• C L Jia, Y Cao, Q Song, W B Zhang, J J Li, X X Wu, P Y Yu, Y K Mou, N Mao, and X C Song.
• Big Data and Artificial Intelligence Laboratory, Yuhuangding Hospital of Qingdao University, Yantai 264000, Shandong Province, China.
• Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2020 Dec 7; 55 (12): 1154-1161.

AbstractObjective: To establish and validate a radiomics nomogram based on MR for predicting cervical lymph node metastasis in laryngeal cancer. Methods: One hundred and seventeen patients with laryngeal cancer who underwent MR examinations and received open surgery and neck dissection between January 2016 and December 2019 were included in this study. All patients were randomly divided into a training cohort (n=89) and test cohort (n=28) using computer-generated random numbers. Clinical characteristics and MR were collected. Radiological features were extracted from the MR images. Enhanced T1 and T2WI were selected for radiomics analysis, and the volume of interest was manually segmented from the Huiyihuiying radiomics cloud platform. The variance analysis (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimensionality of the radiomics features in the training cohort. Then, a radiomic signature was established. The clinical risk factors were screened by using ANOVA and multivariate logistic regression. A nomogram was generated using clinical risk factors and the radiomic signature. The calibration curve and receiver operator characteristic (ROC) curve were used to confirm the nomogram's performance in the training and test sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA). Furthermore, a testing cohort was used to validate the model. Results: The radiomics signature consisted of 21 features, and the nomogram model included the radiomics signature and the MR-reported lymph node status. The model showed good calibration and discrimination. The model yielded areas under the ROC curve (AUC) in the training cohort, specificity, and sensitivity of 0.930, 0.930 and 0.875. In the test cohort, the model yielded AUC, specificity and sensitivity of 0.883, 0.889 and 0.800. DCA indicated that the nomogram model was clinically useful. Conclusion: The MR-based radiomics nomogram model may be used to predict cervical lymph node metastasis of laryngeal cancer preoperatively. MR-based radiomics could serve as a potential tool to help clinicians make an optimal clinical decision.

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