ArticleJCO clinical cancer informatics2019
Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Development and Validation of a Generative Artificial Intelligence-Based Pipeline for Automated Clinical Data Extraction From Electronic Health Records: Technical Implementation Study.JMIR bioinformatics and biotechnology · 2026Article
- Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.JCO clinical cancer informatics · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module.JCO clinical cancer informatics · 2024Article
- Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review.JMIR medical informatics · 2023Article
- Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing.Diagnostics (Basel, Switzerland) · 2022Article
- An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports.Journal of pathology informatics · 2022Article
- Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage.Frontiers in digital health · 2022Article
- Electronic case report forms generation from pathology reports by ARGO, automatic record generator for onco-hematology.Scientific reports · 2021Article
- A Natural Language Processing-Assisted Extraction System for Gleason Scores: Development and Usability Study.JMIR cancer · 2021Article
- A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning.Communications medicine · 2021Article
- Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation.JAMIA open · 2020Article
- Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LABDigital healthArticle
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Authors and funding
11 authors.
Funding
Abstract
purposeCancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches. MATERIALS AND
methodsFrom February 2016, clinicians used note templates with custom structured data elements (SDEs) during routine clinical care for men with prostate cancer. We also developed an NLP algorithm to parse radical prostatectomy pathology reports and extract structured data. We compared accuracy of clinician-entered SDEs and NLP-parsed data to manual abstraction as a gold standard and compared concordance (Cohen's κ) between approaches assuming no gold standard.
resultsThere were 523 patients with NLP-extracted data, 319 with SDE data, and 555 with manually abstracted data. For Gleason scores, NLP and clinician SDE accuracy was 95.6% and 95.8%, respectively, compared with manual abstraction, with concordance of 0.93 (95% CI, 0.89 to 0.98). For margin status, extracapsular extension, and seminal vesicle invasion, stage, and lymph node status, NLP accuracy was 94.8% to 100%, SDE accuracy was 87.7% to 100%, and concordance between NLP and SDE ranged from 0.92 to 1.0.
conclusionWe show that a real-world deployment of an NLP algorithm to extract pathology data and structured data entry by clinicians during routine clinical care in a busy clinical practice can generate accurate data when compared with manual abstraction for some, but not all, components of a prostate pathology report.
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