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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Multiplex computational pathology for treatment response predication.

Ming Y Lu¹, Houssein A Sater², Faisal Mahmood¹

¹Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA 02115, USA.

|August 10, 2021

Summary

This summary is machine-generated.

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AstroPath provides a standardized workflow for multiplex immunofluorescence (mIF) to discover biomarkers for anti-PD-1 treatment response. This enables large-scale computational pathology using machine learning on high-quality mIF data.

Area of Science:

Computational pathology
Biomarker discovery
Immunotherapy research

Background:

Multiplex immunofluorescence (mIF) is crucial for understanding tumor microenvironments.
Standardized workflows are needed for reproducible mIF analysis.
Predicting response to anti-PD-1 therapy remains a challenge.

Purpose of the Study:

To introduce AstroPath, a standardized workflow for mIF panel development, imaging, and analysis.
To demonstrate the utility of mIF in discovering biomarkers for anti-PD-1 treatment response.
To facilitate large-scale computational pathology studies using machine learning.

Main Methods:

Development of a standardized mIF workflow (AstroPath).
Application of mIF for biomarker discovery in anti-PD-1 treated patients.

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Last Updated: Oct 25, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Published on: January 21, 2019

Utilizing machine learning techniques on high-quality mIF datasets.

Main Results:

AstroPath enables reproducible mIF panel development, imaging, and analysis.
Identified potential biomarkers for predicting anti-PD-1 treatment response.
Established a foundation for large-scale computational pathology studies.

Conclusions:

The AstroPath workflow standardizes mIF, improving data quality and reproducibility.
mIF holds significant potential for biomarker discovery in immunotherapy.
This work supports the advancement of data-driven computational pathology.