Academic radiology · 2024
Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Wang Yaqi, Liu Fengjie, Zhang Haicheng, Wang Qi, Yu Pengyi, Wang Jianwei, Zhang Zheng, Wang Guangkuo, Zhang Yu, Yang Yujuan, Mou Yakui, Mao Ning, Song Xicheng
Аннотация
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.
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.