Diagnostic performance of apparent diffusion coefficient for predicting histological grade of hepatocellular carcinoma

Multiparametric assessment of microvascular invasion in hepatocellular carcinoma using gadoxetic acid-enhanced MRI

PURPOSE

To elucidate how precisely microvascular invasion (MVI) in hepatocellular carcinoma (HCC) can be predicted using multiparametric assessment of gadoxetic acid-enhanced MRI.

METHODS

In this retrospective single-center study, patients who underwent liver resection or transplantation of HCC were evaluated. Data obtained in patients who underwent liver resection were used as the training set. Nine kinds of MR findings for predicting MVI were compared between HCCs with and without MVI by univariate analysis, followed by multiple logistic regression analysis. Using significant findings, a predictive formula for diagnosing MVI was obtained. The diagnostic performance of the formula was investigated in patients who underwent liver resection (validation set 1) and in patients who underwent liver transplantation (validation set 2) using a receiver operating characteristic curve analysis. The area under the curves (AUCs) of these three groups were compared.

RESULTS

A total of 345 patients with 356 HCCs were selected for analysis. Tumor diameter (D) (P = 0.021), tumor washout (TW) (P < 0.01), and peritumoral hypointensity in the hepatobiliary phase (PHH) (P < 0.01) were significantly associated with MVI after multivariate analysis. The AUCs for predicting MVI of the predictive formula were as follows: training set, 0.88 (95% confidence interval (CI) 0.82,0.93); validation set 1, 0.81 (95% CI 0.73,0.87); validation set 2, 0.67 (95% CI 0.51,0.80). The AUCs were not significantly different among three groups (training set vs validation set 1; P = 0.15, training set vs validation set 2; P = 0.09, validation set 1 vs validation set 2; P = 0.29, respectively).

CONCLUSION

Our multiparametric assessment of gadoxetic acid-enhanced MRI performed quite precisely and with good reproducibility for predicting MVI.

Non-invasive preoperative prediction of Edmondson-Steiner grade of hepatocellular carcinoma based on contrast-enhanced ultrasound using ensemble learning

Purpose

This study aimed to explore the clinical value of non-invasive preoperative Edmondson-Steiner grade of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS).

Methods

212 cases of HCCs were retrospectively included, including 83 cases of high-grade HCCs and 129 cases of low-grade HCCs. Three representative CEUS images were selected from the arterial phase, portal vein phase, and delayed phase and stored in a 3-dimensional array. ITK-SNAP was used to segment the tumor lesions manually. The Radiomics method was conducted to extract high-dimensional features on these contrast-enhanced ultrasound images. Then the independent sample T-test and the Least Absolute Shrinkage and Selection Operator (LASSO) were employed to reduce the feature dimensions. The optimized features were modeled by a classifier based on ensemble learning, and the Edmondson Steiner grading was predicted in an independent testing set using this model.

Results

A total of 1338 features were extracted from the 3D images. After the dimension reduction, 10 features were finally selected to establish the model. In the independent testing set, the integrated model performed best, with an AUC of 0.931.

Conclusion

This study proposed an Edmondson-Steiner grading method for HCC with CEUS. The method has good classification performance on independent testing sets, which can provide quantitative analysis support for clinical decision-making.

Radiologic features of hepatocellular carcinoma related to prognosis

The cross-sectional imaging findings play a crucial role in the diagnosis of hepatocellular carcinoma (HCC). Recent studies have shown that imaging findings of HCC are not only relevant for the diagnosis of HCC, but also for identifying genetic and pathologic characteristics and determining prognosis. Imaging findings such as rim arterial phase hyperenhancement, arterial phase peritumoral hyperenhancement, hepatobiliary phase peritumoral hypointensity, non-smooth tumor margin, low apparent diffusion coefficient, and the LR-M category of the Liver Imaging-Reporting and Data System have been reported to be associated with poor prognosis. In contrast, imaging findings such as enhancing capsule appearance, hepatobiliary phase hyperintensity, and fat in mass have been reported to be associated with a favorable prognosis. Most of these imaging findings were examined in retrospective, single-center studies that were not adequately validated. However, the imaging findings can be applied for deciding the treatment strategy for HCC, if their significance can be confirmed by a large multicenter study. In this literature, we would like to review imaging findings related to the prognosis of HCC as well as their associated clinicopathological characteristics.

Preoperative Evaluation of Hepatocellular Carcinoma Differentiation Using Contrast-Enhanced Ultrasound-Based Deep-Learning Radiomics Model

Objective

Distinguishing the degree of differentiation, hepatocellular carcinoma (HCC) has important clinical significance in the therapeutic decision-making and patient prognosis evaluation.

