TY - JOUR
T1 - Influence of thoracic radiology training on classification of interstitial lung diseases
AU - Lange, Marcia
AU - Boddu, Priyanka
AU - Singh, Ayushi
AU - Gross, Benjamin D.
AU - Mei, Xueyan
AU - Liu, Zelong
AU - Bernheim, Adam
AU - Chung, Michael
AU - Huang, Mingqian
AU - Masseaux, Joy
AU - Dua, Sakshi
AU - Platt, Samantha
AU - Sivakumar, Ganesh
AU - DeMarco, Cody
AU - Lee, Justine
AU - Fayad, Zahi A.
AU - Yang, Yang
AU - Padilla, Maria
AU - Jacobi, Adam
N1 - Funding Information:
The authors do not have any acknowledgements.
Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/5
Y1 - 2023/5
N2 - Introduction: Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). Methods: This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. Results: Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2–0.46), moderate to almost perfect (Cohen's κ: 0.55–0.92), and moderate to almost perfect (Cohen's κ: 0.53–0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). Conclusions: Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. Summary sentence: Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.
AB - Introduction: Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). Methods: This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. Results: Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2–0.46), moderate to almost perfect (Cohen's κ: 0.55–0.92), and moderate to almost perfect (Cohen's κ: 0.53–0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). Conclusions: Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. Summary sentence: Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.
KW - Artificial intelligence
KW - Cardiothoracic
KW - Interreader agreement
KW - Interstitial lung disease
KW - Pulmonary fibrosis
UR - http://www.scopus.com/inward/record.url?scp=85149377759&partnerID=8YFLogxK
U2 - 10.1016/j.clinimag.2022.12.010
DO - 10.1016/j.clinimag.2022.12.010
M3 - Article
C2 - 36868033
AN - SCOPUS:85149377759
SN - 0899-7071
VL - 97
SP - 14
EP - 21
JO - Clinical Imaging
JF - Clinical Imaging
ER -