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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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AI-based tumor-stroma ratio quantification algorithm: comprehensive evaluation of prognostic role in primary

Rita Carvalho1,2, Thomas Zander3, Vincenzo Mitchell Barroso2

  • 1Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Virchows Archiv : an International Journal of Pathology
|February 13, 2025
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Summary

A new automated algorithm accurately quantifies tumor-stroma ratio (TSR) in colorectal cancer, providing objective prognostic insights. This tool enhances prediction of patient survival outcomes by overcoming human variability in analysis.

Keywords:
AI algorithmColorectal cancerDigital pathologyPrognosisTumor-stroma ratio

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Area of Science:

  • Digital pathology
  • Computational oncology
  • Cancer biomarker development

Background:

  • Tumor-stroma ratio (TSR) is a prognostic indicator in colorectal cancer, but manual quantification suffers from interobserver variability.
  • Accurate and objective TSR assessment is crucial for predicting patient outcomes in primary colorectal cancer.
  • Existing methods for TSR analysis lack automation and standardization, limiting their clinical utility.

Purpose of the Study:

  • To develop a fully automated, quantitative algorithm for precise TSR analysis in colorectal cancer using H&E-stained histological sections.
  • To validate the prognostic value of the automated TSR quantification across multiple patient cohorts.
  • To investigate the impact of analytical area size on TSR quantification and its prognostic performance.

Main Methods:

  • Development of a segmentation-based algorithm for pixel-wise mapping of tissue classes (tumor cells, stroma, necrosis, mucin) in H&E slides.
  • Inclusion of three independent cohorts of patients with stage I-IV primary operable colorectal cancer (total N=1257).
  • Testing of three analytical area sizes (1.0, 1.5, 2.0 mm) for maximal TSR quantification and subsequent prognostic analysis using Cox regression.

Main Results:

  • The automated algorithm achieved accurate segmentation and quantification of tissue components, enabling objective TSR assessment.
  • Maximal TSR values were dependent on the analytical area size, highlighting the need for standardization.
  • An analytical area size of 1.0 mm demonstrated the best prognostic performance, with TSR independently predicting progression-free survival, cancer-specific survival, and overall survival.

Conclusions:

  • A fully automated, quantitative, and objective tool for TSR assessment in primary colorectal cancer has been developed and validated.
  • The developed algorithm provides independent prognostic value, enhancing the prediction of patient survival.
  • Standardization of analytical parameters, particularly the area size for TSR quantification, is essential for consistent prognostic performance.