The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types.

ABSTRACT:

Background

The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed.

Methods

Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns.

Findings

The stroma fraction was highly variable within and across the tumour types, wit

SUBMITTER: Micke P 

PROVIDER: S-EPMC7960932 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature