Digital image analysis provides robust tissue microenvironment-based prognosticators in patients with stage I-IV colorectal cancer

Anna Jakab; Árpád V Patai; Tamás Micsik

doi:10.1016/j.humpath.2022.07.003

Digital image analysis provides robust tissue microenvironment-based prognosticators in patients with stage I-IV colorectal cancer

Hum Pathol. 2022 Oct:128:141-151. doi: 10.1016/j.humpath.2022.07.003. Epub 2022 Jul 9.

Authors

Anna Jakab¹, Árpád V Patai², Tamás Micsik³

Affiliations

¹ Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Üllői őt 26, H-1085, Hungary; Interdisciplinary Gastroenterology Working Group, Semmelweis University, Budapest, Üllői út 78, H-1082, Hungary. Electronic address: jakab.anna@med.semmelweis-univ.hu.
² Interdisciplinary Gastroenterology Working Group, Semmelweis University, Budapest, Üllői út 78, H-1082, Hungary; Department of Surgery, Transplantation and Gastroenterology, Semmelweis University, Budapest, Üllői út 78, H-1082, Hungary.
³ Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Üllői őt 26, H-1085, Hungary; Interdisciplinary Gastroenterology Working Group, Semmelweis University, Budapest, Üllői út 78, H-1082, Hungary; Saint George Teaching Hospital of Fejér County, Székesfehérvár, Seregélyesi út 3, HU-8000, Hungary.

PMID: 35820451
DOI: 10.1016/j.humpath.2022.07.003

Abstract

In patients with colorectal cancer (CRC), a promising marker is tumor-stroma ratio (TSR). Quantification issues highlight the importance of precise assessment that might be solved by artificial intelligence-based digital image analysis systems. Some alternatives have been offered so far, although these platforms are either proprietary developments or require additional programming skills. Our aim was to validate a user-friendly, commercially available software running in everyday computational environment to improve TSR assessment and also to compare the prognostic value of assessing TSR in 3 distinct regions of interests, like hotspot, invasive front, and whole tumor. Furthermore, we compared the prognostic power of TSR with the newly suggested carcinoma percentage (CP) and carcinoma-stroma percentage (CSP). Slides of 185 patients with stage I-IV CRC with clinical follow-up data were scanned and evaluated by a senior pathologist. A machine learning-based digital pathology software was trained to recognize tumoral and stromal compartments. The aforementioned parameters were evaluated in the hotspot, invasive front, and whole tumor area, both visually and by machine learning. Patients were classified based on TSR, CP, and CSP values. On multivariate analysis, TSR-hotspot was found to be an independent prognostic factor of overall survival (hazard ratio for TSR-hotspot_software: 2.005 [95% confidence interval (CI): 1.146-3.507], P = .011, for TSR-hotspot_visual: 1.781 [CI: 1.060-2.992], P = .029). Also, TSR was an independent predictor for distant metastasis and local relapse in most settings. Generally, software performance was comparable to visual evaluation and delivered reliable prognostication in more settings also with CP and CSP values. This study presents that software-assisted evaluation is a robust prognosticator. Our approach used a less sophisticated and thus easily accessible software without the aid of a convolutional neural network; however, it was still effective enough to deliver reliable prognostic information.

Keywords: Colorectal cancer; Digital image analysis; Prognosis; Tumor microenvironment; Tumor stroma; Tumor-stroma ratio.

MeSH terms

Artificial Intelligence
Carcinoma* / pathology
Colorectal Neoplasms* / pathology
Humans
Neoplasm Recurrence, Local / pathology
Prognosis
Stromal Cells / pathology
Tumor Microenvironment