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Background: The diagnosis of thyroid cancer and distinguishing benign from malignant thyroid nodules by junior radiologists can be challenging. This study aimed to develop a computer-aided diagnosis (CAD) system based on the Thyroid Imaging Reporting and Data System (TI-RADS) to distinguish benign from malignant thyroid nodules by analyzing ultrasound images to improve the diagnostic performance of junior radiologists. Material/Methods: A modified TI-RADS based on a convolutional neural network (CNN) was used to develop the CAD system. This retrospective study reviewed 789 thyroid nodules from 695 patients and included radiologists with different diagnostic experience. Five study groups included the CAD group, the junior radiologist group, the intermediate-level radiologist group, the senior radiologist group, and the group in which the junior radiologist used the CAD system. The ultrasound findings were reviewed and compared with the histopathology diagnosis. Results: The CAD system for the diagnosis of thyroid cancer showed an accuracy of 80.35%, a sensitivity of 80.64%, a specificity of 80.13%, a positive predictive value (PPV) of 76.02%, a negative predictive value (NPV) of 84.12%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.87. The accuracy of the junior radiologists in diagnosing thyroid cancer using CAD was similar to that of intermediate-level radiologists (79.21% vs. 77.57%; P=0.427). Conclusions: The use of ultrasound CAD based on the TI-RADS showed potential for distinguishing between benign and malignant thyroid nodules and improved the diagnostic performance of junior radiologists.

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BackgroundLiver biopsy is the criterion standard for diagnosing liver fibrosis, but it is not widely used to monitor liver fibrosis because of the invasiveness, risk of complications, and sample errors. Therefore, it is necessary to involve other techniques to monitor liver fibrosis or cirrhosis during clinical practice. The objective was to explore noninvasive indicators to predict advanced liver fibrosis in autoimmune hepatitis (AIH) patients.MethodsA total of 45 AIH patients and 47 healthy controls were recruited to this retrospective study. Complete blood count and liver function tests were performed for all subjects. AIH patients were divided into “no/minimal fibrosis” group and “advanced fibrosis” group based on liver biopsy.ResultsAIH patients demonstrated significantly higher monocytes, MCV, RDW‐CV, RDW‐SD, NLR, RDW‐CV/PLT, RDW‐SD/PLT, TBIL, DBIL, GLB, ALT, AST, GGT, ALP, and GPR and lower WBC, neutrophils, lymphocytes, RBC, HGB, HCT, LMR, TP, ALB, and AAR compared with healthy controls. Patients with advanced fibrosis showed remarkably higher RDW‐CV, RDW‐SD, RDW‐CV/PLT, RDW‐SD/PLT, AAR, and FIB‐4 and lower RBC, PLT, PCT, and ALB compared with the no/minimal fibrosis group. Logistic regression analysis showed that RDW‐SD/PLT was an independent risk factor for advanced fibrosis with an OR (95% CI) of 2.647 (1.383‐5.170). Receiver operating characteristic (ROC) analysis revealed that RDW‐SD, RDW‐CV/PLT, RDW‐SD/PLT, FIB‐4, and AAR had an area under the ROC curve (AUC) above 0.700 and RDW‐SD/PLT had the largest AUC of 0.785 with a cutoff value of 0.239.ConclusionRDW‐SD, RDW‐CV/PLT, RDW‐SD/PLT, FIB‐4, and AAR were excellent noninvasive biomarkers and RDW‐SD/PLT was an independent risk factor for predicting advanced fibrosis in AIH patients.