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Related Experiment Video

Updated: Mar 29, 2026

Isolation of Primary Cancer-Associated Fibroblasts from a Syngeneic Murine Model of Breast Cancer for the Study of Targeted Nanoparticles

Isolation of Primary Cancer-Associated Fibroblasts from a Syngeneic Murine Model of Breast Cancer for the Study of Targeted Nanoparticles

Published on: May 14, 2021

Deep Learning Enables Pixel-Level Nanoparticle Distribution Mapping in Routine Histological Sections by Integrating

Xin Pan^1,2, Linwen Lv², Jiayi Wang²

¹School of Biomedical Engineering, Capital Medical University, Beijing 100029, China.

|March 27, 2026

Summary

This summary is machine-generated.

This study introduces NanoNet, a deep learning tool using fibroblast activation protein (FAP) imaging to predict nanomedicine distribution in tumors. It achieves high accuracy, improving nanomedicine design and personalized treatments.

Keywords:

cancer-associated fibroblasts deep learning nanomedicine spatial distribution tumor microenvironment

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Published on: June 6, 2025

Area of Science:

Biomedical Engineering
Computational Biology
Oncology

Background:

Efficient nanomedicine accumulation and distribution in tumors are vital for effective cancer therapy.
Current imaging struggles to resolve nanoparticle (NP) distribution and cellular heterogeneity.
Existing deep learning models overlook the role of cancer-associated fibroblasts (CAFs) in drug distribution.

Purpose of the Study:

To develop a deep learning framework (NanoNet) for high-resolution prediction of NP distribution.
To utilize fibroblast activation protein (FAP) immunostaining for spatial characterization of CAFs.
To investigate the impact of CAFs on nanomedicine distribution within tumors.

Main Methods:

Developed NanoNet, a deep learning framework integrating FAP immunostaining data.
Transformed histological tumor section images into pixel-level NP distribution maps.
Evaluated NanoNet's predictive accuracy using quantitative metrics (ICC, R²).

Main Results:

NanoNet achieved high predictive accuracy for NP distribution (ICC = 0.963, R² = 0.9849).
The FAP channel significantly contributed to the model's predictive performance.
Demonstrated the capability to generate pixel-level NP distribution maps from standard histological sections.

Conclusions:

NanoNet provides a novel, spatially resolved predictive framework for nanomedicine distribution.
FAP imaging is crucial for understanding and guiding NP behavior within the tumor microenvironment.
This approach holds potential for optimizing nanomedicine design and enabling personalized cancer therapy.