Cardiac cine with compressed sensing real-time imaging and retrospective motion correction for free-breathing assessment of left ventricular function and strain in clinical practice.

Background: Free-breathing cardiac cine magnetic resonance imaging (MRI) comparable to the traditional breath-hold 2D segmented cine imaging (SegBH) is clinically required for cardiac function and strain analysis. This study is to assess the feasibility and accuracy of a free-breathing cardiac cine technique (RTCSCineMoCo) combined with highly accelerated real-time acquisition, compressed sensing, and fully automated non-rigid motion correction for left ventricular (LV) function and strain analysis, using SegBH as the reference and comparing with free-breathing single-shot real-time compressed sensing cine imaging (RTCSCine) without motion correction. Methods: A total of 67 patients scheduled for clinical cardiac MRI were included. Cine images were acquired using three techniques (SegBH, RTCSCineMoCo, RTCSCine) consecutively at 3.0 T. LV functional parameters, including ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volumes (SV), and LV mass (LVM) were measured and compared. Strain parameters including global radial (GRS), circumferential (GCS), and longitudinal (GLS) strain as well as corresponding time to peak strain (TPS) were computed by magnetic resonance (MR) feature tracking and compared. Subgroup analyses were performed according to heart rate (HR), left ventricular ejection fraction (LVEF), and etiology. Results: All quantitative parameters of LV function and strain measured by RTCSCineMoCo (r≥0.766) and RTCSCine (r≥0.712) showed strong correlations with SegBH (all P<0.001). LV functional parameters were not statistically different between RTCSCineMoCo and SegBH (all P>0.05), but an overestimation of LV end-systolic volume (LVESV) and underestimation of LVEF and LVM were observed using RTCSCine (all P<0.001). GRS, GCS, and GLS by RTCSCineMoCo and RTCSCine were significantly different than those by SegBH (all P<0.05). All TPS values by RTCSCineMoCo showed no significant differences (all P>0.05) compared with SegBH, but TPS in longitudinal directions (TPSL) by RTCSCine was significantly different (P=0.011). There were no significant differences for GRS or GCS between RTCSCineMoCo and SegBH in patients with HR <70 bpm or LVEF <50%. GRS by RTCSCineMoCo showed similar results compared to SegBH in patients with pulmonary hypertension. Conclusions: RTCSCineMoCo is a promising method for robust free-breathing cardiac cine imaging, yielding more precise quantitative analytic results for LV function compared with RTCSCine. RTCSCineMoCo mildly underestimated GRS, GCS, and GLS, but showed smaller bias compared to RTCSCine in LV strain analysis.

Macrotrabecular-massive hepatocellular carcinoma: imaging identification and prediction based on gadoxetic acid-enhanced magnetic resonance imaging.

OBJECTIVES: To identify image features of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) and to determine its role in predicting MTM-HCC. METHODS: Patients who underwent preoperative gadoxetic acid-enhanced MRI and with surgery proven HCC were retrospectively included. Imaging features were assessed according to Liver Imaging Reporting and Data System. Quantitative measurements were recorded. Clinical characteristics and imaging findings were compared between MTM-HCCs and non-MTM-HCCs. Predictive factors of MTM-HCC were screened with univariate analyses and then identified with multivariate logistic regression. A regression-based diagnostic model was constructed. ROC analyses were used to determine cutoff values, AUC, and corresponding 95% confidence interval (CI) of findings. The diagnostic performance was validated by 10-fold cross-validation. RESULTS: One hundred and forty-one patients with 37 MTM-HCCs were included. Multivariate analyses identified high platelet count (≥ 163.5 × 103/ul, odds ratio = 3.20; 95% CI: 1.29, 7.96; p = 0.012), low tumor-to-liver ADC ratio (≤ 1.05, odds ratio = 3.05; 95% CI, 1.23 - 7.55; p = 0.016), and necrosis or severe ischemia (odds ratio = 11.61; 95% CI, 3.99 - 33.76, p < 0.001) as independent predictors of MTM-HCC. Necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. The average AUCs were 0.81 (95% CI: 0.71, 0.90) for the regression-based diagnostic model, with a sensitivity of 57% and specificity of 92%. CONCLUSIONS: Necrosis or severe ischemia was a sensitive imaging feature of MTM-HCC. Noninvasive prediction of this subtype can be achieved with good accuracy and excellent specificity when findings were combined. KEY POINTS: • The macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC) represents an aggressive subtype of HCC and is associated with poor prognosis. • Imaging features of necrosis or severe ischemia alone helped identify 86% MTM-HCCs with a specificity of 66%. • A regression-based diagnostic model including high platelet count (≥ 163.5 × 103/ul), low tumor-to-liver ADC ratio (≤ 1.05), and necrosis or severe ischemia can provide noninvasive assessment of MTM-HCC with good accuracy and high specificity.

Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection.

BACKGROUND: This study aimed to evaluate the efficiency of gadoxetic acid-enhanced MRI-based radiomics features for prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients after surgical resection. METHODS: This prospective study approved by the Institutional Review Board enrolled 120 patients with pathologically confirmed HCC. Radiomics signatures (rad-scores) were built from radiomics features in 3 different regions of interest (ROIs) with the least absolute shrinkage and selection operator (LASSO) cox regression analysis. Preoperative clinical characteristics and semantic imaging features potentially associated with patient survival were evaluated to develop a clinic-radiological model. The radiomics features and clinic-radiological predictors were integrated into a joint model using multivariable Cox regression analysis. Kaplan-Meier analysis and log-rank tests were performed to compare the discriminative performance and evaluated on the validation cohort. RESULTS: The radiomics signatures showed a significant association with patient survival in both cohorts (all P<0.001). The BCLC (Barcelona clinic liver cancer) stage, non-smooth tumor margin, and the combined rad-score were independently associated with OS. Moreover, the combined model incorporating with clinic-radiological and radiomics features showed an improved predictive performance with C-index of 0.92 [95% confidence interval (CI): 0.87-0.97], compared to the clinic-radiological model (C-index, 0.86, 95% CI: 0.79-0.94; P=0.039) or the combined rad-score (C-index, 0.88, 95% CI: 0.81-0.95; P=0.016). CONCLUSIONS: Radiomics features along with clinic-radiological predictors can efficiently aid in preoperative HCC prognosis prediction after surgical resection and enable a step forward precise medicine.

Preoperative prediction of hepatocellular carcinoma with highly aggressive characteristics using quantitative parameters derived from hepatobiliary phase MR images.

BACKGROUND: To prospectively determine whether the quantitative imaging parameters derived from the hepatobiliary phase (HBP) can be used for the preoperative prediction of hepatocellular carcinoma (HCC) with highly aggressive characteristics. METHODS: One hundred and three patients with surgical-proven HCC were included from July 2015 to June 2018. Two independent reviewers measured signal intensity (SI) of liver and tumor, and quantitative parameters, including relative tumor enhancement (RTE), tumor to liver contrast ratio (TLR), tumor enhancement index (TEI), and relative enhancement ratio (RER) were calculated. The aggressive characteristics of HCC were identified by using the Ki-67 labeling index (LI), and patients were classified into low aggressive (Ki-67 LI ≤10%) and high aggressive (Ki-67 LI >10%) groups. The difference of quantitative parameters between two groups was assessed, and the correlation between quantitative parameters and Ki-67 LI was explored. Receiver operating characteristic analyses was used to evaluate the predictive performance of quantitative parameters. RESULTS: The values of RTE, TLR, TEI, and RER, were significantly lower in the highly aggressive group than low aggressive group (P<0.05), and negative correlations were obtained between these quantitative parameters and Ki-67 LI (r ranges from -0.41 to -0.22, P<0.05). TLR demonstrated the highest predictive performance with the area under curve (AUC) of 0.83 [95% confidence interval (CI): 0.75-0.90], sensitivity of 89.0% and specificity of 63.3%, and subsequent with RER, TEI, and RTE with AUC of 0.78 (95% CI: 0.68-0.85), 0.74 (95% CI: 0.64-0.82) and 0.68 (95% CI: 0.58-0.77), respectively. Good inter-observer and intra-observer agreement were found in all parameters. CONCLUSIONS: TLR showed the highest predictive performance in highly aggressive HCC. Quantitative parameters based on HBP could preoperatively predict the aggressiveness of HCC.

Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning.

BACKGROUND: Combined hepatocellular and cholangiocarcinoma (CHC) and intrahepatic cholangiocarcinoma (ICC) are hard to identify in clinical practice preoperatively. This study looked to develop and confirm a radiomics-based model for preoperative differentiation CHC from ICC. METHODS: The model was developed in 86 patients with ICC and 46 CHC, confirmed in 37 ICC and 20 CHC, and data were collected from January 2014 to December 2018. The radiomics scores (Radscores) were built from radiomics features of contrast-enhanced computed tomography in 12 regions of interest (ROI). The Radscore and clinical-radiologic factors were integrated into the combined model using multivariable logistic regression. The best-combined model constructed the radiomics-based nomogram, and the performance was assessed concerning its calibration, discrimination, and clinical usefulness. RESULTS: The radiomics features extracted from tumor ROI in the arterial phase (AP) with preprocessing were selected to build Radscore and yielded an area under the curve (AUC) of 0.800 and 0.789 in training and validation cohorts, respectively. The radiomics-based model contained Radscore and 4 clinical-radiologic factors showed the best performance (training cohort, AUC =0.942; validation cohort, AUC =0.942) and good calibration (training cohort, AUC =0.935; validation cohort, AUC =0.931). CONCLUSIONS: The proposed radiomics-based model may be used conveniently to the preoperatively differentiate CHC from ICC.

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

OBJECTIVES: To explore the noninvasive prediction of hepatocellular carcinoma (HCC) with progenitor phenotype based on gadoxetic acid-enhanced magnetic resonance imaging (MRI). METHODS: 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. RESULTS: At multivariate regression analyses, AFP ≥ 155.25 ng/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). CONCLUSIONS: 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. KEY POINTS: • 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.

Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: A prospective study.

OBJECTIVE: To investigate the value of whole-lesion texture analysis on preoperative gadoxetic acid enhanced magnetic resonance imaging (MRI) for predicting tumor Ki-67 status after curative resection in patients with hepatocellular carcinoma (HCC). METHODS: This study consisted of 89 consecutive patients with surgically confirmed HCC. Texture features were extracted from multiparametric MRI based on whole-lesion regions of interest. The Ki-67 status was immunohistochemical determined and classified into low Ki-67 (labeling index ≤15%) and high Ki-67 (labeling index >15%) groups. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were applied for generating the texture signature, clinical nomogram and combined nomogram. The discrimination power, calibration and clinical usefulness of the three models were evaluated accordingly. Recurrence-free survival (RFS) rates after curative hepatectomy were also compared between groups. RESULTS: A total of 13 texture features were selected to construct a texture signature for predicting Ki-67 status in HCC patients (C-index: 0.878, 95% confidence interval: 0.791-0.937). After incorporating texture signature to the clinical nomogram which included significant clinical variates (AFP, BCLC-stage, capsule integrity, tumor margin, enhancing capsule), the combined nomogram showed higher discrimination ability (C-index: 0.936vs. 0.795, P<0.001), good calibration (P>0.05 in Hosmer-Lemeshow test) and higher clinical usefulness by decision curve analysis. RFS rate was significantly lower in the high Ki-67 group compared with the low Ki-67 group after curative surgery (63.27%vs. 85.00%, P<0.05). CONCLUSIONS: Texture analysis on gadoxetic acid enhanced MRI can serve as a noninvasive approach to preoperatively predict Ki-67 status of HCC after curative resection. The combination of texture signature and clinical factors demonstrated the potential to further improve the prediction performance.

Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study of the Liver (EASL) criteria are widely used for diagnosing hepatocellular carcinoma (HCC). Radiomics allows further quantitative tumor heterogeneity profiling. This study aimed to compare the diagnostic accuracies of the version 2018 (v2018) EASL, LI-RADS criteria and radiomics models for HCC in high-risk patients. METHODS: Ethical approval by the institutional review board and informed consent were obtained for this study. From July 2015 to September 2018, consecutive high-risk patients were enrolled in our tertiary care hospital and underwent gadoxetic acid-enhanced magnetic resonance (MR) imaging and subsequent hepatic surgery. We constructed a multi-sequence-based three-dimensional whole-tumor radiomics signature by least absolute shrinkage and selection operator model and multivariate logistic regression analysis. The diagnostic accuracies of the radiomics signature was validated in an independent cohort and compared with the EASL and LI-RADS criteria reviewed by two independent radiologists. RESULTS: Two hundred twenty-nine pathologically confirmed nodules (173 HCCs, mean size: 5.74 ± 3.17 cm) in 211 patients were included. Among them, 201 patients (95%) were infected with hepatitis B virus (HBV). The sensitivity and specificity were 73 and 71% for the radiomics signature, 91 and 71% for the EASL criteria, and 86 and 82% for the LI-RADS criteria, respectively. The areas under the receiver operating characteristic curves (AUCs) of the radiomics signature (0.810), LI-RADS (0.841) and EASL criteria (0.811) were comparable. CONCLUSIONS: In HBV-predominant high-risk patients, the multi-sequence-based MR radiomics signature, v2018 EASL and LI-RADS criteria demonstrated comparable overall accuracies for HCC.

Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction.

BACKGROUND: This study was performed to prospectively develop and validate a radiomics nomogram for predicting postoperative early recurrence (≤1 year) of hepatocellular carcinoma (HCC) using whole-lesion radiomics features on preoperative gadoxetic acid-enhanced magnetic resonance (MR) images. METHODS: In total, 155 patients (training cohort: n = 108; validation cohort: n = 47) with surgically confirmed HCC were enrolled in this IRB-approved prospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumour margins on multi-sequence MR images. Radiomics features were generated and selected to build a radiomics score using the least absolute shrinkage and selection operator (LASSO) method. Clinical characteristics and qualitative imaging features were identified by two independent radiologists and combined to establish a clinical-radiological nomogram. A radiomics nomogram comprising the radiomics score and clinical-radiological risk factors was constructed based on multivariable logistic regression analysis. Diagnostic performance and clinical usefulness were measured by receiver operation characteristic (ROC) and decision curves. RESULTS: In total, 14 radiomics features were selected to construct the radiomics score. For the clinical-radiological nomogram, the alpha-fetoprotein (AFP) level, gross vascular invasion and non-smooth tumour margin were included. The radiomics nomogram integrating the radiomics score with clinical-radiological risk factors showed better discriminative performance (AUC = 0.844, 95%CI, 0.769 to 0.919) than the clinical-radiological nomogram (AUC = 0.796, 95%CI, 0.712 to 0.881; P = 0.045), with increased clinical usefulness confirmed using a decision curve analysis. CONCLUSIONS: Incorporating multiple predictive factors, the radiomics nomogram demonstrated great potential in the preoperative prediction of early HCC recurrence after surgery.

Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade.

