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.

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound.

OBJECTIVES: To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS). METHODS: 527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists. RESULTS: In the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205). CONCLUSIONS: A CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Surveillance for malignant progression of LI-RADS version 2017 category 3/4 nodules using contrast-enhanced ultrasound.

OBJECTIVES: To identify the risk factors for predicting the malignant progression of LR-3/4 observations on the baseline and contrast-enhanced ultrasound (CEUS). METHODS: In total, 245 liver nodules assigned to LR-3/4 in 192 patients from January 2010 to December 2016 were followed up by baseline US and CEUS. The differences in the rate and time of progression to hepatocellular carcinoma (HCC) among subcategories (defined as P1-P7) of LR-3/4 in CEUS Liver Imaging Reporting and Data System (LI-RADS) were analyzed. The risk factors to predict progression to HCC were analyzed by univariate and multivariate Cox proportional hazard model analysis. RESULTS: A total of 40.3% of LR-3 nodules and 78.9% of LR-4 nodules eventually progressed to HCC. The cumulative incidence of progression was significantly higher for LR-4 than LR-3 (p < 0.001). The rate of progression was 81.2% in nodules with arterial phase hyperenhancement (APHE), 64.7% in nodules with late and mild washout, and 100% in nodules with both characteristics. The overall progression rate and median progression time of subcategory P1 nodules (LR-3a) were lower (38.0% vs. 47.6-100.0%) and later (25.1 months vs. 2.0-16.3 months) than those of other subcategories. The cumulative incidence of progression of LR-3a (P1), LR-3b (P2/3/4), and LR-4 (P5/6/7) categories were 38.0%, 52.9%, and 78.9%. The risk factors of HCC progression were Visualization score B/C, CEUS characteristics (APHE, washout), LR-4 classification, echo changes, and definite growth. CONCLUSION: CEUS is a useful surveillance tool for nodules at risk of HCC. CEUS characteristics, LI-RADS classification, and changes in nodules provide useful information for the progress of LR-3/4 nodules. CLINICAL RELEVANCE STATEMENT: CEUS characteristics, LI-RADS classification, and nodule changes provide important predictions for LR-3/4 nodule progression to HCC, which may stratify the risk of malignant progression to provide a more optimized and refined, more cost-effective, and time-efficient management strategy for patients. KEY POINTS: • CEUS is a useful surveillance tool for nodules at risk of HCC, CEUS LI-RADS successfully stratified the risks that progress to HCC. • CEUS characteristics, LI-RADS classification, and changes in nodules can provide important information on the progression of LR-3/4 nodules, which may be helpful for a more optimized and refined management strategy.

US LI-RADS in surveillance for recurrent hepatocellular carcinoma after curative treatment.

