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Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter
Yaqi Wang1, Fengjie Liu2, Haicheng Zhang3
1Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China (Y.W., P.Y., J.W., Z.Z., G.W., Y.Z., Y.Y., Y.M., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, China (Y.W., P.Y., J.W., Z.Z., G.W., Y.Z., Y.Y., Y.M., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, China (Y.W., P.Y., J.W., Y.Z., Y.Y., Y.M., X.S.).
Rationale And Objectives:
Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing between NP and IP.
Materials And Methods:
A total of 1791 patients with nasal benign tumors from two hospitals were retrospectively enrolled. Patients were divided into training, internal test, and external test sets. DL models (3D ResNet, 3D Xception, and HRNet) were employed to identify NP from IP using computed tomography images. Model performance was evaluated via receiver operating characteristic curve analysis, accuracy, sensitivity, and specificity. The best-performing model was compared with radiologists' interpretations. The potential enhancement of radiologists' diagnostic performance using the optimal DL model was investigated. Additionally, proteomics analysis in 70 patients was conducted to elucidate the biological underpinnings of the DL model.
Results:
The 3D Xception model emerged as the best-performing DL model, achieving the highest area under the receiver operating characteristic curve of 0.999 (95% confidence interval [CI]: 0.950-1.000) in the training set, 0.981 (95% CI: 0.950-1.000) in the internal test set, and 0.933 (95% CI: 0.9099-0.9557) in the external test set. The sensitivity and specificity of the optimal DL model surpassed those of the four radiologists. Furthermore, the DL model improved average radiologist sensitivity from 0.845 to 0.884 and specificity from 0.670 to 0.840. Proteomic analysis revealed an association between the model predictions and epithelial cell differentiation.
Conclusion:
DL based on CT images holds promise for distinguishing between NP and IP lesions, thereby augmenting clinicians' interpretation capabilities.
