Irregular Tumor Margin Research Articles

Comparison of Sonazoid-Contrast‑Enhanced Ultrasound and Gd‑EOB‑DTPA‑Enhanced MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma

ObjectiveThis study aims to evaluate and compare the predictive accuracy of Sonazoid-contrast-enhanced ultrasound (CEUS) and Gd-EOB-DTPA-enhanced MRI for detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). MethodsIn this single-center prospective study, we included 64 patients with histopathologically confirmed single HCC lesions. Based on post-operative pathologic data, patients were categorized into two groups: those with MVI (n = 21) and those without MVI (n = 43). The diagnostic efficacy of CEUS was compared with that of MRI in predicting MVI. ResultsMultifactorial analysis revealed that US features (tumor size > 4.35 cm, peritumoral enhancement, post-vascular ring enhancement, peak energy in the arterial phase of the difference between the margin area of HCC and distal liver parenchyma <−1.0 × 106 a.u), MRI features (rim enhancement, irregular tumor margin, and the halo sign) were all independent predictors of MVI (p < 0.05). The sensitivity and specificity of CEUS features in predicting MVI ranged from 61.9% to 86.4% and from 42.9% to 71.4%, respectively. For MRI features, the sensitivity and specificity ranged from 33.3% to 76.3% and from 54.7% to 90.5%, respectively. No statistically significant differences were observed in the area under the curve between CEUS and MRI (p > 0.05). Notably, peak energy of the difference showed the highest sensitivity at 86.4%, while the halo sign in MRI exhibited the highest specificity at 90.5%. ConclusionSonazoid-CEUS and Gd-EOB-DTPA-enhanced MRI demonstrate potential in predicting MVI in HCC lesions. Notably, CEUS showed higher sensitivity, whereas MRI displayed greater specificity in predicting MVI.

Diagnostic Model for Proliferative HCC Using LI-RADS: Assessing Therapeutic Outcomes in Hepatectomy and TKI-ICI Combination.

Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis. To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value. Retrospective. 241 HCC patients underwent hepatectomy (90 proliferative HCCs: 151 nonproliferative HCCs), divided into the training (N = 167) and validation (N = 74) sets. 57 HCC patients received combination therapy with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). 3.0 T, T1- and T2-weighted, diffusion-weighted, in- and out-phase, T1 high resolution isotropic volume excitation and dynamic gadoxetic acid-enhanced imaging. LI-RADS v2018 and other MRI features (intratumoral artery, substantial hypoenhancing component, hepatobiliary phase peritumoral hypointensity, and irregular tumor margin) were assessed. A diagnostic model for proliferative HCC was established, stratifying patients into high- and low-risk groups. Follow-up occurred every 3-6 months, and recurrence-free survival (RFS), progression-free survival (PFS) and overall survival (OS) in different groups were compared. Fisher's test or chi-square test, t-test or Mann-Whitney test, logistic regression, Harrell's concordance index (C-index), Kaplan-Meier curves, and Cox proportional hazards. Significance level: P < 0.05. The diagnostic model, incorporating corona enhancement, rim arterial phase hyperenhancement, infiltrative appearance, intratumoral artery, and substantial hypoenhancing component, achieved a C-index of 0.823 (training set) and 0.804 (validation set). Median follow-up was 32.5 months (interquartile range [IQR], 25.1 months) for postsurgery patients, and 16.8 months (IQR: 13.2 months) for combination-treated patients. 99 patients experienced recurrence, and 30 demonstrated tumor nonresponse. Differences were significant in RFS and OS rates between high-risk and low-risk groups post-surgery (40.3% vs. 65.8%, 62.3% vs. 90.1%, at 5 years). In combination-treated patients, PFS rates differed significantly (80.6% vs. 7.7% at 2 years). The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation. Stage 2.

CT and MRI findings of small cell neuroendocrine carcinoma of the urinary bladder: comparison with urothelial carcinoma

ObjectiveThis study aimed to evaluate the efficacy of CT and MRI findings to differentiate small cell neuroendocrine carcinoma (SCNEC) from urothelial carcinoma (UC) of the urinary bladder.Materials and methodsThis study included 90 patients with histopathologically confirmed bladder cancer (10 SCNECs and 80 UCs). Eight patients with bladder SCNEC and 80 with UC underwent CT and MRI, whereas the remaining two patients with SCNEC underwent CT alone before treatment. CT and MRI findings were retrospectively evaluated and compared between the two pathologies.ResultsThe maximum diameter (36.5 mm vs. 19.0 mm, p < 0.01) and height (22.0 mm vs. 14.0 mm, p < 0.01) of the tumor in bladder SCNEC were higher than in UC. The pedunculated configuration (20% vs. 61%, p < 0.05) and irregular tumor margins (20% vs. 76%, p < 0.01) in bladder SCNEC were less common than in UC. The CT attenuation of the solid component in unenhanced CT images was higher in bladder SCNEC than in UC (37 Hounsfield unit [HU] vs. 34 HU, p < 0.01). The apparent diffusion coefficient (ADC) of the solid component in bladder SCNEC was lower than in UC (0.49 × 10−3 mm2/s vs. 1.02 × 10−3 mm2/s, p < 0.01).ConclusionIn comparison with UC, bladder SCNEC was larger, had higher unenhanced CT attenuation, and had a lower ADC value. The pedunculated configuration and irregular tumor margins were typical of bladder UC.Graphical abstract

MRI findings of malignant transformation arising from mature cystic teratoma of the ovary: comparison with benign mature cystic teratoma.

This study aimed to evaluate the efficacy of MRI findings to differentiate malignant transformation arising from mature cystic teratoma (MT-MCT) of the ovary from benign mature cystic teratoma (BMCT). This study included 11 patients with histopathologically proven MT-MCT and 50 with BMCT. Overall, 7 patients with MT-MCT and all 50 with BMCT underwent unenhanced and contrast-enhanced MRIs and 4 with MT-MCT only underwent unenhanced MRIs. The MRI findings were evaluated and compared between the two diseases. The median age (55 vs. 38years, p < 0.01) and maximum diameter (109 vs. 65mm, p < 0.01) were higher in MT-MCT than in BMCT. Fat component occupancy was lower in MT-MCT than in BMCT (median, 5% vs. 63%, p < 0.01). Only MT-MCT exhibited irregular tumor margins (64%), peritoneal dissemination (18%), and abnormal ascites (27%). The solid components were more commonly observed in MT-MCT than in BMCT (100% vs. 32%, p < 0.01) on contrast-enhanced images. The maximum diameter of solid components in MT-MCT was larger than that in BMCT (median, 61mm vs. 14mm, p < 0.01). In MT-MCT, the common configuration of solid components was endophytic or exophytic sessile (85%), whereas in BMCT, it was endophytic papillary (88%). Compared with BMCT, MT-MCT demonstrated a larger maximum diameter, lower occupancy rate of fat components, and sessile solid components. The characteristic configuration of solid components was endophytic or exophytic sessile in MT-MCT and endophytic papillary in BMCT.

Correlation between Magnetic Resonance Imaging (MRI) Grading with Histopathological Grading of Supratentorial Meningiomas

Background: Meningiomas, common intracranial tumors originating from meningeal cells, critically impact patient outcomes. Their histologic grade influences recurrence risk and treatment decisions, including radiation therapy. Identifying advanced-grade meningiomas via pre-operative MRI is vital for surgical planning, reducing complications, and guiding adjuvant therapies. Objective: To investigate the correlation between pre-operative MRI features and histopathological grade in supratentorial meningiomas. Methods: This cross-sectional study was conducted from July 2019 to December 2020 at the Department of Neurosurgery of Dhaka Medical College and Hospital. A total of 43 patients with supratentorial meningiomas underwent resection and were analyzed in terms of neuroimaging features of pre-operative MRI. The relationships between MRI features and WHO histopathological grade were analyzed and scored quantitatively. Results: The mean age of the study population was 37±11.86 (SD) years. Male female ratio was 1:2.7. Main clinical presentation was headache (90.7%). The most common location was convexity meningioma (37.2%). Out of all patients, high, grade and low-grade meningioma were diagnosed by MRI, 9 (20.93%) and 34 (79.09%), respectively; on the other hand, high, grade and low-grade meningiomas diagnosed by histopathology 7 (16.3%) and 36 (83.7%) respectively. A positive correlation was found between MRI grading and histopathological grading of supratentorial meningiomas and statistical significance (Spearman rho-0.547, p-value &lt;0.001). A significant association was also observed between MRI grading and histopathological grading of supratentorial meningiomas (p-value 0.002). Unclear tumor brain interface, capsular enhancement, and irregular tumor margin in the MRI were identified factors in predicting advanced histopathological grade of meningiomas as p-values 0.002, 0.008, and 0.001, respectively. Conclusion: This scoring approach may be useful for neurosurgeons ...

Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis.

