Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool

Basilio, Rodrigo; Carvalho, Alysson Roncally; Rodrigues, Rosana; Conrado, Marco; Accorsi, Stefânia Anselmini; Forghani, Reza; Machuca, Tiago; Zanon, Matheus; Altmayer, Stephan; Hochhegger, Bruno

doi:10.1200/go.23.00191

JCO Glob Oncol

2023

DOI: 10.1200/go.23.00191

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Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool

Rodrigo Basilio,

Alysson Roncally Carvalho,

Rosana Rodrigues

et al.

Abstract: NLP tool accurately identifies ILNs in chest CT reports, enabling early lung cancer detection and reducing patient loss to follow-up.

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2024

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The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

Mojibian,

Jaskolka,

Ching

et al. 2024

Can Assoc Radiol J

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Purpose: This study evaluates the efficacy of a commercial medical Named Entity Recognition (NER) model combined with a post-processing protocol in identifying incidental pulmonary nodules from CT reports. Methods: We analyzed 9165 anonymized CT reports and classified them into 3 categories: no nodules, nodules present, and nodules >6 mm. For each report, a generic medical NER model annotated entities and their relations, which were then filtered through inclusion/exclusion criteria selected to identify pulmonary nodules. Ground truth was established by manual review. To better understand the relationship between model performance and nodule prevalence, a subset of the data was programmatically balanced to equalize the number of reports in each class category. Results: In the unbalanced subset of the data, the model achieved a sensitivity of 97%, specificity of 99%, and accuracy of 99% in detecting pulmonary nodules mentioned in the reports. For nodules >6 mm, sensitivity was 95%, specificity was 100%, and accuracy was 100%. In the balanced subset of the data, sensitivity was 99%, specificity 96%, and accuracy 97% for nodule detection; for larger nodules, sensitivity was 94%, specificity 99%, and accuracy 98%. Conclusions: The NER model demonstrated high sensitivity and specificity in detecting pulmonary nodules reported in CT scans, including those >6 mm which are potentially clinically significant. The results were consistent across both unbalanced and balanced datasets indicating that the model performance is independent of nodule prevalence. Implementing this technology in hospital systems could automate the identification of at-risk patients, ensuring timely follow-up and potentially reducing missed or late-stage cancer diagnoses.

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The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

Mojibian,

Jaskolka,

Ching

et al. 2024

Can Assoc Radiol J

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show abstract

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Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool

Abstract: NLP tool accurately identifies ILNs in chest CT reports, enabling early lung cancer detection and reducing patient loss to follow-up.

Cited by 1 publication

References 21 publications

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

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