Fan Xiao's research while affiliated with Chinese PLA General Hospital (301 Hospital) and other places

Objective To evaluate potential improvements in the diagnosis of thyroid nodules when conventional ultrasound (US) is combined with contrast-enhanced US (CEUS). Methods We recruited 515 participants with 323 malignant and 192 benign nodules, who underwent both US and CEUS examinations at 8 different medical centers in China between October 2020 and October 2021. We assessed the malignancy of thyroid nodules in US using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS). Diagnostic criteria for US and US + CEUS were developed by investigators based on evaluations of sonographic features. Using multivariate logistic regression and receiver operating characteristic (ROC) analysis, we compared diagnostic performance between the 2 methods based on criteria identified by investigators and via statistical models. Results On the basis of diagnostic criteria identified by investigators, we measured statistically significant differences in area under the curve (AUC) values between ACR TIRADS (0.83) and CEUS TIRADS (0.87; P < .001). On the basis of diagnostic regression models, we found statistically significant differences in AUC values between US (0.76) and US + CEUS (0.84; P = .001). Models based on US + CEUS outperformed those based on US alone (Akaike information criterion of 347.7 and significant improvement in integrated discrimination). These results were confirmed by similar analyses applied to a validation cohort. Conclusion The accuracy of conventional US for differentiating between benign and malignant thyroid nodules can be improved by combining this approach with CEUS.

Objective: To investigate whether the feeding artery (FA) feature can aid in discriminating small hepatocellular carcinoma (HCC) using the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) from precancerous lesions. Methods: Between June 2017 and May 2021, a total of 347 patients with 351 precancerous liver lesions or small HCCs who underwent CEUS were enrolled. Two independent radiologists assigned LI-RADS categories to all lesions and assessed the presence of the FA feature, which was used as an ancillary feature to either upgrade or downgrade the LI-RADS category. The diagnostic performance of CEUS LI-RADS, both with and without the FA feature, was evaluated based on accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: The FA feature was found to be more prevalent in HCC (85.54%, p < 0.001) than in regenerative nodules (RNs, 29.73%), low-grade dysplastic nodules (LGDNs, 33.33%), and high-grade dysplastic nodules (HGDNs, 55.26%). Furthermore, the presence of arterial phase hyperenhancement (APHE), washout (WO), and FA in liver nodules was associated with a higher expression of GPC-3 and Ki-67 compared to the group without these features (p < 0.001). After adjusting, the sensitivity and accuracy of LR-5 for HCC improved from 68.67% (95%CI: 62.46%, 74.30%) to 77.51% (95%CI: 71.72%, 82.44%) and from 69.23% (95%CI: 64.11%, 74.02%) to 73.79% (95%CI: 68.86%, 78.31%), respectively. Conclusion: The FA feature is a valuable feature for distinguishing small HCC and precancerous lesions and could be added as a possible ancillary feature in CEUS LI-RADS which was backed up by biomarkers. Clinical relevance statement: The presence of a feeding artery is a valuable imaging feature in the differentiation of HCC and precancerous lesions. Incorporating this characteristic in the CEUS LI-RADS can enhance the diagnostic ability. Key points: • Feeding artery is more frequent in HCC than in regenerative nodules, low-grade dysplastic nodules, and high-grade dysplastic nodules. • Feeding artery feature is a valuable ancillary feature for CEUS LI-RADS to differentiate regenerative nodules, low-grade dysplastic nodules, high-grade dysplastic nodules, and HCC. • The existence of feeding artery, arterial phase hyperenhancement, and washout is associated with more GPC-3 positive expression and higher Ki-67 expression than the group without these features.

Background CEUS LI-RADS (Contrast Enhanced Ultrasound Liver Imaging Reporting and Data System) has good diagnostic efficacy for differentiating hepatic carcinoma (HCC) from solid malignant tumors. However, it can be problematic in patients with both chronic hepatitis B and extrahepatic primary malignancy. We explored the diagnostic performance of LI-RADS criteria and CEUS-based machine learning (ML) models in such patients. Methods Consecutive patients with hepatitis and HCC or liver metastasis (LM) who were included in a multicenter liver cancer database between July 2017 and January 2022 were enrolled in this study. LI-RADS and enhancement features were assessed in a training cohort, and ML models were constructed using gradient boosting, random forest, and generalized linear models. The diagnostic performance of the ML models was compared with LI-RADS in a validation cohort of patients with both chronic hepatitis and extrahepatic malignancy. Results The mild washout time was adjusted to 54 s from 60 s, increasing accuracy from 76.8 to 79.4%. Through feature screening, washout type II, rim enhancement and unclear border were identified as the top three predictor variables. Using LI-RADS to differentiate HCC from LM, the sensitivity, specificity, and AUC were 68.2%, 88.6%, and 0.784, respectively. In comparison, the random forest and generalized linear model both showed significantly higher sensitivity and accuracy than LI-RADS (0.83 vs. 0.784; all P < 0.001). Conclusions Compared with LI-RADS, the random forest and generalized linear model had higher accuracy for differentiating HCC from LM in patients with chronic hepatitis B and extrahepatic malignancy.

Background Current guidelines recommend the use of conventional US for risk stratification and management of thyroid nodules. However, fine-needle aspiration (FNA) is often recommended in benign nodules. Purpose To compare the diagnostic performance of multimodality US (including conventional US, strain elastography, and contrast-enhanced US [CEUS]) with the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) in the recommendation of FNA for thyroid nodules to reduce unnecessary biopsies. Materials and Methods In this prospective study, 445 consecutive participants with thyroid nodules from nine tertiary referral hospitals were recruited between October 2020 and May 2021. With univariable and multivariable logistic regression, the prediction models incorporating sonographic features, evaluated with interobserver agreement, were constructed and internally validated with bootstrap resampling technique. In addition, discrimination, calibration, and decision curve analysis were performed. Results A total of 434 thyroid nodules confirmed at pathologic analysis (259 malignant thyroid nodules) in 434 participants (mean age, 45 years ± 12 [SD]; 307 female participants) were included. Four multivariable models incorporated participant age, nodule features at US (proportion of cystic components, echogenicity, margin, shape, punctate echogenic foci), elastography features (stiffness), and CEUS features (blood volume). In recommending FNA in thyroid nodules, the highest area under the receiver operating characteristic curve (AUC) was 0.85 (95% CI: 0.81, 0.89) for the multimodality US model, and the lowest AUC was 0.63 (95% CI: 0.59, 0.68) for TI-RADS (P < .001). At the 50% risk threshold, 31% (95% CI: 26, 38) of FNA procedures could be avoided with multimodality US compared with 15% (95% CI: 12, 19) with TI-RADS (P < .001). Conclusion Multimodality US had better performance in recommending FNA to avoid unnecessary biopsies than the TI-RADS. Clinical trial registration no. NCT04574258 © RSNA, 2023 Supplemental material is available for this article.