The tumor–stroma ratio (TSR) is an established prognostic biomarker across several cancer types, yet its manual assessment remains labour‑intensive and subject to inter‑observer variability. An artificial intelligence (AI)-based estimate could offer an efficient, consistent alternative for this task. In this study, quantitative comparisons were made between expert humans and an AI model for TSR estimation. Using two independent, multi‑institutional histopathology datasets, an Attention U-Net was benchmarked against experienced pathologists. In a subset of the TCGA-BRCA dataset, the AI model demonstrated comparable trends to human consensus for TSR quantification, achieving an intraclass correlation coefficient (ICC) of 0.69. However, the AI model's TSR scores are on average 5 percentage points higher compared to human scores on this dataset. The AI model was found to be more consistent at estimating TSR than either of the human counterparts, with a discrepancy ratio (DR) of 0.86. Results on an external dataset obtained from the Netherlands Cancer Institute consisting of cases (n=357) from 35 different Dutch hospitals showed that the AI model's TSR scores are 7 percentage points lower on average compared to the human rater with an ICC of 0.59. To account for the model's imperfect segmentation performance, we derived an estimate of the ambiguity in AI-based TSR predictions. The results indicate that, despite this ambiguity, the AI not only follows similar trends but also delivers greater overall scoring consistency than manual TSR assessment by humans.

The authors have declared no competing interest.

This study did not receive any funding

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The institutional review board at the Netherlands Cancer Institute based in Amsterdam approved the retrospective use of Histo-AI dataset (IRB number: IRBdm20-140) and the SmallTNBC dataset (IRB number: IRBdm20-231) for this research.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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This study uses data from different sources. The whole slide images from the TCGA-BRCA study are publicly available at the GDC data portal: https://portal.gdc.cancer.gov/projects/TCGA-BRCA. The whole slide images from the Histo-AI, SmallTNBC, the segmentation annotations, the TSR scores of human experts in the TCGA-BRCA and the SmallTNBC datasets are subject to availability upon request. Please contact the corresponding author for access.

https://portal.gdc.cancer.gov/projects/TCGA-BRCA