Methods

We developed a deep-learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) to evaluate the differentiation of HCC noninvasive. We retrospectively analyzed HCC patients who had undergone resection and CEUS one week preoperatively between November 2015 and August 2022. Enrolled patients were randomly divided into training (n=190) and testing (n=82) cohorts in a 7:3 ratio. The depth of learning and radiological characteristics reflecting the differentiation degree of HCC were extracted, and the least absolute shrinkage and selection operator(LASSO) was used for feature selection to obtain the most valuable features and then build a DLR model based on the useful features.

Results

The deep-learning Radiomics model could accurately predict the degree of differentiation of HCC; the area under the curve of the DLR model in the training and testing cohorts was 0.969 and 0.932, respectively. The accuracy, sensitivity, and specificity of the CEUS-based DLR model for predicting the differentiation of HCC were 0.915, 0.938, and 0.900, respectively, in the testing cohort. The decision curve analysis confirmed that the combined model predicted good overall net income for differentiation.

Conclusion

The CEUS-based DLR model provides an easy-to-use, visual, and personalized tool for predicting the differentiation of HCC and can help doctors formulate more favorable treatment plans for patients.

Impact of apparent diffusion coefficient on prognosis of early hepatocellular carcinoma: a case control study

BACKGROUND

We investigated the usefulness of apparent diffusion coefficients (ADC) from diffusion-weighted images (DWI) obtained using magnetic resonance imaging (MRI) for prognosis of early hepatocellular carcinoma (HCC): Barcelona Clinic Liver Cancer (BCLC) stage 0 and A.

METHODS

We enrolled 102 patients who had undergone surgical resection for early HCC: BCLC stage 0 and A, and calculated their minimum ADC using DWI-MRI. We divided patients into ADC^High (n = 72) and ADC^Low (n = 30) groups, and compared clinicopathological factors between the two groups.

RESULTS

The ADC^Low group showed higher protein induced by vitamin K absence-II (PIVKA-II) levels (p = 0.02) compared with the ADC^High group. In overall survival, the ADC^Low group showed significantly worse prognosis than the ADC^High group (p < 0.01). Univariate analysis identified multiple tumors, infiltrative growth, high PIVKA-II, and low ADC value as prognostic factors. Multivariate analysis identified infiltrative growth and low ADC value as an independent prognostic factor.

CONCLUSION

ADC values can be used to estimate the prognosis of early HCC.

Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma

BACKGROUND

To assess the potential of apparent diffusion coefficient (ADC) map in predicting aggressiveness of papillary thyroid carcinoma (PTC) based on whole-tumor histogram-based analysis.

METHODS

A total of 88 patients with PTC confirmed by pathology, who underwent neck magnetic resonance imaging, were enrolled in this retrospective study. Whole-lesion histogram features were extracted from ADC maps and compared between the aggressive and non-aggressive groups. Multivariable logistic regression analysis was performed for identifying independent predictive factors. Receiver operating characteristic curve analysis was used to evaluate the performances of significant factors, and an optimal predictive model for aggressiveness of PTC was developed.

RESULTS

The aggressive and non-aggressive groups comprised 67 (mean age, 44.03 ± 13.99 years) and 21 (mean age, 43.86 ± 12.16 years) patients, respectively. Five histogram features were included into the final predictive model. ADC_firstorder_TotalEnergy had the best performance (area under the curve [AUC] = 0.77). The final combined model showed an optimal performance, with AUC and accuracy of 0.88 and 0.75, respectively.

CONCLUSIONS

Whole-lesion histogram analysis based on ADC maps could be utilized for evaluating aggressiveness in PTC.

Diagnostic Accuracy of the Apparent Diffusion Coefficient for Microvascular Invasion in Hepatocellular Carcinoma: A Meta-analysis

BACKGROUND AND AIMS

Microvascular invasion (MVI) is a major risk factor for the early recurrence of hepatocellular carcinoma (HCC) and it seriously worsens the prognosis. Accurate preoperative evaluation of the presence of MVI could greatly benefit the treatment management and prognosis prediction of HCC patients. The study aim was to evaluate the diagnostic performance of the apparent diffusion coefficient (ADC), a quantitative parameter for the preoperative diagnosis MVI in HCC patients.

METHODS

Original articles about diffusion-weighted imaging (DWI) and/or intravoxel incoherent motion (IVIM) conducted on a 3.0 or 1.5 Tesla magnetic resonance imaging (MRI) system indexed through January 17, 2021were collected from MEDLINE/PubMed, Web of Science, EMBASE, and the Cochrane Library. Methodological quality was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUROC) were calculated, and meta-regression analysis was performed using a bivariate random effects model through a meta-analysis.

