A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection
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To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model.
This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity.
Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively).
Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations.
• Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules.
• Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules.
• Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.
KeywordsNon-small-cell lung carcinoma Adenocarcinoma Multidetector computed tomography Logistic models Differential diagnosis
Atypical adenomatous hyperplasia
Area under the receiver-operating characteristic curve
British Thoracic Society
Invasive pulmonary adenocarcinoma
Minimally invasive adenocarcinoma
Negative predictive value
Positive predictive value
We express our gratitude to Sunkyung Jeon, Jong Hyuk Lee, Su Yeon Ahn, Roh-Eul Yoo, Hyun-ju Lim, Juil Park, and Woo Hyeon Lim for data acquisition. We also thank Chris Woo for the manuscript editing.
This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (grant no. 2017R1A2B4008517).
Compliance with ethical standards
The scientific guarantor of this publication is Chang Min Park.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects or cohorts have been previously reported in journal articles (Eur Radiol 2016 26:4465-4474; Eur J Radiol 2016 85:1174-1180; Eur Radiol 2017 27:3266-3274; Eur Radiol 2017 10.1007/s00330-017-5171-7; Eur Radiol 2017 27:1369-1376).
• diagnostic or prognostic study
• performed at one institution
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