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. Electronic supplementary material The online version of this article (10.1186/s40644-019-0197-5) contains supplementary material, which is available to authorized users.

Purpose To evaluate the diagnostic performance of ultrasonography (US) in the identification and exclusion of biliary atresia with a modified triangular cord thickness metric together with a gallbladder classification scheme, as well as hepatic artery (HA) diameter and liver and spleen size, in a large sample of jaundiced infants. Materials and Methods The ethics committee approved this study, and written informed parental consent was obtained. In 273 infants with conjugated hyperbilirubinemia (total bilirubin level ≥ 31.2 μmol/L, with direct bilirubin level > indirect bilirubin level), detailed abdominal US was performed to exclude biliary atresia. Biliary atresia was found in 129 infants and ruled out in 144. A modified triangular cord thickness was measured at the anterior branch of the right portal vein, and a gallbladder classification scheme was identified that incorporated the appearance of the gallbladder and a gallbladder length-to-width ratio of up to 5.2 when the lumen was visualized, as well as HA diameter and liver and spleen size. Reference standard diagnosis was based on results of one or more of the following: surgery, liver biopsy, cholangiography, and clinical follow-up. Area under the receiver operating characteristic curve (AUC) analysis, binary logistic regression analysis, Fisher exact test, and unpaired t test were performed. Results Triangular cord thickness, HA diameter, ratio of gallbladder length to gallbladder width, liver size, and spleen size exhibited statistically significant differences (all P < .05) between the group with biliary atresia and the group without. AUCs of triangular cord thickness, ratio of gallbladder length to width, and HA diameter were 0.952, 0.844, and 0.838, respectively. Logistic regression analysis demonstrated that these three US parameters were significantly associated (all P < .05) with biliary atresia. The combination of triangular cord thickness and gallbladder classification could yield comparable AUCs (0.915 vs 0.933, P = .400) and a higher sensitivity (96.9% vs 92.2%), compared with triangular cord thickness alone. Conclusion By using the combination of modified triangular cord thickness and gallbladder classification scheme, most infants with biliary atresia could be identified. (©) RSNA, 2015.

• SSWE could be successfully performed in an infant population. • For infants, the liver stiffness will increase as age increases. • SSWE is potentially useful in assessing infants suspected of biliary atresia. • SSWE is inferior to grey-scale US in identifying biliary atresia.

The aim of this meta-analysis was to estimate the diagnostic performance of shear wave elastography (SWE) in differentiating malignant from benign breast lesions. A literature search of PubMed, Web of Science and Scopus up to November 2014 was conducted. A summary receiver operating characteristic curve was constructed, and pooled weighted estimates of sensitivity and specificity were calculated using a bivariate mixed-effects regression model. Thirty-three studies, which included a total of 5838 lesions (2093 malignant, 3745 benign) from 5397 patients, were finally analyzed. Summary sensitivity and specificity were 0.886 (95% confidence interval [CI], 0.858-0.909) and 0.866 (95% CI, 0.833-0.894), respectively. The pooled diagnostic odds ratio was 50.410 (95% CI, 34.972-72.664). And the area under the receiver operating characteristic curve of SWE was 0.94 (95% CI, 0.91-0.96). No publication bias existed among these studies (p = 0.245). In the subgroup analysis, sensitivity and specificity were 0.862 (95% CI, 0.811-0.901) and 0.875 (95% CI, 0.793-0.928) among 1552 lesions from 1429 patients in the 12 studies using acoustic radiation force impulse imaging and 0.897 (95% CI, 0.863-0.923) and 0.863 (95% CI, 0.831-0.889) among another 4436 lesions from 4097 patients in the 21 studies using supersonic shear imaging. When analysis confined to 9 studies evaluated the diagnostic performance of combination SWE and conventional ultrasound, the area under the curve was 0.96 (95% CI, 0.94-0.97), yielding a sensitivity of 0.971 (95% CI, 0.941-0.986) and specificity of 0.801 (95% CI, 0.733-0.856). SWE seems to be a good quantitative method for differentiating breast lesions, with promise for integration into routine imaging protocols.

The triangular cord sign and gallbladder abnormalities are the two most accurate and widely accepted ultrasound characteristics for diagnosing or excluding biliary atresia. Other ultrasound characteristics are less valuable for diagnosis or exclusion of biliary atresia.

To present a systematic [corrected] review and meta-analysis to evaluate the nomograms developed to predict non-sentinel lymph node (NSLN) metastasis in breast cancer patients. We focused on the six nomograms (Cambridge, MSKCC, Mayo, MDA, Tenon, and Stanford) that are the most widely validated. The AUCs were converted to odds ratios for the meta-analysis. In total, the Cambridge, Mayo, MDA, MSKCC, Stanford, and Tenon models were validated in 2,156, 2,431, 843, 8,143, 3,700, and 3,648 patients, respectively. The pooled AUCs for the Cambridge, MDA, MSKCC, Mayo, Tenon, and Stanford models were 0.721, 0.706, 0.715, 0.728, 0.720, and 0.688, respectively. Subgroup analysis revealed that in populations with a higher micrometastasis rate in the SLNs, the Tenon and Stanford models had a significantly higher predictive accuracy. A meta-regression analysis revealed that the SLN micrometastasis rate, but not the NSLN-positivity rate, was associated with improved predictive accuracy in the Tenon and Stanford models. The performance of the MSKCC and Cambridge models was not influenced by these two factors. All of these prediction models perform better than random chance. The Stanford model seems to be relatively inferior to the other models. The accuracy of the Tenon and Stanford models is influenced by the tumor burden in the SLNs.

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The 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.