In colorectal cancer (CRC) patients, a promising marker is tumor-stroma ratio (TSR).
Quantification issues highlight the importance of precise assessment that might be
solved by artificial intelligence (AI)-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 three
distinct regions of interests (ROIs), like hotspot, invasive front and whole tumor.
Furthermore, we compared the prognostic power of TSR with newly suggested carcinoma
percentage (CP) and carcinoma-stroma percentage (CSP).Slides of 185 stage I-IV CRC
patients 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-hotspotsoftware: 2.005 (95% confidence interval (CI): 1.146-3.507), p=0.011,
for TSR-hostpotvisual: 1.781 (CI: 1.060-2.992) p=0.029). 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 convolutional neural network; however,
it was still effective enough to deliver reliable prognostic information.
