Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.
Date
2021-11-04ICR Author
Author
Hunter, B
Reis, S
Campbell, D
Matharu, S
Ratnakumar, P
Mercuri, L
Hindocha, S
Kalsi, H
Mayer, E
Glampson, B
Robinson, EJ
Al-Lazikani, B
Scerri, L
Bloch, S
Lee, R
Type
Journal Article
Metadata
Show full item recordAbstract
Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. Objective: To automate lung nodule identification in a tertiary cancer centre. Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients. Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy. Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.
Collections
Subject
informatics
lung nodule
machine learning
natural language processing (NLP)
structured query language (SQL)
Research team
Computational Biology
Language
eng
Date accepted
2021-10-07
License start date
2021-11-04
Citation
Frontiers in Medicine, 2021, 8 pp. 748168 -
Publisher
FRONTIERS MEDIA SA