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We aimed to evaluate the performance of the newly developed deep learning Radiomics of elastography (DLRE) for assessing liver fibrosis stages. DLRE adopts the radiomic strategy for quantitative analysis of the heterogeneity in two-dimensional shear wave elastography (2D-SWE) images. A prospective multicentre study was conducted to assess its accuracy in patients with chronic hepatitis B, in comparison with 2D-SWE, aspartate transaminase-to-platelet ratio index and fibrosis index based on four factors, by using liver biopsy as the reference standard. Its accuracy and robustness were also investigated by applying different number of acquisitions and different training cohorts, respectively. Data of 654 potentially eligible patients were prospectively enrolled from 12 hospitals, and finally 398 patients with 1990 images were included. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the optimal area under the ROC curve (AUC) for cirrhosis (F4), advanced fibrosis (≥F3) and significance fibrosis (≥F2). AUCs of DLRE were 0.97 for F4 (95% CI 0.94 to 0.99), 0.98 for ≥F3 (95% CI 0.96 to 1.00) and 0.85 (95% CI 0.81 to 0.89) for ≥F2, which were significantly better than other methods except 2D-SWE in ≥F2. Its diagnostic accuracy improved as more images (especially ≥3 images) were acquired from each individual. No significant variation of the performance was found if different training cohorts were applied. DLRE shows the best overall performance in predicting liver fibrosis stages compared with 2D-SWE and biomarkers. It is valuable and practical for the non-invasive accurate diagnosis of liver fibrosis stages in HBV-infected patients. NCT02313649; Post-results.

PurposeTo develop an ultrasound (US)-based radiomics score for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsBetween 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.ResultsThe 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.ConclusionThe 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.

ObjectiveTo assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.Materials and MethodsThis 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).ResultsORF 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).ConclusionMachine-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.

Objective To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. Materials and methods In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). Results By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P  = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P  < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E ( P  = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 ( P  = 0.01) compared to PT-E. Conclusion The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.

Abstract Background and aim The distinction of intestinal fibrosis from inflammation in Crohn's disease (CD) associated strictures has important therapeutic implications. Ultrasound elastography is useful in evaluating the degree of fibrosis in liver, but there is little evidence whether it can assess fibrosis in the bowel. We determined whether shear-wave elastography (SWE), a novel modification of elastography, quantifying tissue stiffness, could differentiate between inflammatory and fibrotic components in strictures of patients with CD. Methods Consecutive CD patients with ileal/ileocolonic strictures who underwent SWE within 1 week to surgical resection were enrolled. The SWE value of the stenotic bowel wall was compared to the grade and severity of fibrosis and inflammation, respectively, in the resected bowel specimen. Results Thirty-five patients were enrolled. The mean SWE value of stenotic bowel wall was significantly higher in severe fibrosis (23.0 ± 6.3 Kpa) than that in moderate (17.4 ± 3.8 Kpa) and mild fibrosis (14.4 ± 2.1 Kpa)(P = 0.008). Using 22.55 KPa as the cutoff value in discriminating between mild/moderate and severe fibrosis, the sensitivity and specificity was 69.6 % and 91.7% with an area under the curve (AUC) of 0.822 (P = 0.002). However, no significant difference regarding mean SWE existed among different grades of inflammation. The sensitivity and specificity of bowel vascularization score on conventional ultrasound in differentiating severe inflammation from mild/moderate was 87.5 % and 57.9% with AUC of 0.811 (P = 0.002). Combining SWE and conventional ultrasound (bowel vascularization score), we propose a bowel ultrasound classification of intestinal strictures. A moderate agreement between ultrasound and pathological classification was observed (κ = 0.536, P＜0.001). Conclusions This pilot study suggests that SWE is feasible and accurate in detecting intestinal fibrosis in patients with CD. After validation, combing SWE and bowel vascularization on conventional ultrasound might be applied to guide a management strategy in CD patients through defining the type of intestinal stricture. 10.1093/ibd/izy115_video1 izy115.video1 5777734754001

Objectives Restricted mean survival time (RMST) has been increasingly used to assess the treatment effect. We aimed to evaluate a treatment effect of radiofrequency ablation (RFA) versus liver transplantation (LT) and surgical resection (SR) for hepatocellular carcinoma (HCC) within Milan criteria by using an adjusted RMST. Methods A total of 7,218 HCC patients (RFA, 3,327; LT, 2,332; SR 1,523) within Milan criteria were eligible for this retrospectively study. The RMST using inverse probability of treatment weighting (IPTW) adjustment were applied to estimate the treatment effect between RFA and LT, RFA, and SR groups. Results The 3-, 5-, and 10-year IPTW-adjusted difference in RMST of OS for LT over RFA were + 4.5, + 12.4, and + 36.3 months, respectively. For SR versus RFA group, the survival benefit was + 2.3, + 6.1, and + 15.8 months at 3, 5, and 10 years, respectively. But the incremental survival benefit of SR over RFA was only half than that of LT over RFA. In the subgroup of solitary tumor ≤ 2 cm, the adjusted RMST of RFA versus SR was comparable with no statistical differences. Beyond that, in comparison with RFA, a notably greater efficacy of LT and SR was consistently across all subgroups with solitary HCC > 2.0 cm, AFP positive or negative, and fibrosis score 0–4 or 5–6. Conclusions RMST provides a measure of absolute survival benefit at a specific time point. Using IPTW-adjusted RMST, we showed that the incremental survival benefit of SR over RFA was about half than that of LT over RFA. Key Points • The restricted mean survival time offers an intuitive, clinically meaningful interpretation to quantify the treatment effect than the hazard ratio. • Liver transplantation and surgical resection provided better overall survival compared to radiofrequency ablation for HCC patients within Milan criteria, but RFA and SR provide equivalent long-term overall survival for solitary HCC ≤ 2 cm. • The incremental survival benefit of surgical resection over radiofrequency ablation was only half than that of liver transplantation over radiofrequency ablation.

ObjectiveTo develop a contrast-enhanced ultrasound (CEUS) M-score and compare it with LR-M in CEUS Liver Imaging Reporting and Data System (LI-RADS).MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

ObjectiveTo develop a machine learning–based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.MethodsFrom December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set.ResultsThe training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384).ConclusionsUS radiomics may be a potential model to accurately predict TDs before therapy.Key Points• We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%.• The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384).• The US radiomics–based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.

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.

BackgroundHistologic microvascular invasion (MVI) substantially worsens the prognosis of patients with hepatocellular carcinoma, and can only be diagnosed postoperatively. Preoperative assessment of MVI by imaging has been focused on tumor-related features, while peritumoral imaging features have been indicated elsewhere to be more accurate. The aim of the present study is to evaluate the association between peritumoral imaging features and MVI.MethodsLiterature search was performed using the PubMed, Embase, and Cochrane Library databases. Summary results of the association between peritumoral imaging features and MVI were presented as the odds ratio (OR) and the 95% confidence interval. Meta-regression and subgroup analyses were performed when heterogeneity was detected. Diagnostic accuracy analysis was also conducted for identified features.ResultsTen studies were included in the analysis. Moderate and low heterogeneities were found among the seven studies on peritumoral enhancement and four studies on peritumoral hypointensity on HBP, respectively. Summary results revealed a significant association between MVI and peritumoral enhancement (OR 4.04 [2.23, 7.32], p < 0.05), and peritumoral hypointensity on HBP (OR 10.62 [5.31, 21.26], p < 0.05). Diagnostic accuracy analysis revealed high specificity (0.90-0.94) but low sensitivity (0.29–0.40) for both features to assess MVI.ConclusionThe two peritumoral imaging features are significantly associated with MVI. The two features highly suggest MVI only when present with a high false negative rate. Promotion of their diagnostic efficiency can be a worthwhile task for future research.