This systematic review and meta-analysis evaluated the diagnostic accuracy of CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from benign lipomatous lesions. MEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to January 2022 were systematically evaluated. Original studies with > 5 patients evaluating the accuracy of CT and/or MRI for detecting liposarcomas with a histopathological reference standard were included. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). This study is registered on PROSPERO, number CRD42022306479. Twenty-six studies with a total of 2613 patients were included. Mean/median reported patient ages ranged between 50 and 63years. The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. Deep depth to fascia, thickened septations, enhancing components, and lesion size (≥ 10cm) all demonstrated sensitivities ≥ 85%. Other imaging characteristics including heterogenous/amorphous signal intensity, irregular tumor margin, and nodules present demonstrated lower sensitivities ranging from 43 to 65%. Inter-reader reliability for radiologist gestalt within studies ranged from fair to substantial (k = 0.23-0.7). Risk of bias was predominantly mixed for patient selection, low for index test and reference standard, and unclear for flow and timing. Higher sensitivities for detecting liposarcomas were achieved with radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size. Combined clinical and imaging scoring and/or radiomics both show promise for optimal performance, though require further analysis with prospective study designs. This pooled analysis evaluates the accuracy of CT and MRI for detecting atypical lipomatous tumors and malignant liposarcomas. Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size demonstrate the highest overall sensitivities. • The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. • Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large tumor size (≥ 10cm) showed the highest sensitivities for detecting atypical lipomatous tumors/well-differentiated liposarcomas and malignant liposarcomas. • A combined clinical and imaging scoring system and/or radiomics is likely to provide the best overall diagnostic accuracy, although currently proposed scoring systems and radiomic feature analysis require further study with prospective study designs.

Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.

To investigate the utility of preoperative multiparametric magnetic resonance imaging (mpMRI)-based clinical-radiomic analysis combined with machine learning (ML) algorithms in predicting the expression of the Ki-67 proliferative index and p53 tumor suppressor protein in patients with meningioma. This multicenter retrospective study included 483 and 93 patients from two centers. The Ki-67 index was classified into high (Ki-67≥5%) and low (Ki-67<5%)-expressed groups, and the p53 index was classified into positive (p53≥5%) and negative (p53<5%)-expressed groups. Clinical and radiological features were analyzed using univariate and multivariate statistical analyses. Six ML models were performed with different types of classifiers to predict Ki-67 and p53 status. In the multivariate analysis, larger tumor volumes (p<0.001), irregular tumor margin (p<0.001), and unclear tumor-brain interface (p<0.001) were independently associated with a high Ki-67 status, whereas the presence of both necrosis (p=0.003) and the dural tail sign (p=0.026) were independently associated with a positive p53 status. A relatively better performance was yielded from the model constructed by combined clinical and radiological features. The area under the curve (AUC) and accuracy of high Ki-67 were 0.820 and 0.867 in the internal test, and 0.666 and 0.773 in the external test, respectively. Regarding p53 positivity, the AUC and accuracy were 0.858 and 0.857 in the internal test, and 0.684 and 0.718 in the external test. The present study developed clinical-radiomic ML models to non-invasively predict Ki-67 and p53 expression in meningioma using mpMRI features, and provides a novel non-invasive strategy for assessing cell proliferation.

Prediction model of lymph node metastasis posterior to the right recurrent laryngeal nerve

At present, it is still controversial whether lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) in papillary thyroid carcinoma (PTC) patients should be dissected. Failure to dissect metastatic lymph nodes results in continued metastasis from the positive lymph nodes to other regions. Our study aimed to establish a predictive model and predict the probability of metastasis of the lymph nodes posterior to the right recurrent laryngeal nerve (LNM-prRLN) in patients. A total of 309 patients underwent surgery for thyroid cancer between May 2019 and September 2022. The risk factors were identified by univariate and multivariate analyses, and statistically significant risk factors identified in the multivariate analysis were included in the nomogram. We used the calibration curve and the receiver operating characteristic (ROC) curve to verify the accuracy of the prediction model. Multivariate analysis showed that irregular tumor margins (OR: 3.549, 95% CI 1.294-9.733, P = 0.014), extrathyroidal extension (OR: 4.507, 95% CI 1.694-11.993, P = 0.003), maximum tumor diameter > 1cm (OR: 5.729, 95% CI 2.617-12.542, P < 0.001), overweight status (OR: 2.296, 95% CI 1.057-4.987, P = 0.036), high total cholesterol level (OR: 5.238, 95% CI 2.304-11.909, P < 0.001), and multifocality (OR: 11.954, 95% CI 5.233-27.305, P < 0.001) were independent risk factors for LNM-prRLN. The area under the ROC curve was 0.927. The calibration curve showed good agreement between the predicted and observed rates of LNM-prRLN. The probability of LNM-prRLN could be predicted by a nomogram based on the statistically significant risk factors identified in the multivariate analysis. This nomogram can guide clinicians when preoperatively evaluating the status of the LN-prRLN with regard to LNM-prRLN in PTC patients. For patients at high risk for LNM-prRLN, the preventive dissection of LN-prRLNs can be considered.

Prognostic role and predictors of lymph node involvement in pancreatic neuroendocrine tumors

PurposeTo define the prognostic role of lymph node involvement (LNI) in patients with pancreatic neuroendocrine tumors (PNETs) and identify predictors of LNI using a comprehensive multifactor analysis focusing on preoperative radiological features. MethodsThis study included 236 patients with preoperative computed tomography who underwent radical surgical resection of PNETs at our hospital between 2009 and 2019. Univariate and multivariable logistic regression analyses were performed to investigate the risk factors associated with LNI and tumor recurrence. The disease-free survival (DFS) rates with and without LNI were compared. ResultsForty-four of the 236 patients (18.6%) had LNI. Biliopancreatic duct dilatation (odds ratio [OR], 2.295; 95% confidence interval [CI], 1.046–5.035; p = 0.038), tumor margin (OR, 2.189; 95% CI, 1.034–4.632; p = 0.041), and WHO grade (G2: OR, 2.923; 95% CI, 1.005–8.507; p = 0.049; G3: OR, 12.067; 95% CI, 3.057–47.629; p < 0.001) were independent risk factors of LNI in PNETs. Multivariable analysis showed that LNI (OR, 2.728; 95% CI, 1.070–6.954; p = 0.036), G3 (OR, 4.894; 95% CI, 1.047–22.866; p = 0.044), and biliopancreatic duct dilatation (OR, 2.895; 95% CI, 1.124–7.458; p = 0.028) were associated with PNET recurrence in patients after surgery. Patients with LNI had a significantly worse DFS than those without LNI (3‐year DFS: 85.9 vs. 96.7%; p < 0.001; 5‐year DFS: 65.1 vs. 93.9%; p < 0.001). ConclusionLNI was associated with decreased DFS. Biliopancreatic duct dilatation, irregular tumor margins, and grades G2 and G3 were independent risk factors for LNI.

Radiological Features of Microvascular Invasion of Hepatocellular Carcinoma in Patients with Non-Alcoholic Fatty Liver Disease

Background: The aim of the present study was to evaluate the presence and the prognostic value of the radiological signs of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD). Methods: Between January 2015 and December 2017, all patients (91 patients) with de novo HCC or HCC recurrence occurring at least 2 years after the last treatment in NAFLD (36 patients) or with hepatitis C virus (HCV) liver disease (55 patients) were included. Each HCC was treated with liver resection and transplantation to obtain the anatomopathological confirmation of MVI. All patients had at least one available computed tomography (CT) scan or magnetic resonance imaging (MRI) performed no more than one month prior to the treatment. The clinical data of each patient, tumor burden (diameter, margins, two-trait predictor of venous invasion (TTPVI), and peritumoral enhancement), the recurrence rate (RR) after a 1-year follow-up, and the time to recurrence (TTR) were collected. Results: The NAFLD–HCC nodules were larger as compared to HCV–HCC (51 mm vs. 36 mm, p = 0.004) and showed a higher prevalence of TTPVI (38.9 vs. 20.0%, p = 0.058). At multivariate analysis, nodule diameter &gt;50 mm was found to be the only independent prognostic factor of TTPVI (hazard ratio: 21.3, 95% confidence interval: 4.2–107.7, p &lt; 0.001), and the presence of TTPVI was confirmed to be the only independent prognostic factors of recurrence (hazard ratio: 2.349, 95% confidence interval: 1.369–4.032, p = 0.002). No correlations were found between TTR and irregular tumor margins or peritumoral enhancement. Conclusion: The NAFLD–HCC patients had larger tumors at diagnosis and showed a more frequent presence of radiological signs of MVI as compared to the HCV–HCC patients. The MVI was related to a more rapid recurrence after curative treatments, demonstrating the prognostic value of this radiological diagnosis.

