51. Use of a radiology tool for the diagnosis of pulmonary fibrosis.
- Author
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Fan W, Chen Q, Luk L, Navot B, Maccarrone V, and Salvatore M
- Subjects
- Humans, Female, Male, Reproducibility of Results, Internet, Middle Aged, Aged, Adult, Observer Variation, Pulmonary Fibrosis diagnostic imaging, Tomography, X-Ray Computed methods
- Abstract
Objective: The purpose of this paper was to perform an exploratory reader study to assess the utility of a web-based application in assisting non-chest radiologist in correctly diagnosing the radiographic pattern of pulmonary fibrosis., Methods: Three non-chest radiologists with 5 to 20 years of experience individually reviewed 3 rounds of randomly chosen chest CT scans (round 1: 100 scans, round 2: 50 scans, round 3: 25 scans) from a list of patients with established diagnosis of pulmonary fibrosis. In round 1, radiologists were asked to directly record their diagnosis for the pattern of fibrosis. In round 2 and 3 they were asked to review for features provided in a web-based application and provide diagnosis based on the most likely predicted diagnosis from the application. There was an approximate 1-month interval and relevant tutorials were provided between each round. Diagnosis accuracy is reported by readers at each round., Results: The overall accuracy increased from 63 % (n = 188/299) in round 1 to 74 % in round 3 (n = 52/70) (p = 0.0265). Difficulty in recognition of mosaic attenuation and homogeneous has led to misdiagnosis. Refining the definition for feature homogeneous increased the diagnosis accuracy of NSIP from 42 % (n = 20/48) in round 2 to 65 % (n = 24/37) in round 3(p = 0.0179). The Fleiss Kappa across readers varied from Round 1 to Round 3 with values 0.36 to 0.42., Conclusions: Using the web-based application with refined definition for feature homogeneous helps to improve the non-subspecialty radiologist's accuracy in diagnosing different types of fibrosis., Competing Interests: Declaration of competing interest Mary M. Salvatore, MD, MBA. Speaker and Consultant: Genentech, Boehringer Ingelheim. Grant funding: Genentech, Boehringer Ingelheim. Speaker: France Foundation, Peer View. Image review: AbbVie, Lunglife AI, Bioclinica., (Copyright © 2024. Published by Elsevier Inc.)
- Published
- 2024
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