---
title: Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response
description: Quantitative analysis of fiber-level collagen features in H&E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ BC
---

[PathAI Resources ](https://www.pathai.com/resources)

# [Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response](https://www.pathai.com/resources/machine-learning-based-characterization-of-the-breast-cancer-tumor-microenvironment-for-assessment-of-neoadjuvant-treatment-response)

 Written by [Admin](https://www.pathai.com/resources/author/admin) | Dec 6, 2022 5:00:00 AM

## Study Background

 Neoadjuvant treatment of breast cancer has been shown to potentially reduce the extent and morbidity of subsequent surgery. Response to neoadjuvant therapy may also be prognostic; complete pathologic response (pCR) following neoadjuvant treatment is associated with improved long-term outcomes 1. pCR, defined as the absence of residual invasive cancer, is determined by evaluation of H&E-stained breast resections and regional lymph nodes following neoadjuvant treatment; however, pathologist assessment is subject to intra- and inter-reader variability.

Here we report machine learning (ML)-based models to identify tissue regions and cell types in the tumor microenvironment (TME) of H&E-stained breast cancer specimens. Model predictions were used to derive tumor bed area and a residual cancer burden score (RCB)-like score to assess residual disease after neoadjuvant therapy 2,3.

References:   
1. Spring, LM et al. Clin Cancer Res. 2020; 26:2838-2848.   
2. Hamy, A-S et al. PLoS One. 2020; 15(6): e0234191.   
3. Yau, C et al. Lancet Oncol. 2022; 23:149-160. Kirkup et al.

[View Poster](https://www.pathai.com/hubfs/2025%20Assets/SABCS-TME-Poster_11152022_FINAL.pdf)

[View full post](https://www.pathai.com/resources/machine-learning-based-characterization-of-the-breast-cancer-tumor-microenvironment-for-assessment-of-neoadjuvant-treatment-response)

```json
{
  "@context" : "http://schema.org",
  "@type" : "BlogPosting",
  "author" : {
    "@type" : "Person",
    "name" : "Admin"
  },
  "dateModified" : "2025-08-06T13:58:01.548Z",
  "datePublished" : "2022-12-06T05:00:00Z",
  "headline" : "Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response",
  "image" : {
    "@type" : "ImageObject",
    "height" : 60,
    "url" : "/hs/hsstatic/content_shared_assets/static-1.4092/img/default-amp-logo.png",
    "width" : 60
  },
  "mainEntityOfPage" : "https://www.pathai.com/resources/machine-learning-based-characterization-of-the-breast-cancer-tumor-microenvironment-for-assessment-of-neoadjuvant-treatment-response",
  "publisher" : {
    "@type" : "Organization",
    "logo" : {
      "@type" : "ImageObject",
      "height" : 60,
      "url" : "/hs/hsstatic/content_shared_assets/static-1.4092/img/default-amp-logo.png",
      "width" : 60
    },
    "name" : "PathAI Resources"
  }
}
```