OBJECTIVES: To investigate the performance of US LI-RADS in surveillance for recurrent hepatocellular carcinoma (RHCC) after curative treatment. MATERIALS AND METHODS: This study enrolled 644 patients between January 2018 and August 2018 as a derivation cohort, and 397 patients from September 2018 to December 2018 as a validation cohort. The US surveillance after HCC curative treatment was performed. The US LI-RADS observation categories and visualization scores were analyzed. Four criteria using US LI-RADS or Alpha-fetoprotein (AFP) as the surveillance algorithm were evaluated. The sensitivity, specificity, and negative predictive value (NPV) were calculated. RESULTS: A total of 212 (32.9%) patients in derivation cohort and 158 (39.8%) patients in validation cohort were detected to have RHCCs. The criterion of US-2/3 or AFP ≥ 20 µg/L had higher sensitivity (derivation, 96.7% vs 92.9% vs 81.1% vs 90.6%; validation, 96.2% vs 90.5% vs 80.4% vs 89.9%) and NPV (derivation, 95.7% vs 93.3% vs 88.0% vs 91.8%; validation, 94.6% vs 89.4% vs 83.6% vs 89.0%), but lower specificity (derivation, 35.9% vs 48.2% vs 67.6% vs 51.9%; validation, 43.5% vs 52.7% vs 66.1% vs 54.0%) than criterion of US-2/3, US-3, and US-3 or AFP ≥ 20 µg/L. Analysis of the visualization score subgroups confirmed that the sensitivity (89.2-97.6% vs 81.0-83.3%) and NPV(88.4-98.0% vs 80.0-83.3%) of score A and score B groups were higher than score C group in criterion of US-2/3 in both two cohorts. CONCLUSIONS: In the surveillance for RHCC, US LI-RADS with AFP had a high sensitivity and NPV when US-2/3 or AFP ≥ 20 µg/L was considered a criterion. CLINICAL RELEVANCE STATEMENT: The criterion of US-2/3 or AFP ≥ 20 µg/L improves sensitivity and NPV for RHCC surveillance, which provides a valuable reference for patients in RHCC surveillance after curative treatment. KEY POINTS: • US LI-RADS with AFP had high sensitivity and NPV in surveillance for RHCC when considering US-2/3 or AFP ≥ 20 µg/L as a criterion. • After US with AFP surveillance, patients with US-2/3 or AFP ≥ 20 µg/L should perform enhanced imaging for confirmative diagnosis. Patients with US-1 or AFP < 20 µg/L continue to repeat US with AFP surveillance. • Patients with risk factors for poor visualization scores limited the sensitivity of US surveillance in RHCC.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions.

PURPOSE: To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs). MATERIAL AND METHODS: We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia. CEUS with SonoVue (Bracco Imaging) and SCCA combining CEUS and maximum value of elastography with < 20 kPa and > 90 kPa thresholds were used for the diagnosis of FLLs. The diagnostic performance of CEUS and SCCA was calculated and compared. RESULTS: A total of 171 FLLs were included, with 124 malignant FLLs and 47 benign FLLs. The area under curve (AUC), sensitivity, and specificity in detecting malignant FLLs were 0.83, 91.94%, and 74.47% for CEUS, respectively, and 0.89, 91.94%, and 85.11% for SCCA, respectively. The AUC of SCCA was significantly higher than that of CEUS (P = 0.019). Decision curves indicated that SCCA provided greater clinical benefits. The SCCA provided significantly improved prediction of clinical outcomes, with a net reclassification improvement index of 10.64% (P = 0.018) and integrated discrimination improvement of 0.106 (P = 0.019). For subgroup analysis, we divided the FLLs into a chronic-liver-disease group (n = 88 FLLs) and a normal-liver group (n = 83 FLLs) according to the liver background. In the chronic-liver-disease group, there were no differences between the CEUS-based and SCCA diagnoses. In the normal-liver group, the AUC of SCCA and CEUS in the characterization of FLLs were 0.89 and 0.83, respectively (P = 0.018). CONCLUSION: SCCA is a feasible tool for differentiating FLLs in patients with normal liver backgrounds. Further investigations are necessary to validate the universality of this algorithm.

Cardiovascular risk factors and breast cancer incidence in a large middle-aged cohort study.

BACKGROUND: Several studies have demonstrated that cardiovascular risk factors play a role in the etiology of breast cancer. However, the combined effect of cardiovascular risk factors on the risk of breast cancer is still uncertain. METHODS: Data from the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort of middle-aged women, were used to investigate the association of individual and combined cardiovascular risk factors with breast cancer. Cox proportional hazards models were applied to calculate the hazard ratio (HR) and 95% confidence intervals (CI). RESULTS: A total of 7501 women were included. During a mean follow-up of 19.7 years, 576 women were diagnosed with breast cancer. White women and premenopausal status were independently associated with increased risk of breast cancer. Of the individual cardiovascular risk factors, only obesity was independently associated with an increased risk of breast cancer (HR 1.29, 95% CI 1.04-1.61). Compared with women without cardiovascular risk factors, women having three or greater, but not those with fewer than three cardiovascular risk factors, had a significantly higher risk of developing breast cancer (HR 1.27, 95% CI 1.06-1.53). Subgroup analyses indicated that women with three or greater cardiovascular risk factors had higher risk of breast cancer among postmenopausal Black women, but not among premenopausal Black and White women. CONCLUSIONS: Combinations of cardiovascular risk factors are associated with increased risk of breast cancer in middle-aged women, especially in postmenopausal Black women. Joint interventions to modify cardiovascular risk factors could be used to prevent breast cancer in these higher-risk individuals.

Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing.

OBJECTIVES: To systematically assess the reproducibility of radiomics features from ultrasound (US) images during image acquisition and processing. MATERIALS AND METHODS: A standardized phantom was scanned to obtain US images. Reproducibility of radiomics features from US images, also known as ultrasomics features, was explored via (a) intra-US machine: changing the US acquisition parameters including gain, focus, and frequency; (b) inter-US machine: comparing three different scanners; (c) changing segmentation locations; and (d) inter-platform: comparing features extracted by the Ultrasomics and PyRadiomics algorithm platforms. Reproducible ultrasomics features were selected based on coefficients of variation. RESULTS: A total of 108 US images from three scanners were obtained; 5253 ultrasomics features including seven categories of features were extracted and evaluated for each US image. From intra-US machine analysis, 37.0-38.8% of features showed good reproducibility. From inter-US machine analysis, 42.8% (2248/5253) of features exhibited good reproducibility. From segmentation location analysis, 55.7-57.6% of features showed good reproducibility. No significant difference in the normalized feature ranges was found between the 100 features extracted by the Ultrasomics and PyRadiomics platforms with the same algorithm (p = 0.563). A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed, most of which were wavelet and shearlet features. CONCLUSIONS: Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. Wavelet and shearlet features showed the best reproducibility across all procedures. KEY POINTS: • Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. • A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed. • Wavelet and shearlet features showed the best reproducibility across all procedures.

Contrast-Enhanced Ultrasound-Based Nomogram: A Potential Predictor of Individually Postoperative Early Recurrence for Patients With Combined Hepatocellular-Cholangiocarcinoma.

PURPOSES: To evaluate the postsurgical prognostic implication of contrast-enhanced ultrasound (CEUS) for combined hepatocellular-cholangiocarcinoma (CHC). To build a CEUS-based early recurrence prediction classifier for CHC, in comparison with tumor-node-metastasis (TNM) staging. METHODS: The CEUS features and clinicopathological findings of each case were analyzed, and the Liver Imaging Reporting and Data System categories were assigned. The recurrence-free survival associated factors were evaluated by Cox proportional hazard model. Incorporating the independent factors, nomograms were built to estimate the possibilities of 3-month, 6-month, and 1-year recurrence and whose prognostic value was determined by time-dependent receiver operating characteristics, calibration curves, and hazard layering efficiency validation, comparing with TNM staging system. RESULTS: In the multivariable analysis, the levels of carbohydrate antigen 19-9, prothrombin time and total bilirubin, and tumor shape, the Liver Imaging Reporting and Data System category were independent factors for recurrence-free survival. The LR-M category showed longer recurrence-free survival than did the LR-4/5 category. The 3-month, 6-month, and 1-year area under the curves of the CEUS-clinical nomogram, clinical nomogram, and TNM staging system were 0.518, 0.552, and 0.843 versus 0.354, 0.240, and 0.624 (P = .048, .049, and .471) vs. 0.562, 0.545, and 0.843 (P = .630, .564, and .007), respectively. The calibration curves of the CEUS-clinical model at different prediction time pionts were all close to the ideal line. The CEUS-clinical model effectively stratified patients into groups of high and low risk of recurrence in both training and validation set, while the TNM staging system only works on the training set. CONCLUSIONS: Our CEUS-clinical nomogram is a reliable early recurrence prediction tool for hepatocellular-cholangiocarcinoma and helps postoperative risk stratification.

Inter-reader agreement of CEUS LI-RADS among radiologists with different levels of experience.

