---
title: Machine Learning Models Identify Key Histological Features of Renal Cell Carcinoma Subtypes
description: Machine Learning Models Identify Key Histological Features of Renal Cell Carcinoma Subtypes
---

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

# [Machine Learning Models Identify Key Histological Features of Renal Cell Carcinoma Subtypes](https://www.pathai.com/resources/machine-learning-models-identify-key-histological-features-of-renal-cell-carcinoma-subtypes)

 Written by [Admin](https://www.pathai.com/resources/author/admin) | Mar 23, 2023 4:00:00 AM

## Study Background

 Renal Cell Carcinoma (RCC) is a heterogeneous disease that can be classified into subtypes for diagnosis based on assessment of tumor histology, and multiple molecular biomarkers and mutations . 1, 2

Clear cell (cc) RCC is the predominant subtype comprising 80% of all cases, with papillary and chromophobe carcinoma accounting for 80% of all other RCCs, but overall, 16 different RCC subtypes have been identified. 1 Treatment selection and prognosis varies by subtype, and treatment response has been associated with the cell and tissue composition of the tumor microenvironment (TME), including tumorspecific mutations. 1 For example, PBRM1 is commonly mutated gene in RCC that may contribute to disease prognosis and response to immunotherapy, although its role is currently unclear. 3-6 Exhaustive classification and quantification of the TME by machine learning (ML) models has the potential to reveal associations between tumor histology and mutations or molecular biomarkers. 7

Here, ML models quantified histologic features of the TME directly from RCC hematoxylin and eosin (H&E)-stained whole slide images (WSI). The potential for model outputs to predict clinically-relevant biomarkers was investigated.

1 Escudier et al. Ann. Oncol. 2019;30(5):706-720  
2 Cimadamore et al. Transl. Androl. Urol. 2021 Mar;10(3):1506-1520  
3 Liu et al. Nat Commun. 2020;11(1):2135.  
4 Braun et al. JAMA Oncol. 2019;5(11):1631- 1633.  
5 Dizman et al. J Immunother Cancer. 2020;8(2):e000953  
6 Carneiro et al. Kidney Cancer 2021 5(2):79-92  
7 Diao et al. Nat Commun. 2021;12(1):1613

 Samuel Vilchez*, Isaac Finberg*, Miles Markey*, Shima Nofallah*, Kathleen Sucipto, Fedaa Najdawi, Geetika Singh, Ben Trotter, Victoria Mountain, Jake Conway, Robert Egger, Chintan Parmar, Ilan Wapinski, Stephanie Hennek, Jon Glickman

*These authors contributed equally to this project.

[View Poster](https://www.pathai.com/hubfs/2025%20Assets/Author-Approved_AACR2023_RCCTMEposter_MAR232023-1.pdf)

[View full post](https://www.pathai.com/resources/machine-learning-models-identify-key-histological-features-of-renal-cell-carcinoma-subtypes)

```json
{
  "@context" : "http://schema.org",
  "@type" : "BlogPosting",
  "author" : {
    "@type" : "Person",
    "name" : "Admin"
  },
  "dateModified" : "2025-08-06T13:48:26.739Z",
  "datePublished" : "2023-03-23T04:00:00Z",
  "headline" : "Machine Learning Models Identify Key Histological Features of Renal Cell Carcinoma Subtypes",
  "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-models-identify-key-histological-features-of-renal-cell-carcinoma-subtypes",
  "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"
  }
}
```