Stella K Kang1, Kira Garry2, Ryan Chung3, William H Moore3, Eduardo Iturrate4, Jordan L Swartz5, Danny C Kim3, Leora I Horwitz6, Saul Blecker6. 1. Department of Radiology, NYU Langone Health, New York, New York; Department of Population Health, NYU Langone Health, New York, New York. Electronic address: stella.kang@nyumc.org. 2. Department of Population Health, NYU Langone Health, New York, New York. 3. Department of Radiology, NYU Langone Health, New York, New York. 4. Department of Medicine, NYU Langone Health, New York, New York. 5. Department of Emergency Medicine, NYU Langone Health, New York, New York. 6. Department of Population Health, NYU Langone Health, New York, New York; Department of Medicine, NYU Langone Health, New York, New York.
Abstract
PURPOSE: To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations. METHODS AND MATERIALS: We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017. RESULTS: The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43). CONCLUSION: NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.
PURPOSE: To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations. METHODS AND MATERIALS: We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017. RESULTS: The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43). CONCLUSION: NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.
Authors: Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi Journal: Radiol Artif Intell Date: 2022-02-02
Authors: Catherine Byrd; Ureka Ajawara; Ryan Laundry; John Radin; Prasha Bhandari; Ann Leung; Summer Han; Stephen M Asch; Steven Zeliadt; Alex H S Harris; Leah Backhus Journal: BMC Med Inform Decis Mak Date: 2022-06-03 Impact factor: 3.298
Authors: Arlene Casey; Emma Davidson; Michael Poon; Hang Dong; Daniel Duma; Andreas Grivas; Claire Grover; Víctor Suárez-Paniagua; Richard Tobin; William Whiteley; Honghan Wu; Beatrice Alex Journal: BMC Med Inform Decis Mak Date: 2021-06-03 Impact factor: 2.796