Objective We aimed to propose an automatic method based on Support Vector Machine (SVM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to segment the tumor lesions of head and neck cancer (HNC). Materials and Methods 120 DCE-MRI samples were collected. Five curve features and two principal components of the normalized time-intensity curve (TIC) in 80 samples were calculated as the dataset in training three SVM classifiers. The other 40 samples were used as the testing dataset. The area overlap measure (AOM) and the corresponding ratio (CR) and percent match (PM) were calculated to evaluate the segmentation performance. The training and testing procedure was repeated for 10 times, and the average performance was calculated and compared with similar studies. Results Our method has achieved higher accuracy compared to the previous results in literature in HNC segmentation. The average AOM with the testing dataset was 0.76 ± 0.08, and the mean CR and PM were 79 ± 9% and 86 ± 8%, respectively. Conclusion With improved segmentation performance, our proposed method is of potential in clinical practice for HNC.

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Background: This study aimed to analyze the value of color Doppler ultrasound in the diagnosis of thyroid nodules.Methods: We searched the PubMed, Web of Science, Embase, and Cochrane Library databases for randomized controlled trials (RCTs) on using color Doppler ultrasound, thyroid nodules, thyroid tumors, and Doppler ultrasound to diagnose the thyroid nodules. The outcome indicators in the articles had to include the numbers of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). Subsequently, the Jadad tool was adopted to evaluate the quality of the included articles, and Review Manager 5.3 software was used to conduct a meta-analysis of the experimental data.Results: A total of eight suitable articles were selected. The results showed that the estimated sensitivity and specificity of color Doppler ultrasound for the diagnostic of thyroid nodules were 0.46-0.89 and 0.00-1.00, respectively. The pooled estimate of sensitivity for the different articles was 0.71 [95% confidence interval (CI): 0.46-0.89], and the pooled estimate of specificity was 0.77 (95% CI: 0.00-1.00). The area under the summary receiver operating characteristic (SROC) curve (AUC) was 0.917, which was larger than 0.9, signifying high diagnostic accuracy. This suggests that color doppler ultrasound can realize the clinical diagnosis of thyroid nodules.Discussion: In summary, the results of this study could provide a clinical data for the promotion and application of color Doppler ultrasound in the clinical diagnosis of thyroid nodules, as well as further reliable data for follow-up clinical research on the diagnosis and treatment of thyroid nodules.

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