OBJECTIVES: To investigate the inter-reader agreement of contrast-enhanced ultrasound (CEUS) of Liver Imaging Reporting and Data System version 2017 (LI-RADS v2017) categories among radiologists with different levels of experience. MATERIALS AND METHODS: From January 2014 to December 2014, a total of 326 patients at high risk of hepatocellular carcinoma (HCC) who underwent CEUS were included in this retrospective study. All lesions were classified according to LI-RADS v2017 by six radiologists with different levels of experiences: two residents, two fellows, and two specialists. Kappa coefficient was used to assess consistency of LI-RADS categories and major features among radiologists with different levels of experience. The diagnostic performance of HCC was described by accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). RESULTS: Inter-reader agreement among radiologists of different experience levels was substantial agreement for arterial phase hyperenhancement, washout appearance, and early or late washout. Inter-reader agreement for LI-RADS categories was moderate to substantial. When LR-5 was used as criteria to determinate HCC, the AUC of LI-RADS for HCC was 0.67 for residents, 0.72 for fellows, and 0.78 for specialist radiologists. When compared between residents and specialists, accuracy, sensitivity, and AUC were significantly different (all p < 0.05). However, there were no significant differences in specificity, PPV, and NPV between the two groups. CONCLUSION: CEUS LI-RADS showed good diagnostic consistency among radiologists with different levels of experience, and consistency increased with experience levels. KEY POINTS: • The inter-reader agreement for LI-RADS categories was moderate to substantial agreement (κ, 0.60-0.80). • When compared between residents and specialists, accuracy, sensitivity, and AUC showed significantly different (all p < 0.05). However, there were no significant differences for specificity, PPV, and NPV between these two groups. • Among the radiologists with more than 1 year of experience, there was no significant difference in the diagnostic performance of HCC, suggesting that CEUS LI-RADS is a good standardized categorization system for high-risk patients.

Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.

BACKGROUND AND AIM: This study aims to construct a strategy that uses assistance from artificial intelligence (AI) to assist radiologists in the identification of malignant versus benign focal liver lesions (FLLs) using contrast-enhanced ultrasound (CEUS). METHODS: A training set (patients = 363) and a testing set (patients = 211) were collected from our institute. On four-phase CEUS images in the training set, a composite deep learning architecture was trained and tuned for differentiating malignant and benign FLLs. In the test dataset, AI performance was evaluated by comparison with radiologists with varied levels of experience. Based on the comparison, an AI assistance strategy was constructed, and its usefulness in reducing CEUS interobserver heterogeneity was further tested. RESULTS: In the test set, to identify malignant versus benign FLLs, AI achieved an area under the curve of 0.934 (95% CI 0.890-0.978) with an accuracy of 91.0%. Comparing with radiologists reviewing videos along with complementary patient information, AI outperformed residents (82.9-84.4%, P = 0.038) and matched the performance of experts (87.2-88.2%, P = 0.438). Due to the higher positive predictive value (PPV) (AI: 95.6% vs residents: 88.6-89.7%, P = 0.056), an AI strategy was defined to improve the malignant diagnosis. With the assistance of AI, radiologists exhibited a sensitivity improvement of 97.0-99.4% (P < 0.05) and an accuracy of 91.0-92.9% (P = 0.008-0.189), which was comparable with that of the experts (P = 0.904). CONCLUSIONS: The CEUS-based AI strategy improved the performance of residents and reduced CEUS's interobserver heterogeneity in the differentiation of benign and malignant FLLs.

Malignancy risk stratification and FNA recommendations for thyroid nodules: A comparison of ACR TI-RADS, AACE/ACE/AME and ATA guidelines.

