---
title: "LLM Framework Extracts Biomarkers and Identifies Triple-Negative Breast Cancer from Pathology Repo"
id: "pubmed-42913472"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42913472"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42913472/"
doi: "10.2196/97117"
published_at: "2026-10-23T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# LLM Framework Extracts Biomarkers and Identifies Triple-Negative Breast Cancer from Pathology Repo
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42913472
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42913472/)
- **DOI:** [10.2196/97117](https://doi.org/10.2196%2F97117)
- **Published At:** 2026-10-23T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The investigators used 532 breast cancer pathology reports from The Cancer Genome Atlas (TCGA) as the source corpus for model development and evaluation. - The study applied prompt-engineering techniques to large language models to extract structured oncology features, including **estrogen receptor status**, **progesterone receptor status**, **HER2 status**, tumor size, tumor grade, and tumor histology. - Four frontier models were evaluated: GPT-4o mini, GPT-4.1 nano, GPT-5 nano, and **Gemini 2.5 Flash-Lite**; the abstract lists release dates for each model but does not report fine-tuning procedures. - Chain-of-thought (CoT) prompting outperformed zero-shot and few-shot strategies across both biomarker extraction and TNBC classification tasks, according to the authors. - Gemini 2.5 Flash-Lite achieved the strongest performance for extracting clinicopathological attributes; GPT-4o mini with CoT prompts achieved the highest reported performance for **TNBC** classification. - The framework translated unstructured pathology text into structured data and enabled automated identification of the **triple-negative breast cancer** subtype. - The abstract does not provide numeric performance metrics, detailed prompt templates, data split proportions, interrater annotation procedures, or error analyses; those methodological specifics were not reported in the source abstract.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877. * 2 School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India. * 3 School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India. * PMID: **42913472** * DOI: [ 10.2196/97117 ](https://doi.org/10.2196/97117) Item in Clipboard # Large Language Model Framework for Structured Biomarker Extraction and Triple-Negative Breast Cancer Identification From Pathology Reports: Development and Evaluation Study Nirmit Jain et al. JMIR Form Res. 2026. Show details Display options Display options Format Abstract PubMed PMID JMIR Form Res Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22JMIR+Form+Res%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22JMIR+Form+Res%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) . 2026 Oct 23:10:e97117. doi: 10.2196/97117. ### Authors [Nirmit Jain](https://pubmed.ncbi.nlm.nih.gov/?term=Jain+N&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877."), [Trisha Annambhotla](https://pubmed.ncbi.nlm.nih.gov/?term=Annambhotla+T&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877."), [Akshara Siddharthan](https://pubmed.ncbi.nlm.nih.gov/?term=Siddharthan+A&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877."), [Harshini Kanala](https://pubmed.ncbi.nlm.nih.gov/?term=Kanala+H&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877."), [Armaan Budhiraja](https://pubmed.ncbi.nlm.nih.gov/?term=Budhiraja+A&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877."), [Manglam Goutam Sinha](https://pubmed.ncbi.nlm.nih.gov/?term=Goutam+Sinha+M&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877."), [Devyani Charan](https://pubmed.ncbi.nlm.nih.gov/?term=Charan+D&cauthor_id=42913472)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-2 "School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India."), [Muzaffar Ahmad Dar](https://pubmed.ncbi.nlm.nih.gov/?term=Ahmad+Dar+M&cauthor_id=42913472)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-3 "School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India."), [Trupti Patel](https://pubmed.ncbi.nlm.nih.gov/?term=Patel+T&cauthor_id=42913472)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-2 "School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India."), [Biji C L](https://pubmed.ncbi.nlm.nih.gov/?term=C+L+B&cauthor_id=42913472)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42913472/#short-view-affiliation-1 "School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877.") ### Affiliations * 1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Road, Katpadi, Vellore, Tamil Nadu, 632014, India, 91 9446171877. * 2 School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India. * 3 School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India. * PMID: **42913472** * DOI: [ 10.2196/97117 ](https://doi.org/10.2196/97117) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract **Background:** Triple-negative breast cancer (TNBC) is an aggressive molecular subtype of breast cancer that needs to be identified accurately from pathology reports to inform decision-making. Large language models (LLMs) have been reported to show great potential in the automatic extraction of important oncology information from unstructured medical text. **Objective:** We aimed to create and test an LLM-based approach for automatic biomarker extraction and TNBC subtype classification based on breast cancer pathology reports using prompt-engineering techniques. **Methods:** In total, 532 breast cancer pathology reports from The Cancer Genome Atlas (TCGA) were used. Our approach extracted structured oncology features, such as estrogen receptor status, progesterone receptor status, HER2 status, tumor size, tumor grade, and tumor histology, which were used to identify TNBC. We tested 4 frontier models: GPT-4o mini (released July 18, 2024), GPT-4.1 nano (released April 14, 2025), GPT-5 nano (released August 7, 2025), and Gemini 2.5 Flash-Lite. **Results:** The designed framework was able to extract relevant biomarkers and clinicopathological attributes from unstructured pathology reports and allowed for the automated identification of patients with TNBC. Gemini 2.5 Flash-Lite showed superior performance in the extraction of clinicopathological attributes. For all models investigated, chain-of-thought (CoT) prompting was shown to outperform other strategies, such as zero-shot and few-shot prompting, for both biomarker extraction and TNBC classification. GPT-4o mini using CoT prompts reported the highest performance for TNBC classification. **Conclusions:** Using prompt-engineering techniques based on LLMs proved to be an efficient method of translating unstructured breast cancer pathology reports into structured clinical information. Moreover, it can be used to accurately identify the TNBC subtype. **Keywords:** breast cancer; chain of thought; generative pretrained transformer; histology; large language model. © Nirmit Jain, Trisha Annambhotla, Akshara Siddharthan, Harshini Kanala, Armaan Budhiraja, Manglam Goutam Sinha, Devyani Charan, Muzaffar Ahmad Dar, Trupti Patel, Biji C L. Originally published in JMIR Formative Research (https://formative.jmir.org). [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## MeSH terms * Biomarkers, Tumor* / analysis Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Biomarkers%2C+Tumor%2Fanalysis%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Biomarkers%2C+Tumor) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) * Female Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Female%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Female) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) * Humans Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Humans%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Humans) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) * Large Language Models* Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Large+Language+Models%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Large+Language+Models) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) * Triple Negative Breast Neoplasms* / classification Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Triple+Negative+Breast+Neoplasms%2Fclassification%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Triple+Negative+Breast+Neoplasms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) * Triple Negative Breast Neoplasms* / diagnosis Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Triple+Negative+Breast+Neoplasms%2Fdiagnosis%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Triple+Negative+Breast+Neoplasms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) * Triple Negative Breast Neoplasms* / pathology Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Triple+Negative+Breast+Neoplasms%2Fpathology%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Triple+Negative+Breast+Neoplasms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) ## Substances * Biomarkers, Tumor Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Biomarkers%2C+Tumor%22%5Bnm%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=%22Biomarkers%2C+Tumor%22) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42913472/) [x] Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM **Send To** * [Clipboard](https://pubmed.ncbi.nlm.nih.gov/42913472/) * [Email](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42913472%2F%23open-email-panel) * [Save](https://pubmed.ncbi.nlm.nih.gov/42913472/) * [My Bibliography](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42913472%2F%23open-bibliography-panel) * [Collections](https://account.ncbi.nlm.nih
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