Deciphering the Morphology of Tumor-stromal Features in Invasive Breast Cancer Using Artificial Intelligence.

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Abstract

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphological assessment of tumor and stroma with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (i) stroma to tumor ratio (S:TR) and (ii) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole slide images (WSIs) of a large cohort (n=1,968) of well-characterized luminal BC cases were examined. Region and cell level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n=1027) and test (n=941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristics of good prognosis and longer patients` survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC specific survival; HR:1.7, p=0.03, 95%CI: 1.04 – 2.83 and distant metastasis-free survival; HR:1.64, p=0.04, 95%CI: 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphological stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.Copyright © 2023. Published by Elsevier Inc.