Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre

• Published in: Frontiers in medicine Vol. 8; p. 748168
• Main Authors: Hunter, Benjamin, Reis, Sara, Campbell, Des, Matharu, Sheila, Ratnakumar, Prashanthi, Mercuri, Luca, Hindocha, Sumeet, Kalsi, Hardeep, Mayer, Erik, Glampson, Ben, Robinson, Emily J., Al-Lazikani, Bisan, Scerri, Lisa, Bloch, Susannah, Lee, Richard
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 04.11.2021
• Subjects: informatics; lung nodule; machine learning; Medicine; natural language processing (NLP); structured query language (SQL)
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2021.748168

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.