A sequential algorithm combining the noninvasive tests Fibrosis-4 Index (FIB-4) and a US deep learning (DL) model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone.

Purpose: We retrospectively compared the diagnostic performance of contrast-enhanced ultrasonography (CEUS) and contrast-enhanced computer tomography–magnetic resonance imaging (CT/MRI) for recurrent hepatocellular carcinoma (HCC) after curative treatment. Materials and methods: After curative treatment with 421 ultrasound (US) detected lesions, 303 HCC patients underwent both CEUS and CT/MRI. Each lesion was assigned a Liver Imaging Reporting and Data System (LI-RADS) category according to CEUS and CT/MRI LI-RADS. Receiver-operating characteristic (ROC) curves were computed to determine the optimal diagnosis algorithms for CEUS, CT and MRI. The diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were compared between CEUS and CT/MRI. Results: Among the 421 lesions, 218 were diagnosed as recurrent HCC, whereas 203 lesions were diagnosed as benign. In recurrent HCC, CEUS detected more arterial hyperenhancement (APHE) and washout than CT and more APHE than MRI. CEUS yielded better diagnostic performance than CT (AUC: 0.981 vs. 0.958) (p = 0.024) comparable diagnostic performance to MRI (AUC: 0.952 vs. 0.933) (p > 0.05) when using their optimal diagnostic criteria. CEUS missed 12 recurrent HCCs, CT missed one, and MRI missed none. The detection rate of recurrent HCC on CEUS (94.8%, 218/230) was lower than that on CT/MRI (99.6%, 259/260) (p = 0.001). Lesions located on the US blind spots and visualization score C would hinder the ability of CEUS to detect recurrent HCC. Conclusion: CEUS demonstrated excellent diagnostic performance but an inferior detection rate for recurrent HCC. CEUS and CT/MRI played a complementary role in the detection and characterization of recurrent HCC.

Background: The imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability. This study aimed to establish and confirm an ultrasomics model for differentiating CHC from HCC. Methods: Between 2004 and 2016, we retrospectively identified 53 eligible CHC patients and randomly included 106 eligible HCC patients with a ratio of HCC:CHC = 2:1, all of whom were categorized according to Contrast-Enhanced (CE) ultrasonography (US) Liver Imaging Reporting and Data System (LI-RADS) version 2017. The model based on ultrasomics features of CE US was developed in 74 HCC and 37 CHC and confirmed in 32 HCC and 16 CHC. The diagnostic performance of the LI-RADS or ultrasomics model was assessed by the area under the curve (AUC), accuracy, sensitivity and specificity. Results: In the entire and validation cohorts, 67.0% and 81.3% of HCC cases were correctly assigned to LR-5 or LR-TIV contiguous with LR-5, and 73.6% and 87.5% of CHC cases were assigned to LR-M correctly. Up to 33.0% of HCC and 26.4% of CHC were misclassified by CE US LI-RADS. A total of 90.6% of HCC as well as 87.5% of CHC correctly diagnosed by the ultrasomics model in the validation cohort. The AUC, accuracy, sensitivity of the ultrasomics model were higher though without significant difference than those of CE US LI-RADS in the validation cohort. Conclusion: The proposed ultrasomics model showed higher ability though the difference was not significantly different for differentiating CHC from HCC, which may be helpful in clinical diagnosis.

Objectives: This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. Methods: This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images—grayscale ultrasound, arterial phase, portal venous phase and delayed phase—were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. Results: The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). Conclusion: These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. Advances in knowledge: CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).

The adaptive dual-task deep learning model, ThyNet-S, demonstrated exceptional performance in enhancing thyroid cancer US screening efficiency and improved clinical decision-making.