Can lymphovascular invasion be predicted by contrast-enhanced CT imaging features in patients with esophageal squamous cell carcinoma? A preliminary retrospective study

BackgroundTo investigate the value of contrast-enhanced CT (CECT)-derived imaging features in predicting lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC) patients.MethodsOne hundred and ninety-seven patients with postoperative pathologically confirmed esophageal squamous cell carcinoma treated in our hospital between January 2017 and January 2019 were enrolled in our study, including fifty-nine patients with LVI and one hundred and thirty-eight patients without LVI. The CECT-derived imaging features of all patients were analyzed. The CECT-derived imaging features were divided into quantitative features and qualitative features. The quantitative features consisted of the CT attenuation value of the tumor (CTVTumor), the CT attenuation value of the normal esophageal wall (CTVNormal), the CT attenuation value ratio of the tumor-to-normal esophageal wall (TNR), the CT attenuation value difference between the tumor and normal esophageal wall (ΔTN), the maximum thickness of the tumor measured by CECT (Thickness), the maximum length of the tumor measured by CECT (Length), and the gross tumor volume measured by CECT (GTV). The qualitative features consisted of an enhancement pattern, tumor margin, enlarged blood supply or drainage vessels to the tumor (EVFDT), and tumor necrosis. For the clinicopathological characteristics and CECT-derived imaging feature analysis, the chi-squared test was used for categorical variables, the Mann–Whitney U test was used for continuous variables with a nonnormal distribution, and the independent sample t-test was used for the continuous variables with a normal distribution. The trend test was used for ordinal variables. The association between LVI status and CECT-derived imaging features was analyzed by univariable logistic analysis, followed by multivariable logistic regression and receiver operating characteristic (ROC) curve analysis.ResultsThe CTVTumor, TNR, ΔTN, Thickness, Length, and GTV in the group with LVI were higher than those in the group without LVI (P < 0.05). A higher proportion of patients with heterogeneous enhancement pattern, irregular tumor margin, EVFDT, and tumor necrosis were present in the group with LVI (P < 0.05). As revealed by the univariable logistic analysis, the CECT-derived imaging features, including CTVTumor, TNR, ΔTN and enhancement pattern, Thickness, Length, GTV, tumor margin, EVFDT, and tumor necrosis were associated with LVI status (P < 0.05). Only the TNR (OR 8.655; 95% CI 2.125–37.776), Thickness (OR 6.531; 95% CI 2.410–20.608), and tumor margin (OR 4.384; 95% CI 2.004–9.717) were independent risk factors for LVI in the multivariable logistic regression analysis. The ROC curve analysis incorporating the above three CECT-derived imaging features showed that the area under the curve obtained by the multivariable logistic regression model was 0.820 (95% CI 0.754–0.885).ConclusionThe CECT-derived imaging features, including TNR, Thickness, tumor margin, and their combination, can be used as predictors of LVI status for patients with ESCC.

A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature.

ObjectivesThe objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation.Methods202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant’s MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC).ResultsThe entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors α-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets.ConclusionsThe combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC.

What is the difference between the tall cell variant and the classic type of papillary thyroid carcinoma on ultrasonography?

PurposeThe prevalence of the tall cell variant of papillary thyroid carcinoma (TCVPTC), which has a poor prognosis, has increased as its definition has been modified. We sought to investigate whether TCVPTC is different from the classic type on ultrasonography (US).MethodsThis study included 46 consecutive TCVPTC patients and 92 classic papillary thyroid carcinoma (PTC) patients who were confirmed surgically at the authors’ institution. The US findings and pathologic reports of these patients were retrospectively reviewed. US features based on the Korean Thyroid Imaging Reporting and Data System, preoperative US suspicion for lymph node metastasis, and the presence of capsular location were evaluated.ResultsUnivariable and multivariable analyses identified that TCVPTC showed more frequent irregular tumor margin (odds ratio [OR], 6.62; 95% confidence interval [CI], 1.46 to 30.09; P=0.014) and capsular location (OR, 4.63; 95% CI, 1.49 to 14.41; P=0.008) than classic PTC. Capsular location was an independent predictor of TCVPTC for tumors less than or equal to 1.5 cm in size (OR, 4.23; 95% CI, 1.12 to 15.92; P=0.033). Irregular margin was an independent predictor of TCVPTC for tumors larger than 1.5 cm (OR, 10.46; 95% CI, 1.16 to 94.48; P=0.037). Extrathyroidal extension was not significantly different between the two groups.ConclusionThe two key features of TCVPTC on US are frequent capsular location for tumors less than or equal to 1.5 cm in size and the higher likelihood of an irregular margin for tumors larger than 1.5 cm.

A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid-enhanced MRI.

