Characterization of parotid tumors is important for treatment planning and prognosis, and parotid tumor discrimination has recently been developed at the molecular level. The aim of the present study was to establish a machine learning (ML) predictive model based on multiparametric traditional multislice CT (MSCT) radiomic and clinical data analysis to improve the accuracy of differentiation among pleomorphic adenoma (PA), Warthin tumor (WT) and parotid carcinoma (PCa). A total of 345 patients (200 with WT, 91 with PA and 54 with PCa) with pathologically confirmed parotid tumors were retrospectively enrolled from five independent institutions between January 2010 and May 2019. A total of 273 patients recruited from institutions 1, 2 and 3 were randomly assigned to the training model; the independent validation set consisted of 72 patients treated at institutions 1, 4 and 5. Data were investigated using a linear discriminant analysis-based ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy of the predictive model was compared with histopathological findings as reference results. This classifier achieved a satisfactory performance for the discrimination of PA, WT and PCa, with a total accuracy of 82.1% in the training cohort and 80.5% in the validation cohort. In conclusion, ML-based multiparametric traditional MSCT radiomics can improve the accuracy of differentiation among PA, WT and PCa. The findings of the present study should be validated by multicenter prospective studies using completely independent external data.

Underwater pipeline transportation, as an important means of natural gas transportation, will cause great economic loss and environmental pollution in case of leakage damage. Aiming at improving the accuracy of pipeline leakage monitoring research, a method was proposed to monitor the leakage process of underwater natural gas pipelines using distributed optical fiber acoustic sensing technology. In this paper, a processing algorithm combining empirical modal decomposition method and wavelet decomposition reconstruction is used to extract the signal frequency domain features. The experimental results show that the frequency domain amplitude of the vibration signal gradually grows as the leakage orifice diameter and internal pipe pressure increase, and the standard deviation of the vibration signal of the pipeline exhibits a quadratic fitting relationship with the size of the leak aperture. The method proposed in this paper has a minimum leak aperture identification error of 7.2%, which can effectively improve the pipeline leak monitoring accuracy.