RESULTS

Nine original articles with a total of 988 HCCs were included. Most studies had low bias risk and minimal applicability concerns. The pooled sensitivity, specificity and AUROC of the ADC value were 73%, 70%, and 0.78, respectively. The time interval between the index test and the reference standard was identified as a possible source of heterogeneity by subgroup meta-regression analysis.

CONCLUSIONS

Meta-analysis showed that the ADC value had moderate accuracy for predicting MVI in HCC. The time interval accounted for the heterogeneity.

The Roles of Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion Diffusion-Weighted Imaging Parameters in Preoperative Evaluation of Pathological Grades and Microvascular Invasion in Hepatocellular Carcinoma

Background

Currently, there are disputes about the parameters of diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and diffusion-weighted imaging (DWI) in predicting pathological grades and microvascular invasion (MVI) in hepatocellular carcinoma (HCC). The aim of our study was to investigate and compare the predictive power of DKI and IVIM-DWI parameters for preoperative evaluation of pathological grades and MVI in HCC.

Methods

PubMed, Web of Science, and Embase databases were searched for relevant studies published from inception to October 2021. Review Manager 5.3 was used to summarize standardized mean differences (SMDs) of mean kurtosis (MK), mean diffusivity (MD), tissue diffusivity (D), pseudo diffusivity (D*), perfusion fraction (f), mean apparent diffusion coefficient (ADCmean), and minimum apparent diffusion coefficient (ADCmin). Stata12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC). Overall, 42 up-to-standard studies with 3,807 cases of HCC were included in the meta-analysis.

Results

The SMDs of ADCmean, ADCmin, and D values, but not those of D* and f values, significantly differed between well, moderately, and poorly differentiated HCC (P < 0.01). The sensitivity, specificity, and AUC of the MK, D, ADCmean, and ADCmin for preoperative prediction of poorly differentiated HCC were 69%/94%/0.89, 87%/80%/0.89, 82%/75%/0.86, and 83%/64%/0.81, respectively. In addition, the sensitivity, specificity, and AUC of the D and ADCmean for preoperative prediction of well-differentiated HCC were 87%/83%/0.92 and 82%/88%/0.90, respectively. The SMDs of ADCmean, ADCmin, D, MD, and MK values, but not f values, showed significant differences (P < 0.01) between MVI-positive (MVI+) and MVI-negative (MVI-) HCC. The sensitivity and specificity of D and ADCmean for preoperative prediction of MVI+ were 80%/80% and 74%/71%, respectively; the AUC of the D (0.87) was significantly higher than that of ADCmean (0.78) (Z = −2.208, P = 0.027). Sensitivity analysis showed that the results of the above parameters were stable and reliable, and subgroup analysis confirmed a good prediction effect.

Conclusion

DKI parameters (MD and MK) and IVIM-DWI parameters (D value, ADCmean, and ADCmin) can be used as a noninvasive and simple preoperative examination method to predict the grade and MVI in HCC. Compared with ADCmean and ADCmin, MD and D values have higher diagnostic efficacy in predicting the grades of HCC, and D value has superior diagnostic efficacy to ADCmean in predicting MVI+ in HCC. However, f value cannot predict the grade or MVI in HCC.

Adaptive multimodal fusion with attention guided deep supervision net for grading hepatocellular carcinoma

Multimodal medical imaging plays a crucial role in the diagnosis and characterization of lesions. However, challenges remain in lesion characterization based on multimodal feature fusion. First, current fusion methods have not thoroughly studied the relative importance of characterization modals. In addition, multimodal feature fusion cannot provide the contribution of different modal information to inform critical decision-making. In this study, we propose an adaptive multimodal fusion method with an attention-guided deep supervision net for grading hepatocellular carcinoma (HCC). Specifically, our proposed framework comprises two modules: attention-based adaptive feature fusion and attention-guided deep supervision net. The former uses the attention mechanism at the feature fusion level to generate weights for adaptive feature concatenation and balances the importance of features among various modals. The latter uses the weight generated by the attention mechanism as the weight coefficient of each loss to balance the contribution of the corresponding modal to the total loss function. The experimental results of grading clinical HCC with contrast-enhanced MR demonstrated the effectiveness of the proposed method. A significant performance improvement was achieved compared with existing fusion methods. In addition, the weight coefficient of attention in multimodal fusion has demonstrated great significance in clinical interpretation.

