US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US 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. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Dynamic Contrast-Enhanced Ultrasound Radiomics for Hepatocellular Carcinoma Recurrence Prediction After Thermal Ablation.

PURPOSE: To develop a radiomics model based on dynamic contrast-enhanced ultrasound (CEUS) to predict early and late recurrence in patients with a single HCC lesion ≤ 5 cm in diameter after thermal ablation. PROCEDURES: We enrolled patients who underwent thermal ablation for HCC in our hospital from April 2004 to April 2017. Radiomics based on two branch convolution recurrent network was utilized to analyze preoperative dynamic CEUS image of HCC lesions to establish CEUS model, in comparison to the conventional ultrasound (US), clinical, and combined models. Clinical follow-up of HCC recurrence after ablation were taken as reference standard to evaluate the predicted performance of CEUS model and other models. RESULTS: We finally analyzed 318 patients (training cohort: test cohort = 255:63). The combined model showed better performance for early recurrence than CUES (in training cohort, AUC, 0.89 vs. 0.84, P < 0.001; in test cohort, AUC, 0.84 vs. 0.83, P = 0.272), US (P < 0.001), or clinical model (P < 0.001). For late recurrence prediction, the combined model showed the best performance than the CEUS (C-index, in training cohort, 0.77 vs. 0.76, P = 0.009; in test cohort, 0.77 vs. 0.68, P < 0.001), US (P < 0.001), or clinical model (P < 0.001). CONCLUSIONS: The CEUS model based on dynamic CEUS radiomics performed well in predicting early HCC recurrence after ablation. The combined model combining CEUS, US radiomics, and clinical factors could stratify the high risk of late recurrence.

Intraprocedural computed tomography/magnetic resonance-contrast-enhanced ultrasound fusion imaging improved thermal ablation effect of hepatocellular carcinoma: Comparison with conventional ultrasound.

AIM: To retrospectively compare the treatment effect of intraprocedural computed tomography/magnetic resonance-contrast-enhanced ultrasound (CT/MR-CEUS) fusion imaging (FI) with that of conventional ultrasound (US) in the guidance and assessment of thermal ablation of hepatocellular carcinoma (HCC). METHODS: The FI group (112 patients with 129 HCC) was treated between April 2010 and December 2012, whereas the US group (83 patients with 90 HCC) was treated between January 2008 and March 2010. Either CT/MR-CEUS FI or US was used to guide puncture, provide immediate assessment, and guide supplementary ablation. Technical efficacy, cumulative local tumor progression rate (LTP), recurrence-free survival (RFS), and overall survival (OS) were evaluated and compared during follow-up. Technical success rate of CT/MR-CEUS FI was also recorded. RESULTS: Technical efficacy was significantly higher in the FI group than in the US group (100% vs. 86.7%, P < 0.001). The 1-, 2-, 3-, 4-, 5-, and 6-year cumulative LTP rates in the FI group were significantly lower than in the US group (3.8%, 4.9%, 6.0%, 6.0%, 7.2%, and 7.2% vs. 16.9%, 20.1%, 25%, 25%, 25%, and 25%, respectively; P < 0.001); RFS and OS were significantly higher in the FI group than in the US group (P = 0.027 and P = 0.049, respectively). The technical success rate of FI was 85.3%. CONCLUSIONS: Intraprocedural CT/MR-CEUS FI improved the treatment effect of thermal ablation of HCC by immediately assessing treatment response and guiding supplementary ablation relative to those resulting from the use of conventional US.