We aimed to develop a radiomics-based model derived from gadoxetic acid-enhanced MR images to preoperatively identify cytokeratin (CK) 19 status of hepatocellular carcinoma (HCC). A cohort of 227 patients with single HCC was classified into a training set (n = 159) and a time-independent validated set (n = 68). A total of 647 radiomic features were extracted from multi-sequence MR images. The least absolute shrinkage and selection operator regression and decision tree methods were utilized for feature selection and radiomics signature construction. A multivariable logistic regression model incorporating clinico-radiological features and the fusion radiomics signature was built for prediction of CK19 status by evaluating area under curve (AUC). In the whole cohort, 57 patients were CK19 positive and 170 patients were CK19 negative. By combining 11 and 6 radiomic features extracted in arterial phase and hepatobiliary phase images, respectively, a fusion radiomics signature achieved AUCs of 0.951 and 0.822 in training and validation datasets. The final combined model integrated a-fetoprotein levels, arterial rim enhancement pattern, irregular tumor margin, and the fusion radiomics signature, with a sensitivity of 0.818 and specificity of 0.974 in the training cohort and that of 0.769 and 0.818 in the validated cohort. The nomogram based on the combined model showed satisfactory prediction performance in training (C-index 0.959) and validation (C-index 0.846) dataset. The combined model based on a fusion radiomics signature derived from arterial and hepatobiliary phase images of gadoxetic acid-enhanced MRI can be a reliable biomarker for CK19 status of HCC. • Arterial rim enhancement pattern and irregular tumor margin on hepatobiliary phase on gadoxetic acid-enhanced MRI can be useful for evaluating CK19 status of HCC. • A radiomics-based model performed better than the clinico-radiological model both in training and validation datasets for predicting CK19 status of HCC. • The nomogram based on the fusion radiomics signature can be easily used for CK19 stratification of HCC.

The Role of Preoperative Dynamic Contrast-enhanced 3.0-T MR Imaging in Predicting Early Recurrence in Patients With Early-Stage Hepatocellular Carcinomas After Curative Resection.

Objectives: Liver resection is potentially curative for early-stage hepatocellular carcinoma (eHCC) in patients with well-preserved liver function. The prognosis of these patients after resection is still unsatisfactory because of frequent early recurrence (ER). Therefore, we investigated the role of preoperative dynamic contrast-enhanced 3.0-T MR imaging in predicting ER of eHCC after curative resection.Methods From May 2014 to October 2017, we retrospectively analyzed 82 patients with eHCC who underwent dynamic MR imaging and subsequently underwent curative resection. Liver Imaging Reporting and Data System (LI-RADS) v2018 major and ancillary imaging features, as well as two non-LI-RADS MR imaging features (irregular tumor margin and tumor number), were evaluated. A multivariate Cox regression analysis was used to identify independent predictors, and two models (preoperative and postoperative prediction models) were developed.Results ER was observed in 25 patients (25/82, 30.5%). In the univariate analyses, preoperative alpha-fetoprotein (AFP) level >200 ng/ml, three MR imaging features (multifocal tumors, corona enhancement, and irregular tumor margin), and microvascular invasion (MVI) were associated with ER. In the multivariate analysis, corona enhancement (hazard ratio [HR]: 2.970; p = 0.013) and irregular tumor margin (HR: 2.377; p = 0.048) were independent predictors in the preoperative prediction model, and preoperative AFP level >200 ng/ml (HR: 2.493; p = 0.044) plus corona enhancement (HR: 3.046; p = 0.014) were independent predictors in the postoperative prediction model (microvascular invasion [MVI] was not; p = 0.061). When combined with both predictors, the specificity for ER in the preoperative prediction model was 98.2% (56/57), which was comparable to that of the postoperative prediction model [96.7% (55/57)].Conclusions Our results demonstrated that preoperative MR imaging features (corona enhancement and irregular tumor margin) have the potential to preoperatively identify high-risk ER patients with eHCC, with a specificity >90%.

Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI.

To explore the noninvasive prediction of hepatocellular carcinoma (HCC) with progenitor phenotype based on gadoxetic acid-enhanced magnetic resonance imaging (MRI). This retrospective study included 115 surgery-proven HCCs with preoperative gadoxetic acid-enhanced MRI from August 2015 to September 2018. Image features were reviewed. Quantitative image analysis was performed using histogram analysis. HCC with progenitor phenotype was defined as positive for either cytokeratin 19 (CK19) or epithelial cell adhesion molecule (EpCAM) expression. Statistically significant variables for identifying HCCs with progenitor phenotype were determined at multivariate analyses. ROC analyses were used to determined cutoff values and the diagnostic performance of significant variables and combinations. Prediction nomogram was constructed based on multivariate analysis. At multivariate regression analyses, AFP ≥ 155.25ng/mL (p < 0.001), skewness on T2WI ≤ 1.10 (p = 0.024), uniformity on pre-T1WI ≤ 0.91 (p = 0.024), irregular tumor margin (p = 0.006), targetoid appearance (p = 0.001), and the absence of mosaic architecture (p = 0.014) were significant predictors of HCCs expressing progenitor cell markers. Combing any three of those significant variables, it provides a diagnostic accuracy of 0.86 (95% CI 0.78-0.92) with sensitivity of 0.97 (95% CI 0.86-1.00), and specificity of 0.74 (95% CI 0.63-0.83). The C-index of the regression coefficient-based nomogram was 0.94 (95% CI 0.91-0.98). Noninvasive prediction of HCCs with progenitor phenotype can be achieved with high accuracy by integrated interpretation of biochemical and radiological information, representing a handy tool for precise patient management and the prediction of prognosis. • Qualitative image features of irregular tumor margin, targetoid appearance, and the absence of mosaic architecture are significant predictors of hepatocellular carcinoma with progenitor phenotype. • Quantitative analyses using whole-lesion histogram analysis provides additional information for the prediction of hepatocellular carcinoma with progenitor phenotype. • Noninvasive prediction of hepatocellular carcinoma with progenitor phenotype can be achieved with high accuracy by integrated interpretation of clinical information and qualitative and quantitative imaging analyses.

Differentiation of Hepatic Sclerosed Hemangiomas From Cavernous Hemangiomas Based on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Features.

The objective of this study was to evaluate the imaging features of a hepatic sclerosed hemangioma by comparing them with those of a cavernous hemangioma using magnetic resonance imaging with gadoxetic acid enhancement. Nine patients with hepatic sclerosed hemangiomas and 36 patients with cavernous hemangiomas (control group) who underwent gadoxetic acid-enhanced magnetic resonance imaging were included. Qualitative values (imaging findings and enhancement pattern [typical vs atypical enhancement]) and quantitative values (apparent diffusion coefficient) were evaluated. Patients with sclerosed hemangiomas showed significantly more irregular tumor margin on hepatobiliary phase images, peritumoral arterial enhancement, and a lower proportion of hyperintensity on heavily T2-weighted images compared with controls (all P values <0.05). In addition, the sclerosed hemangioma group had significantly more frequent atypical enhancement patterns than did the control group (88.9% vs 33.3%; P = 0.006). However, the mean apparent diffusion coefficient value of hemangiomas was not significantly different between the 2 groups (P = 0.639). Gadoxetic acid-enhanced magnetic resonance images can help in differentiating between hepatic sclerosed hemangiomas and cavernous hemangiomas.

Radiomic features to predict microvascular invasion in hepatocellular carcinoma based on conventional MRI: preliminary findings

Objective To identify the preoperative MRI findings for predicting microvascular invasion (MVI) using texture analysis (TA) on multiple MRI sequences. Methods Two hundred and fifty patients with HCC pathologically confirmed by surgery in Zhongshan Hospital from October 2015 to October 2016 were analyzed retrospectively. All patients underwent conventional MRI plain scan and dynamic contrast-enhanced examination within 2 weeks before operation. According to the ratio of 1∶1, the patients were divided into a training set (125 cases) and a test set (125 cases).The training set was used to establish a classifier to predict MVI of HCCs via the TA, and the test set was used to evaluate the performance of the classifier. An image analysis was performed using an in-house software contained a set of 2 415 features which were generated from all conventional axial sequences, including the T2WI, DWI, ADC map, and dynamic enhancement images.. A four-fold cross validation (FFCV) and sequential forward floating feature selection strategy (SFFS) were employed to select an optimal subset of features and a linear discriminant analysis (LDA) was employed to establish a classifier. The clinical laboratory examination, morphologic characteristics and quantitative analysis of conventional MR were used to compare the performance of predicting MVI with the classifier. A Chi-squared test or Fisher exact probablities test were used for categorical variables, and independent t test or Mann-Whitney U test were used for used for continuous variables. Factors with a P value less than 0.05 at univariate analyses were entered into the multivariate model to identify independent predictors. The Hosmer-Lemeshow test was performed to explain the goodness of fit of the multivariate logistic model. A receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance. Results The classifier set up by the training set consists of 13 texture features. When conventional MRI texture features of test set were used to judge whether there was MVI or not, the AUC of all texture features of arterial phase (AP) was the highest (0.506 3). Univariate regression analysis showed that there were significant differences in pathological grade (P=0.026), AFP level (P=0.033), lesion edge shape (P=0.038), AP enhancement (P=0.038), and AP peritumoral enhancement (P=0.008). Multivariate binary logistic regression analysis showed that peritumoral enhancement and texture classifier assessed MVI with P values of 0.005 and 0.001,which were independent risk factors for MVI. The significance level of Hosmer Lemeshow test was 0.796, indicating the goodness of fit of acceptable models. The AUCs of single variable, combined variable (including of AFP level, irregular tumor margin, enhancement intensity in AP and peritumoral enhancement in AP) and texture classifier for MVI were 0.588 to 0.627, 0.798 and 0.733, respectively. When compared the AUC of the combination features (including of AFP level, irregular tumor margin, enhancement intensity in AP and peritumoral enhancement in AP) with the classifier to identify MVI of HCC in the test set, no significant difference was found(P=0.108 6). However, although the sensitivity of them were same as 70.73%, the specificity of the combination features was mildly higher than that of classifier (82.14% vs. 78.57%). Conclusions Combination features of AFP level, tumor margin, enhancement intensity in AP and peritumoral enhancement in AP can be used to predict MVI of HCCs. It is a new method of noninvasive evaluation of MVI before operation. The performance of the classifier made by TA was not superior to that of combination features based on clinic and conventional MR sequences. Key words: Liver neoplasms; Magnetic resonance imaging; Radiomics; Microvascular invasion

