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Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer.

Wadia R, Akgun K, Brandt C, Fenton BT, Levin W, Marple AH, Garla V, Rose MG, Taddei T, Taylor C. Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer. JCO clinical cancer informatics. 2018 Dec 1; 2:1-7.

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PURPOSE: To compare the accuracy and reliability of a natural language processing (NLP) algorithm with manual coding by radiologists, and the combination of the two methods, for the identification of patients whose computed tomography (CT) reports raised the concern for lung cancer. METHODS: An NLP algorithm was developed using Clinical Text Analysis and Knowledge Extraction System (cTAKES) with the Yale cTAKES Extensions and trained to differentiate between language indicating benign lesions and lesions concerning for lung cancer. A random sample of 450 chest CT reports performed at Veterans Affairs Connecticut Healthcare System between January 2014 and July 2015 was selected. A reference standard was created by the manual review of reports to determine if the text stated that follow-up was needed for concern for cancer. The NLP algorithm was applied to all reports and compared with case identification using the manual coding by the radiologists. RESULTS: A total of 450 reports representing 428 patients were analyzed. NLP had higher sensitivity and lower specificity than manual coding (77.3% v 51.5% and 72.5% v 82.5%, respectively). NLP and manual coding had similar positive predictive values (88.4% v 88.9%), and NLP had a higher negative predictive value than manual coding (54% v 38.5%). When NLP and manual coding were combined, sensitivity increased to 92.3%, with a decrease in specificity to 62.85%. Combined NLP and manual coding had a positive predictive value of 87.0% and a negative predictive value of 75.2%. CONCLUSION: Our NLP algorithm was more sensitive than manual coding of CT chest reports for the identification of patients who required follow-up for suspicion of lung cancer. The combination of NLP and manual coding is a sensitive way to identify patients who need further workup for lung cancer.

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