OBJECTIVE: To compare diagnostic performance and malignancy risk stratification among guidelines set forth by the American Thyroid Association (ATA) in 2015, the American Association of Clinical Endocrinologists (AACE), the American College of Endocrinology (ACE) and the Association Medici Endocrinologi (AME) in 2016, and the American College of Radiology (ACR) in 2017. METHODS: The retrospective study was approved by the hospital ethics committee, and the informed consent requirement was waived. From October 2015 to March 2016, a total of 230 patients with 230 consecutive thyroid nodules were enrolled in this study. Each nodule was classified by one junior and one senior radiologist separately according to ACR TI-RADS, AACE/ACE/AME and ATA guidelines. The malignancy diagnostic performance and the number of FNA recommendations were pairwise compared among three guidelines using chi-square tests. RESULTS: Of the 230 thyroid nodules, 137 were malignant, and 93 were benign. However, 19.6% of the nodules (45 of 230) did not match any pattern using the ATA guidelines but with a high risk of malignancy (68.9%). The ACR TI-RADS derived the highest diagnostic performance, from both junior radiologist (AUC 0.815) and senior radiologist (AUC 0.864). The ACR guidelines also showed the greatest level of sensitivity (junior: 86.1%, senior: 94.9%), compared with AACE/ACE/AME and ATA guidelines. The number of thyroid nodules recommended to fine-needle aspiration (FNA) was the lowest (37.8%, 40.4%) by ACR TI-RADS, and meanwhile, the malignant detection rate within these nodules was highest (64.4%, 68.8%). CONCLUSIONS: The ACR guidelines present a higher level of diagnostic indicators and may offer a meaningful reduction in FNA recommendations with a higher malignancy detection rate.

Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma.

PURPOSE: To develop an ultrasound (US)-based radiomics score for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: Between January 1, 2012, and October 31, 2017, a total of 482 HCC patients who underwent contrast-enhanced ultrasound (CEUS) were retrospectively reviewed. The study population was divided into a training cohort (n = 341) and a validation cohort (n = 141) based on a cutoff time of January 1, 2016. Radiomics features were extracted from the grayscale US images of HCC. After features selection, a radiomics score was developed from the training cohort. The incremental value of the radiomics score to the clinic-pathological factors for MVI prediction was assessed in the validation cohort with respect to discrimination, calibration, and clinical usefulness. RESULTS: The US-based radiomics score consisted of six selected features. Multivariate logistic regression analysis showed that the radiomics score, alpha-fetoprotein (AFP), and tumor size were independent predictors of MVI. The radiomics nomogram (based on the three factors) showed better performance for MVI detection (area under the curve [AUC] 0.731[0.647, 0.815] than the clinical nomogram (based on AFP and tumor size) (0.634 [0.543, 0.724]) (p = 0.015). Both nomograms showed good calibration. Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION: The US-based radiomics score was an independent predictor of MVI in HCC. Combining the radiomics score with clinical factors improved the prediction efficacy. KEY POINTS: • Radiomics can be applied in US images. • US-based radiomics score was an independent predictor of MVI. • Radiomics nomogram incorporated with the radiomics score showed good performance for MVI prediction.

Comparison between M-score and LR-M in the reporting system of contrast-enhanced ultrasound LI-RADS.

