The following abstract is drawn from a recently published paper in American Journal of Roentgenology. We invite you to read the full paper and join the conversation, become a member of the Radiology News community to share your thoughts, ask questions, and engage with others around this work.

Authors: Hyungjin Kim, MD, PhD, Jong Eun Lee, MD, Kum Ju Chae, MD, PhD, Jooae Choe, MD, PhD, Jung Hee Hong, MD, MS, Kwang Nam Jin, MD, PhD, Hyo-jae Lee, MD, PhD, Yun-Hyeon Kim, MD, PhD, Anna J. Podolanczuk, MD, Christopher J. Ryerson, MD, Yeon Joo Jeong, MD, PhD, and Soon Ho Yoon, MD, PhD

Abstract

Background
Interstitial lung abnormalities (ILA) on chest CT are receiving growing attention given their association with progression to interstitial lung disease. Radiography’s role in ILA detection is not well described.

Objective
To evaluate the diagnostic performance of radiologists and an artificial intelligence (AI) model in detecting ILA on chest radiographs using CT as the reference.

Methods
This retrospective study included adults who underwent both chest CT and chest radiography as part of health check-up programs at two institutions in Korea between January 2007 and December 2010. Five thoracic radiologists independently assessed ILA likelihood on radiographs using a 5-point Likert scale (positive, ≥4). A previously developed AI model (AIRead-CXR; Soombit.ai) processed radiographs to generate a probability (0 to 1) of reticular or reticulonodular opacities (positive, ≥0.4). CT served as the reference standard for fibrotic and nonfibrotic ILA. Radiologists’ diagnostic performance for ILA detection was reported using mean performance metrics and compared with AI performance using generalized estimating equations. Associations of AI-based radiographic ILA, adjusting for age, sex, and smoking status, were assessed with all-cause mortality and respiratory disease-related mortality using Cox proportional hazard and Fine–Gray competing-risk regression models.

Results
The analysis included 1168 individuals (median age, 56 years; 786 male, 382 female). Forty-one individuals had ILA on CT (fibrotic, 22; nonfibrotic, 19). For fibrotic ILA, radiologists and AI had AUC of 0.86 and 0.92 (P=.06), sensitivity of 62.7% and 68.2% (P=.43), specificity of 97.8% and 98.7% (P=.05), and accuracy of 97.2% and 98.1% (P=.04), respectively. For fibrotic or nonfibrotic ILA, radiologists and AI had AUC of 0.75 and 0.83 (P=.009), sensitivity of 38.5% and 41.5% (P=.48), specificity of 98.0% and 98.8% (P=.05), and accuracy of 95.9% and 96.8% (P=.03), respectively. During a median follow-up of 11.9 years, radiographic ILA was independently associated with respiratory disease-mortality (adjusted subdistribution HR, 8.72; P<.001) but not overall mortality (adjusted HR, 1.75; P=.17).

Conclusion
Radiologists and AI achieved suboptimal sensitivity for ILA detection on radiography, albeit high specificity.

Clinical Impact
Despite association of radiographic ILA with a clinically relevant outcome, the findings do not support radiographic screening for ILA, whether incorporating radiologist or AI interpretation.

Read the full article: Detection of Interstitial Lung Abnormalities on Chest Radiographs: Diagnostic Performance of Radiologists and Artificial Intelligence | AJR