ArticleBMC medical informatics and decision making2019
Natural language processing for populating lung cancer clinical research data.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Applying clinical natural language processing to lung cancer in Spain: a terminology-based panel approach.ESMO real world data and digital oncology · 2026Article
- Lung cancer research: how natural language processing enhances clinical data extraction.Journal of thoracic disease · 2026Article
- Large language model ensemble for automated TNM staging from radiology reports.Bioinformatics (Oxford, England) · 2026Article
- Transforming unstructured breast cancer pathology reports into the Observational Medical Outcomes Partnership Common Data Model.BMC medical informatics and decision making · 2026Article
- AI-Driven Patient Screening for Clinical Trials in Pancreatic Cancer: The PANCR-AI Pilot Retrospective Comparative Study.JMIR cancer · 2026Article
- Development and Assessment of a Pipeline for Extracting Structured Data From Free-Text Medical Reports Using a Large Language Model.JCO clinical cancer informatics · 2026Article
- Deep Learning Model for Natural Language to Assess Effectiveness of Patients With Non-Muscle Invasive Bladder Cancer Receiving Intravesical Bacillus Calmette-Guérin Therapy.JCO clinical cancer informatics · 2025Article
- MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers.BMC medical informatics and decision making · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights.Frontiers in oncology · 2025Review
- Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.Clinical and translational science · 2023Article
- Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.Wireless personal communications · 2023Review
- 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
- CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model.Applied clinical informatics · 2022Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundLung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique.
methodsIn this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports.
resultsEvaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively.
conclusionThis study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.
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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.