Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment.

ABSTRACT:

Background

The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible.

Methods

A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages

SUBMITTER: Smit MA 

PROVIDER: S-EPMC9922811 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature