Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.

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.

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.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an end-to-end convolutional neural network model. Results A total of 1161 US images of thyroid nodules (498 benign, 663 malignant) from 725 patients (mean age, 42.2 years ± 14.1 [SD]; 516 women) were evaluated. ChatGPT 4.0 and Bard displayed substantial to almost perfect intra-LLM agreement (κ range, 0.65-0.86 [95% CI: 0.64, 0.86]), while ChatGPT 3.5 showed fair to substantial agreement (κ range, 0.36-0.68 [95% CI: 0.36, 0.68]). ChatGPT 4.0 had an accuracy of 78%-86% (95% CI: 76%, 88%) and sensitivity of 86%-95% (95% CI: 83%, 96%), compared with 74%-86% (95% CI: 71%, 88%) and 74%-91% (95% CI: 71%, 93%), respectively, for Bard. Moreover, with ChatGPT 4.0, the image-to-text-LLM strategy exhibited an AUC (0.83 [95% CI: 0.80, 0.85]) and accuracy (84% [95% CI: 82%, 86%]) comparable to those of the human-LLM interaction strategy with two senior readers and one junior reader and exceeding those of the human-LLM interaction strategy with one junior reader. Conclusion LLMs, particularly integrated with image-to-text approaches, show potential in enhancing diagnostic medical imaging. ChatGPT 4.0 was optimal for consistency and diagnostic accuracy when compared with Bard and ChatGPT 3.5. © RSNA, 2024 Supplemental material is available for this article.

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.

Development and Assessment of Nomogram Based on AFP Response for Patients with Unresectable Hepatocellular Carcinoma Treated with Immune Checkpoint Inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have been increasingly used to treat hepatocellular carcinoma (HCC). Prognostic biomarkers are an unmet need. We aimed to develop a prognostic nomogram for patients with unresectable HCC receiving ICIs therapy. METHODS: A total of 120 patients with unresectable HCC receiving ICIs treatment were enrolled in this study. Patients were randomly divided into a training set (n = 84) and a validation set (n = 36) in a 7:3 ratio. Clinical characteristics were retrospectively analyzed. Serum α-fetoprotein protein (AFP) response was defined as a decline of ≥20% in AFP levels within the initial eight weeks of treatment. Univariable and multivariable Cox analyses were used to select relevant variables and construct the nomogram. The areas under the receiver operating characteristic curves (AUCs) were used to determine the performance of the model. Kaplan-Meier analysis with the log-rank test was used to compare different risk groups. RESULTS: The median progression-free survival (PFS) was 7.7 months. In the multivariate Cox analysis, the presence of extrahepatic metastasis (hazard ratio [HR] = 2.08, 95% confidence interval [CI]: 1.02-4.27, p < 0.05), white blood cell count (HR = 3.48, 95% CI: 1.02-11.88, p < 0.05) and AFP response (HR = 0.41, 95% CI: 0.18-0.95, p < 0.05) independently predicted PFS. A nomogram for PFS was established with AUCs of 0.79 and 0.70 in the training and validation sets, respectively. The median PFS of the high- and low-risk subgroups was 3.5 and 11.7 months, respectively (p < 0.05). CONCLUSION: The nomogram could predict PFS in patients with unresectable HCC receiving ICIs treatment and further help decision making in daily clinical practice.

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.

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.

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.

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.

Strategy for Accurate Diagnosis by Contrast-Enhanced Ultrasound of Focal Liver Lesions in Patients Not at High Risk for Hepatocellular Carcinoma: A Preliminary Study.

OBJECTIVE: To develop an effective strategy for accurate diagnosis of focal liver lesions (FLLs) in patients with non-high risk for hepatocellular carcinoma (HCC). METHODS: From January 2012 to December 2015, consecutive patients with non-high risk for HCC who underwent contrast-enhanced ultrasound (CEUS) were included in this retrospective double-reader study. All patients were stratified into 2 different risks (intermediate, low-risk) groups according to criteria based on clinical characteristics, known as clinical risk stratification criteria. For the intermediate-risk group, the CEUS criteria for identifying benign lesions and HCCs were constructed based on selected CEUS features. The diagnostic performance of the clinical risk stratification criteria, and CEUS criteria for identifying benign lesions and HCCs was evaluated. RESULTS: This study included 348 FLLs in 348 patients. The sensitivity and specificity of the clinical risk stratification criteria for malignancy was 97.8 and 69.8%. Patients were classified as intermediate risk if they were male, or older than 40 years of age, or HBcAb positive, or having positive tumor markers. Otherwise, patients were classified as low risk. Among the 348 patients, 327 were in the intermediate-risk group and 21 were in the low-risk group. In the intermediate-risk group, the CEUS criteria for identifying benign lesions were any of the following features: 1) hyper/isoenhancement in the arterial phase without washout, 2) nonenhancement in all phases, 3) peripheral discontinuous globular enhancement in the arterial phase, 4) centrifugal enhancement or peripheral enhancement followed by no central enhancement, or 5) enhanced septa. The accuracy, sensitivity, and specificity of the CEUS criteria for identifying benign lesions were 94.5, 83.0, and 99.6%, respectively. Arterial phase hyperenhancement followed by mild and late washout (>60 seconds) was more common in HCC patients than in non-HCC patients (P < .001). Using arterial phase hyperenhancement followed by mild and late washout as the CEUS criteria for identifying HCCs, the sensitivity and specificity were 52.6 and 95.3%, but unfortunately, the positive predictive value was only 82.0%. For the low-risk group, no further analysis was performed due to the small sample size. CONCLUSIONS: Initial clinical risk stratification followed by assessment of certain CEUS features appears to be a promising strategy for the accurate diagnosis of FLLs in patients not at high risk for HCC.

Identification of the origin of tumor in vein: comparison between CEUS LI-RADS v2017 and v2016 for patients at high risk.

OBJECTIVES: To compare the diagnostic performance of the Contrast-Enhanced Ultrasound (CEUS) Liver Imaging Report and Data System (LI-RADS) v2016 and v2017 in identifying the origin of tumor in vein (TIV). METHODS: From April 2014 to December 2018, focal liver lesions (FLLs) accompanied by TIV formation in patients at high risk for hepatocellular carcinoma (HCC) were enrolled. Histologic evaluation or composite imaging reference standard were served as the reference standard. Each case was categorized according to the CEUS LI-RADS v2016 and v2017, respectively. Diagnostic performance of CEUS LI-RADS v2016 and v2017 in identifying the originated tumor of TIV was validated via sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value. RESULTS: A total of 273 FLLs with TIV were analyzed finally, including 266 HCCs and 7 non-HCCs. In v2016, when adopting all TIV as LR-5V, the accuracy and PPV in identifying the originated tumor were both 97.4%. In v2017, when assigning TIV according to contiguous FLLs CEUS LI-RADS category, the accuracy and PPV were 61.9% and 99.4% in subclass of LR-5 as the diagnostic criteria of HCC, and 64.1% and 99.4% in subclass of LR-4/5 as the criteria of HCC diagnosis. There were significant differences in diagnostic accuracy between CEUS LI-RADS v2016 and v2017 in identifying the originated tumor of TIV (p < 0.001). CONCLUSIONS: CEUS LI-RADS v2016 could be better than v2017 in identifying the originated tumor of TIV.

Circulating tumour cell counts and ultrasomics signature-based nomogram for preoperative prediction of early recurrence of hepatocellular carcinoma after radical treatment.

METHODS: Between December 2017 and December 2018, 153 HCC patients (134 males and 19 females; mean age, 56.0 ± 10.2 years; range, 28-78 years) treated with radical therapy were enrolled in our retrospective study and were divided into a training cohort (n = 107) and a validation cohort (n = 46). All patients underwent preoperative CTC tests and CEUS examinations before treatment. The ultrasomics signature was extracted and built from CEUS images. Univariate and multivariate logistic regression analyses were used to identify the significant variables related to ER, which were then combined to build a predictive nomogram. The performance of the nomogram was evaluated by its discrimination, calibration and clinical utility. The predictive model was further evaluated in the internal validation cohort. RESULTS: HBV DNA, serum AFP level, CTC status, tumour size and ultrasomics score were identified as independent predictors associated with ER (all p < 0.05). Multivariable logistic regression analysis showed that the CTC status (OR = 7.02 [95% CI, 2.07 to 28.38], p = 0.003) and ultrasomics score (OR = 148.65 [95% CI, 25.49 to 1741.72], p < 0.001) were independent risk factors for ER. The nomogram based on ultrasomics score, CTC status, serum AFP level and tumour size exhibited C-indexes of 0.933 (95% CI, 0.878 to 0.988) and 0.910 (95% CI, 0.765 to 1.055) in the training and validation cohorts, respectively, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSION: The nomogram incorporating CTC, ultrasomics features and independent clinical risk factors achieved satisfactory preoperative prediction of ER in HCC patients after radical treatment. ADVANCES IN KNOWLEDGE: 1. CTC status and ultrasomics score were identified as independent predictors associated with ER of HCC after radical treatment. 2. The nomogram constructed by ultrasomics score generated by 17 ultrasomics features, combined with CTCs and independent clinical risk factors such as AFP and tumour size. 3. The nomogram exhibited satisfactory discriminative power, and could be clinically useful in the preoperative prediction of ER after radical treatment in HCC patients.

Preoperative prognostic value of alfa-fetoprotein density in patients with hepatocellular carcinoma undergoing radiofrequency ablation.

OBJECTIVES: To examine the prognostic value of preoperative alfa-fetoprotein (AFP) density and other clinical factors in patients undergoing percutaneous radiofrequency ablation (RFA) of hepatocellular carcinoma (HCC). METHODS: From January 2010 to December 2018, a total of 543 patients undergoing RFA for HCC meeting the Milan criteria were included at our institution. AFP density was calculated as absolute AFP pre-ablation divided by the total volume of all HCC lesions. The survival rates according to AFP density were estimated using the Kaplan-Meier method and compared using the log-rank test. Univariate and multivariate Cox proportional-hazards regression analyses were used to assess predictors of overall survival (OS) and progression-free survival (PFS). RESULTS: The Kaplan-Meier 1-, 3-, and 5-year OS rates were 98.8%, 88.5%, and 70.4%, respectively, for the low AFP density group, and 98.3%, 74.9%, and 49.4%, respectively, for the high AFP density group. The corresponding PFS rates were 78.9%, 56.7%, and 40.9% (low AFP density group), and 63.6%, 40.8%, and 27.5% (high AFP density group). High AFP density was associated with significantly reduced PFS and OS (both p < 0.001). Multivariate analysis suggested that AFP density was a predictor of OS and PFS. CONCLUSIONS: Serum AFP density may serve as a promising predictor of survival in patients with HCC undergoing RFA. High AFP density could identify patients who might be prone to recurrence or progression and need close surveillance.

Application of the CT/MRI LI-RADS Treatment Response Algorithm to Contrast-Enhanced Ultrasound: A Feasibility Study.

Purpose: The contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) treatment response algorithm (TRA) is still in development. The aim of this study was to explore whether the CT/MRI LI-RADS TRA features were applicable to CEUS in evaluating the liver locoregional therapy (LRT) response. Patients and Methods: This study was a retrospective review of a prospectively maintained database of patients with hepatocellular carcinoma undergoing ablation between July 2017 and December 2018. The standard criteria for a viable lesion were a histopathologically confirmed or typical viable appearance in the follow-up CT/MRI. Performance of the LI-RADS TRA assessing tumor viability was then compared between CEUS and CT/MRI. Inter-reader association was calculated. Results: A total of 244 patients with 389 treated observations (118 viable) were evaluated. The sensitivity and specificity of the CEUS TRA and CT/MRI LI-RADS TRA viable categories for predicting viable lesions were 55.0% (65/118) versus 56.8% (67/118) (P = 0.480) and 99.3% (269/271) versus 96.3% (261/271) (P = 0.013), respectively. The PPV of CEUS was higher than that of CT/MRI (97.0% vs 87.0%). Subgroup analysis showed that the sensitivity was low in the 1-month assessment for both CEUS (38.1%, 16/42) and CT/MR (47.6%, 20/42) and higher in the 2-6-month assessment for both CEUS (65.7%, 23/35) and CT/MR (62.9%, 22/35). Interobserver agreements were substantial for both CEUS TRA and CT/MRI LI-RADS TRA (κ, 0.74 for both). Conclusion: The CT/MRI LI-RADS TRA features were applicable to CEUS TRA for liver locoregional therapy. The CEUS TRA for liver locoregional therapy has sufficiently high specificity and PPV to diagnose the viability of lesions after ablation.

Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017.

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.

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.

An assessment of liver lesions using a combination of CEUS LI-RADS and AFP.

PURPOSE: To improve noninvasive diagnosis of HCC using a combination of CE US LI-RADS and alpha-fetoprotein (AFP). METHODS: 757 solitary liver nodules from 757 patients at risk of HCC with CE US and serum AFP test were categorized as LR-1 to LR-5 through LR-M according to CE US LI-RADS version 2017. In LR-3, LR-4, and LR-M nodules, those with AFP > 200 ng/ml were reclassified as mLR-5. Nodules with LR-5 and mLR-5 were reclassified as definitely HCC to modify CE US LI-RADS. Diagnostic performance was assessed with specificity, sensitivity, and PPV. RESULTS: The sensitivity, specificity, and PPV of LR-5 as a predictor of HCC were 64.7%, 97.8%, and 98.9%, respectively. 32.1% patients with solitary liver nodule had AFP greater than 200 ng/ml, of which 98.8% were HCC (25.8%, 7.5%, 2.5% assigned to LR-M, LR-4, LR-3, respectively) and 1.2% were Combined Hepatocellular Cholangiocarcinoma. After modification, the sensitivity increased to 79.6% (P < 0.001), while specificity and PPV remained high (96.6% and 98.7%, P > 0.050). CONCLUSION: The combination of CE US LI-RADS and AFP for diagnosing HCC improved diagnostic sensitivity significantly, while maintaining high PPV and specificity in patients with the solitary liver nodule.

Preoperative Survival Prediction in Intrahepatic Cholangiocarcinoma Using an Ultrasound-Based Radiographic-Radiomics Signature.

OBJECTIVES: To construct a preoperative model for survival prediction in intrahepatic cholangiocarcinoma (ICC) patients using ultrasound (US) based radiographic-radiomics signatures. METHODS: Between April 2010 and September 2015, 170 patients with ICC who underwent curative resection were retrospectively recruited. Overall survival (OS)-related radiographic signatures and radiomics signatures based on preoperative US were built and assessed through a time-dependent receiver operating characteristic curve analysis. A nomogram was developed based on the selected predictors from the radiographic-radiomics signatures and clinical and laboratory results of the training cohort (n = 127), validated in an independent testing cohort (n = 43) by the concordance index (C-index), and compared with the Tumor Node Metastasis (TNM) cancer staging system as well as the radiographic and radiomics nomograms. RESULTS: The median areas under the curve of the radiomics signature and radiographic signature were higher than that of the TNM staging system in the testing cohort, although the values were not significantly different (0.76-0.82 versus 0.62, P = .485 and .264). The preoperative nomogram with CA 19-9, sex, ascites, radiomics signature, and radiographic signature had C-indexes of 0.72 and 0.75 in the training and testing cohorts, respectively, and it had significantly higher predictive performance than the 8th TNM staging system in the testing cohort (C-index: 0.75 versus 0.67, P = .004) and a higher C-index than the radiomics nomograms (0.75 versus 0.68, P = .044). CONCLUSIONS: The preoperative nomogram integrated with the radiographic-radiomics signature demonstrated good predictive performance for OS in ICC and was superior to the 8th TNM staging system.