BACKGROUND: The aim of this study was to prospectively evaluate the diagnostic efficacy of diffusion kurtosis imaging (DKI) in predicting microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) with comparison to the conventional diffusion-weighted imaging (DWI). METHODS: This prospective study was approved by the Institutional Review Board, and written informed consent was obtained from all patients. From September 2015 to January 2017, 74 consecutive HCC patients were enrolled in this study. Preoperative magnetic resonance imaging including DKI protocol was performed, and patients were followed up for at least one year after surgery. Diffusion parameters including the mean corrected apparent diffusion coefficient (MD), mean apparent kurtosis coefficient (MK), and apparent diffusion coefficient (ADC) were calculated. Differences of diffusion parameters among different histopathological groups were compared. For parameters that were significantly different between pathological groups, receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic efficiency for identifying MVI and predicting high-grade HCC. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of clinical and laboratory variables and diffusion parameters as risk factors for early recurrence (≤1 year). RESULTS: Among all the studied diffusion parameters, only MK differed significantly between the MVI-positive and MVI-negative group (0.91±0.10 vs. 0.82±0.09, P<0.001), and showed moderate diagnostic efficacy (AUC =0.77) for identifying MVI. High-grade HCCs showed significantly higher MK values (0.93±0.10 vs. 0.82±0.09, P<0.001), along with MD (1.34±0.18 vs. 1.54±0.22, P<0.001) and ADC values (1.17±0.15 vs. 1.30±0.16, P=0.001) than low-grade HCCs. For differentiating high-grade from low-grade HCCs, MK demonstrated a higher area under the ROC curve (AUC) and significantly higher specificity than MD and ADC (AUC =0.81 vs. 0.76 and 0.74; specificity =82.2% vs. 60.0% and 60.0%, P=0.02). In addition, higher MK (OR =5.700, P=0.002) and Barcelona Clinic Liver Cancer (BCLC) stage C (OR =6.329, P=0.005) were independent risk factors for early HCC recurrence. CONCLUSIONS: DKI-derived MK values outperformed conventional ADC values for predicting MVI and histologic grade of HCC, and are associated with increased risk of early tumor recurrence.

Hepatocellular carcinoma: Can LI-RADS v2017 with gadoxetic-acid enhancement magnetic resonance and diffusion-weighted imaging improve diagnostic accuracy?

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS), supported by the American College of Radiology (ACR), has been developed for standardizing the acquisition, interpretation, reporting, and data collection of liver imaging examinations in patients at risk for hepatocellular carcinoma (HCC). Diffusion-weighted imaging (DWI), which is described as an ancillary imaging feature of LI-RADS, can improve the diagnostic efficiency of LI-RADS v2017 with gadoxetic acid-enhanced magnetic resonance imaging (MRI) for HCC. AIM: To determine whether the use of DWI can improve the diagnostic efficiency of LI-RADS v2017 with gadoxetic acid-enhanced magnetic resonance MRI for HCC. METHODS: In this institutional review board-approved study, 245 observations of high risk of HCC were retrospectively acquired from 203 patients who underwent gadoxetic acid-enhanced MRI from October 2013 to April 2018. Two readers independently measured the maximum diameter and recorded the presence of each lesion and assigned scores according to LI-RADS v2017. The test was used to determine the agreement between the two readers with or without DWI. In addition, the sensitivity (SE), specificity (SP), accuracy (AC), positive predictive value (PPV), and negative predictive value (NPV) of LI-RADS were calculated. Youden index values were used to compare the diagnostic performance of LI-RADS with or without DWI. RESULTS: Almost perfect interobserver agreement was obtained for the categorization of observations with LI-RADS (kappa value: 0.813 without DWI and 0.882 with DWI). For LR-5, the diagnostic SE, SP, and AC values were 61.2%, 92.5%, and 71.4%, respectively, with or without DWI; for LR-4/5, they were 73.9%, 80%, and 75.9% without DWI and 87.9%, 80%, and 85.3% with DWI; for LR-4/5/M, they were 75.8%, 58.8%, and 70.2% without DWI and 87.9%, 58.8%, and 78.4% with DWI; for LR- 4/5/TIV, they were 75.8%, 75%, and 75.5% without DWI and 89.7%, 75%, and 84.9% with DWI. The Youden index values of the LI-RADS classification without or with DWI were as follows: LR-4/5: 0.539 vs 0.679; LR-4/5/M: 0.346 vs 0.467; and LR-4/5/TIV: 0.508 vs 0.647. CONCLUSION: LI-RADS v2017 has been successfully applied with gadoxetate-enhanced MRI for patients at high risk for HCC. The addition of DWI significantly increases the diagnostic efficiency for HCC.