OBJECTIVE: To develop a contrast-enhanced ultrasound (CEUS) M-score and compare it with LR-M in CEUS Liver Imaging Reporting and Data System (LI-RADS). METHODS: We retrospectively enrolled 105 consecutive high-risk patients with hepatocellular carcinoma (HCC) and 105 with intrahepatic cholangiocarcinoma (ICC). The subjects were selected by propensity score matching between November 2003 and December 2017. A CEUS M-score for predicting ICC was constructed based on specific CEUS features by the least absolute shrinkage and selection operator regularised regression. M-score was used to develop a modified CEUS LI-RADS. The diagnostic performance of the modified CEUS LI-RADS using M-score for diagnosing HCC and ICC was compared with American College of Radiology (ACR) CEUS LI-RADS using LR-M. RESULTS: The most useful features for ICC were as follows: poorly circumscribed (69.52%), rim enhancement (63.81%), early washout (92.38%), intratumoural vein (56.19%), obscure boundary of intratumoural non-enhanced area (57.14%), and marked washout (59.05%, all p < 0.001). For predicting ICC, the M-score had a higher specificity (88.57% vs. 63.81%) with lower sensitivity (89.52% vs. 95.24%) compared with LR-M. For diagnosing HCC, the sensitivity of modified LI-RADS (80.95%) was much higher than that of ACR LI-RADS (57.14%), but the specificity was lower (90.48% vs. 96.19%). The area under the curve (AUC) of modified LI-RADS (0.857) was much higher than that of ACR LI-RADS (0.767, p = 0.0001). The modified positive predictive value (PPV) of ACR LI-RADS and modified LI-RADS were 99.42% and 98.99%, respectively. CONCLUSIONS: The modified LI-RADS with M-score had higher sensitivity for diagnosing HCC and higher specificity for diagnosing ICC than ACR LI-RADS. KEY POINTS: • For predicting ICC, the M-score had a higher specificity (88.57% vs. 63.81%) with lower sensitivity (89.52% vs. 95.24%) compared with LR-M. • A CEUS M-score for predicting ICC consisted of more detailed CEUS features (poorly circumscribed, rim enhancement, early washout, intratumoural vein, obscure boundary of intratumoural non-enhanced area, and marked washout) was constructed. • For diagnosing HCC, the sensitivity of modified LI-RADS (80.95%) was much higher than that of ACR LI-RADS (57.14%), but the specificity was lower (90.48% vs. 96.19%). The modified positive predictive value (PPV) of ACR LI-RADS and modified LI-RADS were 99.42% and 98.99%, respectively.

Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.

OBJECTIVE: To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning. MATERIALS AND METHODS: This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-learning algorithms by calculating the area under the receiver-operator characteristic curve (AUC). RESULTS: ORF and CEMF features had better predictive power than conventional radiomics for liver fibrosis stage (both p < 0.01). CEMF features exhibited the highest diagnostic value for activity stage (both p < 0.05), and ORF had the best diagnostic value for steatosis stage (both p < 0.01). The machine-learning classifiers of adaptive boosting, random forest and support vector machine were found to be optimal algorithms with better (all mean AUCs = 0.85) and more stable performance (coefficient of variation = 0.01-0.02) for fibrosis staging than decision tree, logistic regression and neural network (mean AUC = 0.61-0.72, CV = 0.07-0.08). The multiparametric ultrasomics model achieved much better performance (mean AUC values of 0.78-0.85) than the features from a single modality in discriminating significant fibrosis (≥ F2). CONCLUSION: Machine-learning-based analysis of multiparametric ultrasomics can help improve the discrimination of significant fibrosis compared with mono or dual modalities. KEY POINTS: • Multiparametric ultrasomics has achieved much better performance in the discrimination of significant fibrosis (≥ F2) than the single modality of conventional radiomics, original radiofrequency and contrast-enhanced micro-flow. • Adaptive boosting, random forest and support vector machine are the optimal algorithms for machine learning.

Comparison of Real-Time Two-Dimensional and Three-Dimensional Contrast-Enhanced Ultrasound to Quantify Flow in an In Vitro Model: A Feasibility Study.

BACKGROUND This feasibility study aimed to compare real-time two-dimensional contrast-enhanced ultrasound (2D-CEUS) and three-dimensional contrast-enhanced ultrasound (3D-CEUS) to quantify flow in an in vitro model. MATERIAL AND METHODS Five polyvinyl chloride (PVC) tubes were used for the perfusion models and used SonoVue ultrasound contrast agent with a perfusion volume ratio of 1: 2: 4: 8: 16. The contrast was injected at a constant speed to compare the raw quantitative data of 2D-CEUS and 3D-CEUS at angles of 0°, 45°, and 90°. The coefficient of variation (CV) of the peak intensity (PI) in the model were compared and the correlations between weighted PI and perfusion volume were analyzed. RESULTS In the three angles used, real-time 3D-CEUS resulted in a more comprehensive view of the spatial relationships in the perfusion model. Using real-time 2D-CEUS, the mean CV was 0.92±0.36, and the mean CV in the real-time 3D-CEUS model was significantly less at 0.48±0.32 (p<0.001). Quantitative 3D-CEUS parameters showed a good correlation with those of 2D-CEUS with an r-value of 0.93 (p=0.02). The r-value of weighted PI and the perfusion ratio using 2D-CEUS was 0.66 (p=0.23) compared with values in 3D-CEUS of 0.84 (p=0.08). CONCLUSIONS The combination of real-time 3D-CEUS and quantitative analysis identified the spatial distribution of the changes in angle in the model, which was less influenced by sectional planes, and was more representative of the perfusion volume when compared with 2D-CEUS. Quantitative real-time 3D-CEUS requires in vivo studies to evaluate the potential role in the clinical evaluation of vascular perfusion of malignant tumors.

