ArticleCureus2026
A Reproducible Protocol and Framework for Large Language Model (LLM)-Assisted Estrogen and Progesterone Receptor (ER/PR) Scoring.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Estrogen and progesterone receptor (ER/PR) scoring in breast cancer is vulnerable to interobserver variability, particularly at diagnostic thresholds. While large language models (LLMs) offer potential as ancillary tools, no standardized deployment framework exists. This is a feasibility and protocol-development study that establishes a reproducible protocol for LLM implementation in pathology labs. Using College of American Pathologists (CAP) proficiency-testing tissue microarrays, we developed a systematic framework that combines recursive prompt engineering and zero-state reset (ZSR) protocols, requiring fresh chat sessions for each evaluation to prevent conversational bias. Three models were evaluated: Claude Haiku 4.5 (Anthropic, San Francisco, CA, USA), Gemini 3.0 (Google, Mountain View, CA, USA), and Gemma 3.0 12B (Google, Mountain View, CA, USA). The ZSR protocol proved essential, improving accuracy from a 70-80% baseline to ≥95% CAP concordance. Performance varied across models and scoring approaches: Claude achieved the highest concordance (90-98%), followed by Gemini (85-100%) and Gemma (73-93%), demonstrating that systematic prompt refinement, not model capacity, drives diagnostic accuracy. Final concordance exceeded 83% across all models. The locally hosted model approached cloud-level performance within a clinically meaningful range, achieving concordance rates that, while numerically lower, remained within acceptable feasibility thresholds for a protocol development context. We identified systematic error sources, including image artifacts, slide contamination, and compression effects. This protocol provides pathology laboratories with evidence-based guidance for implementing LLM-assisted ER/PR scoring without requiring specialized infrastructure or extensive computational resources. The framework is immediately actionable as a research and quality-assurance tool and provides a reproducible foundation for future clinical validation studies across laboratory settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.