Noninvasive imaging of hepatocellular carcinoma: From diagnosis to prognosis.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a major public health problem worldwide. Hepatocarcinogenesis is a complex multistep process at molecular, cellular, and histologic levels with key alterations that can be revealed by noninvasive imaging modalities. Therefore, imaging techniques play pivotal roles in the detection, characterization, staging, surveillance, and prognosis evaluation of HCC. Currently, ultrasound is the first-line imaging modality for screening and surveillance purposes. While based on conclusive enhancement patterns comprising arterial phase hyperenhancement and portal venous and/or delayed phase wash-out, contrast enhanced dynamic computed tomography and magnetic resonance imaging (MRI) are the diagnostic tools for HCC without requirements for histopathologic confirmation. Functional MRI techniques, including diffusion-weighted imaging, MRI with hepatobiliary contrast agents, perfusion imaging, and magnetic resonance elastography, show promise in providing further important information regarding tumor biological behaviors. In addition, evaluation of tumor imaging characteristics, including nodule size, margin, number, vascular invasion, and growth patterns, allows preoperative prediction of tumor microvascular invasion and patient prognosis. Therefore, the aim of this article is to review the current state-of-the-art and recent advances in the comprehensive noninvasive imaging evaluation of HCC. We also provide the basic key concepts of HCC development and an overview of the current practice guidelines.

Intrahepatic cholangiocarcinoma in the setting of HBV-related cirrhosis: Differentiation with hepatocellular carcinoma by using Intravoxel incoherent motion diffusion-weighted MR imaging.

Accurate preoperative differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) in the setting of cirrhotic liver is of great clinical significance because the treatment and prognosis of these entities differ markedly. Through a retrospectively research, we sought to determine the diagnostic performances of intravoxel incoherent motion (IVIM) and diffusion weighted imaging (DWI) parameters in the differentiating of ICC and HCC. According to the results, we found that apparent diffusion coefficient (ADC) derived from mono-exponential model and true ADC (ADCslow) derived from bi-exponential model can be used to distinguish the ICC and HCC, and ADCslowentailed the higher diagnostic performance than ADC. However, pseudo-ADC (ADCfast) and perfusion fraction (f) can not be used to differentiate ICC and HCC. These results suggested that IVIM and DWI parameters can be useful in differentiating ICC and HCC and might be helpful in selecting the treatment plan and predicting prognosis.

[The Value of Diffusion Kurtosis Imaging in Predicting Post-surgery Early Recurrence of Hepatocellular Carcinoma].

OBJECTIVE: To determine the value of diffusion kurtosis imaging for predicting one-year early recurrence (ER) of hepatocellular carcinoma (HCC) after curative resection. METHODS: 55 HCC patients were enrolled into this prospective study and received preoperative magnetic resonance (MR) examination including diffusion kurtosis imaging (DKI). The patients were followed up for at least one year after curative resection. The morphological features of HCC were assessed using the conventional contrast-enhanced MR images. Mean diffusivity (MD) and mean kurtosis (MK) were calculated for the intratumoral and peritumoral regions. Univariate and multivariate logistic regression analyses were performed to assess the relative value of these parameters as a potential predictor of ER. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic performance of these quantitative parameters. RESULTS: Increased alpha-fetoprotein (AFP), BCLC stage, tumor number (≥2), high grade HCC, peritumoral MK, and decreased peritumoral MD were associated with higher one-year ER of HCC (P<0.05). The multivariate analyses confirmed that high grade HCC 〔odds ratio (OR)=5.37,95% confidence interval (CI):1.01-28.50,P=0.048〕 and increased peritumoral MK (OR=5.38,95%CI:1.53-18.92,P=0.009) were independent risk factors for the ER of HCC. The area under curve was 0.79 (P<0.001) for peritumoral MK, with an optimal sensitivity of 85.2% and specificity of 64.3% at the cut-off of 0.96. CONCLUSIONS: Peritumoral MK has moderate diagnostic performance in predicting ER of HCC. Higher peritumoral MK value in combination with high-grade HCC are potential biomarkers for predicting one-year ER of HCC.