JOURNAL CLUB: The Warthin Tumor Score: A Simple and Reliable Method to Distinguish Warthin Tumors From Pleomorphic Adenomas and Carcinomas.

OBJECTIVE: The objective of this article is to propose a Warthin tumor (WT) score to distinguish WTs from other parotid tumors. MATERIALS AND METHODS: The study included 78 patients with 92 histologically proven parotid tumors, including 42 WTs, 30 pleomorphic adenomas (PMAs), and 20 carcinomas. Echo-planar DW images were acquired. The WT score, which comprised the mean apparent diffusion coefficient (ADCM) and the SD of the ADC (ADCSD) of tumors, patient age, and patient sex, was used to predict WTs. The diagnostic performance of the WT score was evaluated using ROC analyses. Statistical significance was denoted by p < 0.05. RESULTS: With the use of optimized criteria, including an ADCM less than or equal to 1.016 × 10-3 mm2/s (WT score, 1), an ADCSD less than or equal to 0.1171 × 10-3 mm2/s (WT score, 1), patient age older than 49 years (WT score, 1), and male sex (WT score, 1), a WT score greater than 2 had a sensitivity, specificity, positive negative value, negative predictive value, and accuracy of 85.7%, 100.0%, 100.0%, 89.3%, and 93.4%, respectively. CONCLUSION: The WT score allows parotid WTs to be distinguished from PMAs and carcinomas with high accuracy.

To Explore Intracerebral Hematoma with a Hybrid Approach and Combination of Discriminative Factors.

OBJECTIVES: To find discriminative combination of influential factors of Intracerebral hematoma (ICH) to cluster ICH patients with similar features to explore relationship among influential factors and 30-day mortality of ICH. METHODS: The data of ICH patients are collected. We use a decision tree to find discriminative combination of the influential factors. We cluster ICH patients with similar features using Fuzzy C-means algorithm (FCM) to construct a support vector machine (SVM) for each cluster to build a multi-SVM classifier. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality. RESULTS: The two influential factors chosen to split the decision tree are Glasgow coma scale (GCS) score and Hematoma size. FCM algorithm finds three centroids, one for high danger group, one for middle danger group, and the other for low danger group. The proposed approach outperforms benchmark experiments without FCM algorithm to cluster training data. CONCLUSIONS: It is appropriate to construct a classifier for each cluster with similar features. The combination of factors with significant discrimination as input variables should outperform that with only single discriminative factor as input variable.