A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

Xia He,Yuanye Qu,Xuemiao Tang,Lina Jiang,Songguo Peng,Qingxia Xu,Wanli Liu,Ning Xue,Xiaohua Liu,Shulin Chen

doi:10.1186/s12935-021-01810-5

Abstract

BackgroundThis study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs).Methods Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort.ResultsA total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed − 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models.ConclusionsThe novel model in our study had a high clinical value in diagnose of MSPNs.

Highlights

This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs)
According to the 1-SE criteria, we selected λ = 0.044 as the optimal value for the model, which included 11 potential predictors with non-zero coefficients from the 63 candidate variables identified in the training cohort
(6) In order to combine the merits of the four models in predicting malignant SPNs (MSPNs), a combined easy-to-use nomogram was constructed from the three models, and the results showed the nomogram could improve the diagnostic accuracy and agreement in MSPNs and benign SPNs (BSPNs), and optimize treatment in this clinical setting

Summary

Introduction

This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). With the widespread use of low-dose computed tomography (LDCT) screening for lung cancer, a frequently reported incidence of SPNs has shown a significantly increasing trend in recent years [2]. In the SPNs cases, malignant SPNs (MSPNs) account for less than 10% of these nodules [4]. He et al Cancer Cell Int (2021) 21:115 found that the rate of SPNs positivity was 25%, but 96% of the nodules evaluated in that study were benign SPNs (BSPNs) [5]. Diagnosis and treatment of MSPNs greatly improves the overall survival rate and prognosis of patients with lung cancer [6]

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