RESUMEN
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.
RESUMEN
UNLABELLED: Glaucoma, a complex heterogenous disease, is the leading cause for optic nerve-related blindness worldwide. Primary open angle glaucoma (POAG) is the most common subset and by the year 2020 it is estimated that approximately 60 million people will be affected. MYOC, OPTN, CYP1B1 and WDR36 are the important candidate genes. Nearly 4% of the glaucoma patients have mutation in any one of these genes. Mutation in any of these genes causes disease either directly or indirectly and the severity of the disease varies according to position of the genes. We have compiled all the related mutations and SNPs in the above genes and developed a database, to help access statistical and clinical information of particular mutation. This database is available online at http:bicmku.in:8081/glaucoma The database, constructed using SQL, contains data pertaining to the SNPs and mutation information involved in the above genes and relevant study data. AVAILABILITY: The database is available for free at http:bicmku.in:8081/glaucoma.
