---
title: "Automated OCR extraction of genomic biomarkers (Oncotype DX) to reduce oncology data latency"
id: "pubmed-42763830"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42763830"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42763830/"
doi: "10.1007/s10552-026-02252-y"
published_at: "2026-09-20T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Automated OCR extraction of genomic biomarkers (Oncotype DX) to reduce oncology data latency
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42763830
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42763830/)
- **DOI:** [10.1007/s10552-026-02252-y](https://doi.org/10.1007%2Fs10552-026-02252-y)
- **Published At:** 2026-09-20T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Real-world oncology research and precision care depend on timely capture of **genomic biomarkers**, but many results are trapped in scanned, unstructured clinical documents requiring manual abstraction. - This study tested automated extraction of **Oncotype DX** recurrence scores from scanned reports using three open-source optical character recognition (**OCR**) approaches: Tesseract, EasyOCR, and a hybrid implementation. - The dataset comprised 675 Oncotype DX reports from a Midwestern U.S. health system; OCR-derived values were compared with manually abstracted scores and local cancer registry data. - Performance was evaluated with agreement, precision, recall, F1 score, and processing time; multivariable logistic regression examined factors associated with discordance between registry and manual abstraction. - The **hybrid OCR** approach performed best: 97% agreement with manual abstraction, precision 0.997, recall 0.972, and F1 score 0.984. - Registry abstraction showed comparable accuracy but required substantially more manual effort; automated methods greatly reduced processing time while maintaining high accuracy. - Logistic regression indicated registry discordance was largely independent of patient and tumor characteristics; the only significant predictor identified was unknown progesterone receptor (PR) status. - The authors conclude that automated extraction of genomic biomarkers is a scalable strategy to reduce delays in cancer data availability and could support cancer registry modernization and improved real-world evidence generation in precision oncology. - Keywords reported in the source included Breast cancer, Genomics, Oncology, and Optical character recognition. - The authors declared no competing interests in the conflict of interest statement.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 University of Minnesota Medical School, Minneapolis, MN, USA. * 2 Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. * 3 Center for Learning Health Systems Science (CLHSS), University of Minnesota, Minneapolis, MN, USA. * 4 Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA. * 5 Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA. * 6 Division of Surgical Oncology, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. * 7 Division of Surgical Oncology, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * 8 Clinical Quality, Outcomes, Discovery and Evaluation Core (CQODE), University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * 9 Center for Learning Health Systems Science (CLHSS), University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * 10 Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * PMID: **42763830** * DOI: [ 10.1007/s10552-026-02252-y ](https://doi.org/10.1007/s10552-026-02252-y) Item in Clipboard # Bridging the data latency gap: automated extraction of genomic biomarkers from unstructured clinical documents to support real-world oncology data Qianyun Luo et al. Cancer Causes Control. 2026. Show details Display options Display options Format Abstract PubMed PMID Cancer Causes Control Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Cancer+Causes+Control%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Cancer+Causes+Control%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42763830/) . 2026 Sep 20;37(10):165. doi: 10.1007/s10552-026-02252-y. ### Authors [Qianyun Luo](https://pubmed.ncbi.nlm.nih.gov/?term=Luo+Q&cauthor_id=42763830)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-1 "University of Minnesota Medical School, Minneapolis, MN, USA."), [Rui Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+R&cauthor_id=42763830)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-2 "Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-3 "Center for Learning Health Systems Science \(CLHSS\), University of Minnesota, Minneapolis, MN, USA.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-4 "Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA."), [Nikitha Vobugari](https://pubmed.ncbi.nlm.nih.gov/?term=Vobugari+N&cauthor_id=42763830)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-5 "Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-4 "Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA."), [Jane Y C Hui](https://pubmed.ncbi.nlm.nih.gov/?term=Hui+JYC&cauthor_id=42763830)[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-6 "Division of Surgical Oncology, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-4 "Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA."), [Schelomo Marmor](https://pubmed.ncbi.nlm.nih.gov/?term=Marmor+S&cauthor_id=42763830)[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-7 "Division of Surgical Oncology, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu.")[ 8 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-8 "Clinical Quality, Outcomes, Discovery and Evaluation Core \(CQODE\), University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu.")