Feasibility and Measurement Value of Pancreatic 2-D Shear Wave Elastography in Healthy Adults: Evaluation, Influencing Factors, Reference Range, Measurement Stability and Reproducibility.

OBJECTIVE: The present study was aimed at assessing the success rate and measurement value, determining the influencing factors and reference range and examining the intra-operator stability and inter-operator reproducibility of pancreatic 2-D shear wave elastography (SWE) measurement in healthy adults. METHODS: In 2022, 387 healthy adults were prospectively recruited. Logistic regression and linear regression analyses were used to explore the factors influencing the success rate and the measurement value of pancreatic 2-D SWE measurement, respectively. A two-sided 95% reference range was estimated accordingly. The intraclass correlation coefficient was calculated to evaluate the intra-operator stability and inter-operator reproducibility of the pancreatic 2-D SWE measurement. RESULTS: The pancreatic body (89.6%) bore the highest while the tail (72.8%) bore the lowest success rate of pancreatic 2-D SWE measurement. Sex and body mass index (BMI) were the independent factors influencing measurement success rate in all three parts of the pancreas. Mean measurement values (Emean) were not the same in the three parts of the pancreas of the same participant. BMI and image depth were the independent factors influencing Emean in the pancreatic body, while region of interest depth and BMI were the only independent factors influencing Emean in the pancreatic head and tail, respectively. The intra-operator stability of pancreatic 2-D SWE measurement was found to be excellent, whereas its inter-operator reproducibility was poor to good. CONCLUSION: Pancreatic 2-D SWE is a reliable technique for evaluating pancreatic stiffness in healthy adults, but its success rate and measurement value are influenced by multiple factors.

A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma.

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.

Complementary Role of CEUS and CT/MR LI-RADS for Diagnosis of Recurrent HCC.

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.

Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.

