Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Xiaonan Cui; Marjolein A Heuvelmans; Daiwei Han; Yingru Zhao; Shuxuan Fan; Sunyi Zheng; Grigory Sidorenkov; Harry J M Groen; Monique D Dorrius; Matthijs Oudkerk; Geertruida H de Bock; Rozemarijn Vliegenthart; Zhaoxiang Ye

doi:10.21037/tlcr.2019.09.17

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Transl Lung Cancer Res. 2019 Oct;8(5):605-613. doi: 10.21037/tlcr.2019.09.17.

Authors

Affiliations

¹ Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Centre of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
² Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
³ Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
⁴ Department of Pulmonology, Medisch Spectrum Twente, Enschede, The Netherlands.
⁵ Department of Radiotherapy, Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.
⁶ Department of Pulmonary Diseases, Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.
⁷ Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.
⁸ i-DNA BV, Groningen, The Netherlands.

Abstract

Background: Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients.

Methods: This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference.

Results: In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models.

Conclusions: In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable.

Keywords: Lung cancer; classification model; prognosis; pulmonary nodules.