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PURPOSE: Many Thyroid Imaging Reporting and Data System (TI-RADS) category 3-4 nodules are benign. Our study aimed to add virtual touch tissue imaging (VTI) to TI-RADS using two methods, namely conservative and aggressive, and to explore which method had better diagnostic performance and which method avoided more unnecessary biopsies. METHODS: From January 2016 to December 2021, we included 121 thyroid nodules classified as TI-RADS category 3-4 in 115 consecutive patients in this retrospective study. This study used the reference standard for pathological diagnosis by surgical resection or biopsy. The diagnostic performance of the different methods was evaluated and compared by receiver operating characteristic (ROC) and area under the ROC curve (AUC). RESULTS: In this study, the aggressive approach had the best diagnostic performance among TI-RADS alone, the conservative approach, and the aggressive approach (AUC: 0.863 versus 0.598, P = 0.0007; 0.863 versus 0.755, P = 0.0067). When we used an aggressive approach, 75.44% (43/57) of the 57 false-positive nodes diagnosed by TI-RADS were appropriately downgraded from TI-RADS category 4 to category 3, avoiding unnecessary biopsies. CONCLUSION: VTI improves the diagnostic performance of TI-RADS. The aggressive approach of combining the TI-RADS with VTI would help reduce unnecessary biopsies. [ABSTRACT FROM AUTHOR]

To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules.Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating characteristic (ROC) curves.Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P <  0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880.Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate the importance of color-map virtual touch tissue imaging (CMV) in assisting Breast Imaging Reporting and Data Systems (BI-RADS) in diagnosing malignant breast lesions. METHODS: A dataset included 134 patients and 146 breast lesions was assembled. All patients underwent biopsy or surgical excision of breast lesions, and pathological results were obtained. All patients with breast lesions also underwent conventional ultrasound (US) and CMV. Each lesion was assigned a CMV score based on the color pattern of the lesion and surrounding breast tissue and a BI-RADS classification rating based on US characteristics. We compared the diagnostic performance of using BI-RADS and CMV separately and their combination. RESULTS: BI-RADS (odds ratio [OR]: 3.665; 95% confidence interval [CI]: 2.147, 6.258) and CMV (OR: 6.616; 95% CI: 2.272, 19.270) were independent predictors of breast malignancy (all P < 0.05). The area under the receiver operating characteristic curves (AUC) for either CMV or BI-RADS alone was inferior to that of the combination (0.877 vs. 0.962; 0.938 vs. 0.962; all P < 0.05). CONCLUSIONS: The performance of BI-RADS in diagnosing breast lesions is significantly improved by combining CMV. Therefore, we recommend CMV as an adjunct to BI-RADS. [ABSTRACT FROM AUTHOR]

Background: Conventional ultrasound (US) is the most widely used imaging test for thyroid nodule surveillance.Objective: We used the color-coded virtual touch tissue imaging (VTI) in the Acoustic Radiation Force Impulse (ARFI) technique to assess the hardness of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) TR3-5 nodules. The ability of color-coded VTI (CV) to discriminate between benign and malignant nodules was investigated.Methods: In this retrospective study, US and CV were performed on 211 TR3-5 thyroid lesions in 181 consecutive patients. All nodules were operated on to obtain pathological results. A multivariate logistic regression model was chosen to integrate the data obtained from the US and CV.Results: The area under the receiver operating characteristic (ROC) curve for the model was 0.945 (95% CI, 0.914 to 0.976). The cutoff value of predictive probability for diagnosing malignant thyroid nodules was 10.64%, the sensitivity was 94.43%, and the specificity was 83.12%. Through comparing with US and CV, respectively, it had been observed that the regression model had the best performance (all P< 0.001). However, when the US was compared with CV, the difference was not significant (P= 0.3304).Conclusions: A combination of US and CV should be recommended for suspected malignant thyroid nodules in clinical practice. [ABSTRACT FROM AUTHOR]

BACKGROUND: Researchers have evaluated the virtual touch tissue imaging (VTI) value in the diagnosis of breast lesions, mostly based on gray-scale. PURPOSE: This study aimed to evaluate the value of color-map virtual touch tissue imaging (CMV) in the diagnosis of breast lesions. METHODS: We retrospectively analyzed the VTI images of 55 breast lesions in 49 female patients who underwent an examination of breast lesions in our hospital from January 2019 to December 2019. The pathological results were taken as the gold standard. The receiver operating characteristic (ROC) curve of CMV was analyzed, and its diagnostic performance was evaluated. Weighted Kappa (k) statistics were used to assess the inter-observer agreement for CMV. RESULTS: A total of 55 breast lesions were included, including 19 malignant lesions and 36 benign lesions. Multivariate analysis showed that patients with higher CMV scores (P = 0.014, odds ratio [OR] = 13.667, 95% confidence interval = 1.702–109.773) were independent predictors of breast cancer. The sensitivity, specificity, and the area under curve (AUC) of CMV were 94.47%, 72.22%, and 0.912. The CMV's inter-observer agreement was almost perfect among radiologists with different work experience (k = 0.854, standard error = 0.049, 95% CI = 0.758–0.950). CONCLUSIOS: CMV has high accuracy and repeatability in the diagnosis of malignant breast lesions. [ABSTRACT FROM AUTHOR]

Purpose: The value of virtual touch tissue imaging (VTI) with support of Image-Pro Plus (IPP) for diagnosing malignant thyroid tumors was assessed in the present study. Methods: In this retrospective study, we enrolled 160 patients with 198 thyroid nodules. TI-RADS, VTI grade, and VTI with support of IPP (VTI-IPP) were underwent for each nodule. With the pathological diagnosis as the gold standard, the receiver-operating characteristic curve (ROC) was drawn to evaluate the diagnostic performance of VTI-IPP, VTI, TI-RADS, VTI-IPP combinate with TI-RADS in thyroid carcinoma. Results: VTI-IPP score >2, VTI score >3, TI-RADS score >1, and VTI-IPP combine with TI-RADS score >4 expressed the highest diagnostic value for malignant thyroid nodules, the areas under the curve (AUC) were 0.939, 0.905, 0.925, and 0.967, respectively. The combination indicated the largest AUC, compared with VTI-IPP and TI-RADS, respectively (P = 0.0054 and 0.0009). The performance of VTI-IPP in diagnosing thyroid carcinomas was better than VTI (P = 0.0321). Conclusion: Compare with VTI, VTI-IPP exhibited more excellent value in distinguishing between benign and malignant thyroid nodules. The value of malignant thyroid nodules diagnosis can be improved when VTI-IPP combines with TI-RADS. [ABSTRACT FROM AUTHOR]