A Radiomics-Based Biomarker for Cytokeratin 19 Status of Hepatocellular Carcinoma with Gadoxetic Acid-Enhanced MRI

Background: We aimed to develop and validate a radiomics-based model derived from gadoxetic acid-enhanced MR images to preoperatively identify cytokeratin (CK) 19 status of hepatocellular carcinoma (HCC). Methods: A cohort of 227 patients with single HCC was classified into a training set (n=159) and a time-independent validated set (n=68). A total of 647 radiomic features were extracted from multi-sequence MR images separately. The least absolute shrinkage and selection operator regression and decision tree methods were utilized for feature selection and radiomics signature construction. A further multivariable-based model was built for predicting CK19 status of HCC. Findings: By combining 11 and 6 radiomics features extracted in arterial phase and hepatobiliary phase images, respectively, a fusion radiomics signature achieved a satisfying performance for CK19 stratification. The final combined model integrated serum a-fetoprotein levels, arterial rim enhancement pattern, irregular tumor margin and the fusion radiomics signature, with a sensitivity of 0.818 and specificity of 0.974 in the training cohort and that of 0.769 and 0.818 in the validated cohort. The nomogram based on the combined model showed satisfactory prediction performance in training (C-index: 0.959) and validation (C-index: 0.846) dataset. Interpretations: The combined model based on a fusion radiomics signature derived from arterial phase and hepatobiliary phase images of gadoxetic acid-enhanced MRI can be a reliable biomarker for identifying CK19 status of HCC. Funding: This paper is supported by National Natural Science Foundation of China; Ministry of Science and Technology of China; Chinese Academy of Sciences; the Strategic Priority Research Program of Chinese Academy of Science; the Shanghai Sailing Program. Declaration of Interest: All authors declare no potential conflict of interest. Ethical Approval: The retrospective study was approved by our Institutional Review Board (approval number B2018-236) and the requirement for informed consent was waived.

Prognostic evaluation of pancreatic ductal adenocarcinoma: Associations between molecular biomarkers and CT imaging findings

ObjectivesTo investigate association between molecular biomarkers and computed tomography (CT) imaging findings in patients with pancreatic ductal adenocarcinoma (PDAC). MethodsFifty-three consecutive patients with PDAC (34 men and 19 women; mean age, 70.6 ± 8.1 years; range, 56–86 years) who underwent dynamic contrast-enhanced CT prior to pancreatectomy were included. The Ki-67 index and expressions of E-cadherin, Vimentin, and TWIST were immunohistochemically evaluated. Qualitative image analysis and histogram analysis of CT numbers were conducted. Clinical and molecular biomarkers were tested as possible prognostic factors for overall survival (OS) using Kaplan–Meier method and Cox proportional hazards regression. In addition, associations between CT imaging findings and significant molecular biomarkers were investigated. ResultsThe TNM stage (P = 0.018) and E-cadherin expression status (P = 0.018) were independently associated with OS. E-cadherin-negative PDACs had a worse prognosis than E-cadherin-positive PDACs (hazard ratio: 2.21). Irregular tumor margin was observed more frequently in E-cadherin-negative PDACs (54.7%) than in E-cadherin-positive PDACs (45.3%) (P = 0.00054). The kurtosis of CT number during the pancreatic parenchymal phase was significantly higher in E-cadherin-negative PDACs than in E-cadherin-positive PDACs (P = 0.035). ConclusionsE-cadherin suppression was found to be a prognostic factor for OS in patients with PDAC, and irregular tumor margin and kurtosis of CT numbers during the pancreatic parenchymal phase could be indicators for E-cadherin suppression.