ArticleJournal of digital imaging2012
Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT).
Article in Journal of digital imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed.
- Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.AJR. American journal of roentgenology · 2023Article
- Factors Associated With Follow-up Testing Completion in Patients With Incidental Pulmonary Nodules Assessed to Require Follow-up.Journal of the American College of Radiology : JACR · 2022Article
- Radiologist Variation in the Rates of Follow-up Imaging Recommendations Made for Pulmonary Nodules.Journal of the American College of Radiology : JACR · 2021Article
- Integrity of clinical information in radiology reports documenting pulmonary nodules.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Factors Associated With Optimal Follow-up in Women With BI-RADS 3 Breast Findings.Journal of the American College of Radiology : JACR · 2020Article
- Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.JCO clinical cancer informatics · 2019Article
- Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.Journal of the American College of Radiology : JACR · 2019Article
- Automatic Detection of Negated Findings in Radiological Reports for Spanish Language: Methodology Based on Lexicon-Grammatical Information Processing.Journal of digital imaging · 2019Article
- Semiautomated System for Nonurgent, Clinically Significant Pathology Results.Applied clinical informatics · 2018Article
- Impact of a Health Information Technology Intervention on the Follow-up Management of Pulmonary Nodules.Journal of digital imaging · 2018Article
- Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange-Based Prior Computed Tomography (CT) Alerting System.JMIR medical informatics · 2017Article
- Assessing Inaccuracies in Automated Information Extraction of Breast Imaging Findings.Journal of digital imaging · 2017Article
- Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.Journal of digital imaging · 2015Article
- Biomedical imaging ontologies: A survey and proposal for future work.Journal of pathology informatics · 2015Article
- Four-year impact of an alert notification system on closed-loop communication of critical test results.AJR. American journal of roentgenology · 2014Article
- Journal club: Renal masses detected at abdominal CT: radiologists' adherence to guidelines regarding management recommendations and communication of critical results.AJR. American journal of roentgenology · 2014Article
- Retrieval of radiology reports citing critical findings with disease-specific customization.The open medical informatics journal · 2012Article
- Risk of Malignancy in Incidentally Detected Lung Nodules in Patients Aged Younger Than 35 Years.Journal of computer assisted tomographyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Radiology reports are permanent legal documents that serve as official interpretation of imaging tests. Manual analysis of textual information contained in these reports requires significant time and effort. This study describes the development and initial evaluation of a toolkit that enables automated identification of relevant information from within these largely unstructured text reports. We developed and made publicly available a natural language processing toolkit, Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT). Core functions are included in the following modules: the Data Loader, Header Extractor, Terminology Interface, Reviewer, and Analyzer. The toolkit enables search for specific terms and retrieval of (radiology) reports containing exact term matches as well as similar or synonymous term matches within the text of the report. The Terminology Interface is the main component of the toolkit. It allows query expansion based on synonyms from a controlled terminology (e.g., RadLex or National Cancer Institute Thesaurus [NCIT]). We evaluated iSCOUT document retrieval of radiology reports that contained liver cysts, and compared precision and recall with and without using NCIT synonyms for query expansion. iSCOUT retrieved radiology reports with documented liver cysts with a precision of 0.92 and recall of 0.96, utilizing NCIT. This recall (i.e., utilizing the Terminology Interface) is significantly better than using each of two search terms alone (0.72, p=0.03 for liver cyst and 0.52, p=0.0002 for hepatic cyst). iSCOUT reliably assembled relevant radiology reports for a cohort of patients with liver cysts with significant improvement in document retrieval when utilizing controlled lexicons.
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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.