[ 9 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-9 "Center for Learning Health Systems Science \(CLHSS\), University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu.")[ 10 ](https://pubmed.ncbi.nlm.nih.gov/42763830/#short-view-affiliation-10 "Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu.") ### Affiliations * 1 University of Minnesota Medical School, Minneapolis, MN, USA. * 2 Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. * 3 Center for Learning Health Systems Science (CLHSS), University of Minnesota, Minneapolis, MN, USA. * 4 Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA. * 5 Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA. * 6 Division of Surgical Oncology, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. * 7 Division of Surgical Oncology, Department of Surgery, University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * 8 Clinical Quality, Outcomes, Discovery and Evaluation Core (CQODE), University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * 9 Center for Learning Health Systems Science (CLHSS), University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * 10 Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA. marm0014@umn.edu. * PMID: **42763830** * DOI: [ 10.1007/s10552-026-02252-y ](https://doi.org/10.1007/s10552-026-02252-y) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Purpose:** Real-world oncology data are essential for clinical research and precision cancer care. However, genomic biomarkers are often embedded in scanned, unstructured clinical documents requiring manual abstraction before becoming available in cancer registries, delaying real-world evidence generation. This study evaluated and compared three open-source optical character recognition (OCR) approaches, Tesseract, EasyOCR, and a hybrid implementation, to determine which best enables automated extraction of Oncotype DX recurrence scores and improves the timeliness and quality of real-world oncology data. **Methods:** We evaluated the feasibility of automated genomic data extraction using 675 Oncotype DX reports from a Midwestern U.S. health system. EasyOCR, Tesseract, and a hybrid OCR approach were used to extract recurrence scores from scanned reports. OCR-derived values were compared with manually abstracted scores and local cancer registry data. Performance was assessed using agreement, precision, recall, F1 score, and processing time. Multivariable logistic regression was performed to identify factors associated with discordance between registry-reported and manually abstracted scores. **Results:** The hybrid OCR approach demonstrated the highest performance, achieving 97% agreement with manual abstraction, precision of 0.997, recall of 0.972, and an F1 score of 0.984. Registry abstraction demonstrated comparable performance but required greater manual effort. Automated extraction substantially reduced processing time while maintaining high accuracy. Logistic regression showed registry discordance was largely independent of patient and tumor characteristics, with unknown progesterone receptor (PR) status as the only significant predictor. **Conclusion:** Automated extraction of genomic biomarkers represents a scalable approach to reducing delays in cancer data availability. Earlier capture of genomic information may support cancer registry modernization and improve real-world evidence generation in precision oncology. **Keywords:** Breast cancer; Genomics; Oncology; Optical character recognition. © 2026. The Author(s). [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declarations. Competing interests: The authors declare no competing interests. ## References 1. 1. Booth CM, Karim S, Mackillop WJ (2019) Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol 16:312–325. - [DOI](https://doi.org/10.1038/s41571-019-0167-7) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/30700859/) 2. 1. (2020) Real-World Evidence. U.S. Food and Drug Administration 3. 1. U.S. Food and Drug Administration (2017) Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. U.S. Food and Drug Administration 4. 1. Chishtie J, Sapiro N, Wiebe N et al (2023) Use of Epic electronic health record system for health care research: scoping review. J Med Internet Res 25:e51003. - [DOI](https://doi.org/10.2196/51003) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/38100185/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/10757236/) 5. 1. Penberthy LT, Rivera DR, Lund JL et al (2022) An overview of real-world data sources for oncology and considerations for research. CA A Cancer J Clin 72:287–300. - [DOI](https://doi.org/10.3322/caac.21714) Show all 36 references ## MeSH terms * Biomarkers, Tumor* / genetics Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Biomarkers%2C+Tumor%2Fgenetics%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/42763830/) * Data Mining* / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Data+Mining%2Fmethods%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Data+Mining) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42763830/) * 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/42763830/) * Genomics* / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Genomics%2Fmethods%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Genomics) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42763830/) * 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/42763830/) * Medical Oncology* / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Medical+Oncology%2Fmethods%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Medical+Oncology) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42763830/) * Neoplasms* / genetics Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Neoplasms%2Fgenetics%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](htt
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