
  • Key molecular pathways associated with IPH, including TNF-α signaling and foam cell presence, were identified.

Conclusions:

  • Automated IPH quantification using digital pathology and machine learning offers a scalable, reproducible, and interpretable method for plaque phenotyping.
  • This approach enhances the prediction of cardiovascular events and provides novel insights into the molecular mechanisms underlying IPH-driven plaque instability.
  • The findings facilitate a deeper understanding of how IPH contributes to symptoms and MACE, paving the way for improved patient management.