Importance: To optimize the integration of artificial intelligence (AI) decision aids and reduce workload in thyroid nodule management, it is critical to incorporate personalized AI into the decision-making processes of radiologists with varying levels of expertise. Objective: To develop an optimized integration of AI decision aids for reducing radiologists' workload while maintaining diagnostic performance compared with traditional AI-assisted strategy. Design, Setting, and Participants: In this diagnostic study, a retrospective set of 1754 ultrasonographic images of 1048 patients with 1754 thyroid nodules from July 1, 2018, to July 31, 2019, was used to build an optimized strategy based on how 16 junior and senior radiologists incorporated AI-assisted diagnosis results with different image features. In the prospective set of this diagnostic study, 300 ultrasonographic images of 268 patients with 300 thyroid nodules from May 1 to December 31, 2021, were used to compare the optimized strategy with the traditional all-AI strategy in terms of diagnostic performance and workload reduction. Data analyses were completed in September 2022. Main Outcomes and Measures: The retrospective set of images was used to develop an optimized integration of AI decision aids for junior and senior radiologists based on the selection of AI-assisted significant or nonsignificant features. In the prospective set of images, the diagnostic performance, time-based cost, and assisted diagnosis were compared between the optimized strategy and the traditional all-AI strategy. Results: The retrospective set included 1754 ultrasonographic images from 1048 patients (mean [SD] age, 42.1 [13.2] years; 749 women [71.5%]) with 1754 thyroid nodules (mean [SD] size, 16.4 [10.6] mm); 748 nodules (42.6%) were benign, and 1006 (57.4%) were malignant. The prospective set included 300 ultrasonographic images from 268 patients (mean [SD] age, 41.7 [14.1] years; 194 women [72.4%]) with 300 thyroid nodules (mean [SD] size, 17.2 [6.8] mm); 125 nodules (41.7%) were benign, and 175 (58.3%) were malignant. For junior radiologists, the ultrasonographic features that were not improved by AI assistance included cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, and nodules smaller than 5 mm, whereas for senior radiologists the features that were not improved by AI assistance were cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, very hypoechoic nodules, nodules taller than wide, lobulated or irregular nodules, and extrathyroidal extension. Compared with the traditional all-AI strategy, the optimized strategy was associated with increased mean task completion times for junior radiologists (reader 11, from 15.2 seconds [95% CI, 13.2-17.2 seconds] to 19.4 seconds [95% CI, 15.6-23.3 seconds]; reader 12, from 12.7 seconds [95% CI, 11.4-13.9 seconds] to 15.6 seconds [95% CI, 13.6-17.7 seconds]), but shorter times for senior radiologists (reader 14, from 19.4 seconds [95% CI, 18.1-20.7 seconds] to 16.8 seconds [95% CI, 15.3-18.3 seconds]; reader 16, from 12.5 seconds [95% CI, 12.1-12.9 seconds] to 10.0 seconds [95% CI, 9.5-10.5 seconds]). There was no significant difference in sensitivity (range, 91%-100%) or specificity (range, 94%-98%) between the 2 strategies for readers 11 to 16. Conclusions and Relevance: This diagnostic study suggests that an optimized AI strategy in thyroid nodule management may reduce diagnostic time-based costs without sacrificing diagnostic accuracy for senior radiologists, while the traditional all-AI strategy may still be more beneficial for junior radiologists.

Prevalence of breast fibroadenoma in healthy physical examination population in Guangdong province of China: a cross-sectional study.

OBJECTIVE: To provide an accurate assessment of the prevalence of breast fibroadenoma in a large population and to confirm the diagnostic accuracy of ultrasound for fibroadenoma. DESIGN: This was a cross-sectional survey. SETTING: This research was conducted at Nanfang Hospital, Guangzhou, Guangdong, China. PARTICIPANTS: A total of 11 898 women aged 18-40 years who underwent breast screening between 1 January 2019 and 31 December 2019 were included in the fibroadenoma prevalence study. From 1 June 2019 to 31 December 2019, 342 breast lesions with pathology reports and preoperative ultrasound images were collected for diagnostic fibroadenoma testing (vs histological diagnostic testing). PRIMARY OUTCOME MEASURES: Pearson's χ2 test was performed to compare the prevalence of different lesions between age groups, and descriptive statistics were used to report the clinical characteristics of fibroadenoma. For ultrasound diagnosis, fibroadenoma was defined as a well-circumscribed lesion with round or oval shape, consisting of a homogeneously hypoechoic or isoechoic solid mass, located parallel to the chest wall with a smooth margin and no posterior shadowing. Diagnostic test results for breast fibroadenoma were stratified by diagnostic type (histological vs ultrasound). RESULTS: Of the women aged 18-40 years, 27.6% (3285/11 898) had an ultrasound diagnosis offibroadenoma. Of these, the prevalence of fibroadenoma was stable across age groups (p=0.14) and did not differ between the left and right sides of the breast. Almost two-thirds of women presented with a single fibroadenoma, and most fibroadenomas did not exceed 1 cm in size. The sensitivity and specificity for fibroadenoma were 97.0% (95% CI for sensitivity: 93.7% to 98.8%) and 91.4% (95% CI for specificity: 85.4% to 95.5%) for ultrasonography, respectively. CONCLUSIONS: The prevalence of fibroadenoma in South China is as high as 27.6%, and ultrasound could be used as a tool to diagnose fibroadenoma.