Natural language processing for populating lung cancer clinical research data

Abstract Background Lung 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. Methods In 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. Results Evaluation 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. Conclusion This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.

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Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review

Frontiers in Health Informatics ◽

10.30699/fhi.v10i1.264 ◽

2021 ◽

Vol 10 (1) ◽

pp. 68

Author(s):

Mahdieh Montazeri ◽

Ali Afraz ◽

Raheleh Mahboob Farimani ◽

Fahimeh Ghasemian

Keyword(s):

Lung Cancer ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

English Language ◽

Lung Cancer Patients ◽

The Difference ◽

Combination Of Methods ◽

Manual Extraction

Introduction: Lung cancer is the second most common cancer for men and women. Using natural language processing to automatically extract information from text, lead to decrease labor of manual extraction from large volume of text material and save time. The aim of this study is to systematically review of studies which reviewed NLP methods in diagnosing and staging lung cancer.Material and Methods: PubMed, Scopus, Web of science, Embase was searched for English language articles that reported diagnosing and staging methods in lung cancer Using NLP until DEC 2019. Two reviewers independently assessed original papers to determine eligibility for inclusion in the review.Results: Of 119 studies, 7 studies were included. Three studies developed a NLP algorithm to scan radiology notes and determine the presence or absence of nodules to identify patients with incident lung nodules for treatment or follow-up. Two studies used NLP to transform the report text, including identification of UMLS terms and detection of negated findings to classifying reports, also one of them used an SVM-based text classification system for staging lung cancer patients. All studies reported various performance measures based on the difference between combination of methods. Most of studies have reported sensitivity and specificity of the NLP algorithm for identifying the presence of lung nodules.Conclusion: Evaluation of studies in diagnosing and staging methods in lung cancer using NLP shows there is a number of studies on diagnosing lung cancer but there are a few works on staging that. In some studies, combination of methods was considered and NLP isolated was not sufficient for capturing satisfying results. There are potentials to improve studies by adding other data sources, further refinement and subsequent validation.

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Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00008 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Tomasz Oliwa ◽

Steven B. Maron ◽

Leah M. Chase ◽

Samantha Lomnicki ◽

Daniel V.T. Catenacci ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Classification Model ◽

Supervised Machine Learning ◽

Named Entity ◽

Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

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Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2021.103864 ◽

2021 ◽

pp. 103864

Author(s):

Hossein Naseri ◽

Kamran Kafi ◽

Sonia Skamene ◽

Marwan Tolba ◽

Mame Daro Faye ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Bone Metastases ◽

Cancer Patients ◽

Language Processing ◽

Processing Pipeline

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P2.09-29 Automatic Lung Cancer Staging from Medical Reports Using Natural Language Processing

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2018.08.1326 ◽

2018 ◽

Vol 13 (10) ◽

pp. S772

Author(s):

X. Sui ◽

T. Liu ◽

Q. Huang ◽

Y. Hou ◽

Y. Wang ◽

...

Keyword(s):

Lung Cancer ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Cancer Staging ◽

Medical Reports

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Natural Language Processing for Surveillance of Cervical and Anal Cancer and Precancer: Algorithm Development and Split-Validation Study (Preprint)

10.2196/preprints.20826 ◽

2020 ◽

Author(s):

Carlos R Oliveira ◽

Patrick Niccolai ◽

Anette Michelle Ortiz ◽

Sangini S Sheth ◽

Eugene D Shapiro ◽

...

Keyword(s):

Natural Language Processing ◽

Human Papillomavirus ◽

Natural Language ◽

Language Processing ◽

Medical Records ◽

Processing Algorithm ◽

Accurate Identification ◽

Pathology Reports ◽

Manual Review ◽

Natural Language Processing Algorithm

BACKGROUND Accurate identification of new diagnoses of human papillomavirus–associated cancers and precancers is an important step toward the development of strategies that optimize the use of human papillomavirus vaccines. The diagnosis of human papillomavirus cancers hinges on a histopathologic report, which is typically stored in electronic medical records as free-form, or unstructured, narrative text. Previous efforts to perform surveillance for human papillomavirus cancers have relied on the manual review of pathology reports to extract diagnostic information, a process that is both labor- and resource-intensive. Natural language processing can be used to automate the structuring and extraction of clinical data from unstructured narrative text in medical records and may provide a practical and effective method for identifying patients with vaccine-preventable human papillomavirus disease for surveillance and research. OBJECTIVE This study's objective was to develop and assess the accuracy of a natural language processing algorithm for the identification of individuals with cancer or precancer of the cervix and anus. METHODS A pipeline-based natural language processing algorithm was developed, which incorporated machine learning and rule-based methods to extract diagnostic elements from the narrative pathology reports. To test the algorithm’s classification accuracy, we used a split-validation study design. Full-length cervical and anal pathology reports were randomly selected from 4 clinical pathology laboratories. Two study team members, blinded to the classifications produced by the natural language processing algorithm, manually and independently reviewed all reports and classified them at the document level according to 2 domains (diagnosis and human papillomavirus testing results). Using the manual review as the gold standard, the algorithm’s performance was evaluated using standard measurements of accuracy, recall, precision, and F-measure. RESULTS The natural language processing algorithm’s performance was validated on 949 pathology reports. The algorithm demonstrated accurate identification of abnormal cytology, histology, and positive human papillomavirus tests with accuracies greater than 0.91. Precision was lowest for anal histology reports (0.87, 95% CI 0.59-0.98) and highest for cervical cytology (0.98, 95% CI 0.95-0.99). The natural language processing algorithm missed 2 out of the 15 abnormal anal histology reports, which led to a relatively low recall (0.68, 95% CI 0.43-0.87). CONCLUSIONS This study outlines the development and validation of a freely available and easily implementable natural language processing algorithm that can automate the extraction and classification of clinical data from cervical and anal cytology and histology.

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