Added Value of Quantitative Apparent Diffusion Coefficients for Identifying Small Hepatocellular Carcinoma from Benign Nodule Categorized as LI-RADS 3 and 4 in Cirrhosis

BACKGROUND AND AIMS

Correct identification of small hepatocellular carcinomas (HCCs) and benign nodules in cirrhosis remains challenging, quantitative apparent diffusion coefficients (ADCs) have shown potential value in characterization of benign and malignant liver lesions. We aimed to explore the added value of ADCs in the identification of small (≤3 cm) HCCs and benign nodules categorized as Liver Imaging Reporting and Data System (LI-RADS) 3 (LR-3) and 4 (LR-4) in cirrhosis.

METHODS

Ninety-seven cirrhosis patients with 109 small nodules (70 HCCs, 39 benign nodules) of LR-3 and 4 LR-4 based on major and ancillary magnetic resonance imaging features were included. Multiparametric quantitative ADCs of the lesions, including the mean ADC (ADC_mean), minimum ADC (ADC_min), maximal ADC (ADC_max), ADC standard deviation (ADC_std), and mean ADC value ratio of lesion-to-liver parenchyma (ADC_ratio) were calculated. Regarding the joint diagnosis, a nomogram model was plotted using multivariate logistic regression analysis. The performance was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The ADC_mean, ADC_min, ADC_ratio, and ADC_std were significantly associated with the identification of small HCC and benign nodules (p<0.001). For the joint diagnosis, the LI-RADS category (odds ratio [OR]=12.50), ADC_min (OR=0.14), and ADC_ratio (OR=0.12) were identified as independent factors for distinguishing HCCs from benign nodules. The joint nomogram model showed good calibration and discrimination, with a C-index of 0.947. Compared with the LI-RADS category alone, this nomogram model demonstrated a significant improvement in diagnostic performance, with AUC increasing from 0.820 to 0.967 (p=0.001).

CONCLUSIONS

The addition of quantitative ADCs could improve the identification of small HCC and benign nodules categorized as LR-3 and 4 LR-4 in patients with cirrhosis.

The Benefit of Apparent Diffusion Coefficient in Evaluating the Invasiveness of Hepatocellular Carcinoma

Background

This study aimed to evaluate hepatocellular carcinoma (HCC) invasiveness using the apparent diffusion coefficient (ADC).

Methods

Eighty-one patients with HCC confirmed by pathology and examined by preoperative magnetic resonance imaging diffusion-weighted imaging from January 2015 to September 2020 were retrospectively analyzed. Clinical and pathological data were recorded. The minimum ADC (ADCmin), average ADC (ADCmean), and the ratio of ADCmean to normal-appearing hepatic parenchyma ADC (ADCnahp) were assessed. The associations between clinical information, ADC value, and HCC invasiveness (microvascular invasion [MVI], tumor differentiation, and Ki-67 expression) were evaluated statistically. Independent risk factors related to HCC invasiveness were screened using binary logistic regression, and the diagnostic efficiency was evaluated by the receiver operating characteristic curve and its area under the curve (AUC) value.

Results

Tumor size was related to HCC MVI and tumor differentiation (P < 0.05). HCC MVI was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.860, 0.860, and 0.909, respectively. If these were combined with tumor size, the AUC value increased to 0.912. The degree of tumor differentiation was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.719, 0.708, and 0.797, respectively. If these were combined with tumor size, the AUC value increased to 0.868. Ki-67 expression was associated with ADCmin, ADCmean, and the ADCmean-to-ADCnahp ratio (all P < 0.05) with AUC values of 0.731, 0.747, and 0.746, respectively. Combined them, the AUC value increased to 0.763.

Conclusions

The findings indicated that the ADC value has significant potential for the non-invasive preoperative evaluation of HCC invasiveness.

A study of the correlations between IVIM-DWI parameters and the histologic differentiation of hepatocellular carcinoma

The present study aimed to investigate the value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in the preoperative prediction of the histologic differentiation of hepatocellular carcinoma (HCC). Seventy HCC patients were scanned with a 3.0 T magnetic resonance scanner. The values of apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and the fraction of the fast apparent diffusion coefficient (f) were measured. Analysis of variance was used to compare the differences in parameters between groups with different degrees of histologic differentiation. p < 0.05 was considered statistically significant. Receiver operating characteristic (ROC) curves were used to analyse the efficacy of IVIM-DWI parameters for predicting the histologic differentiation of HCC. The ADC and D values for well, moderately and poorly differentiated HCC were 1.35 ± 0.17 × 10^-3 mm²/s, 1.16 ± 0.17 × 10^-3 mm²/s, 0.98 ± 0.21 × 10^-3 mm²/s, and 1.06 ± 0.15 × 10^-3 mm²/s, 0.88 ± 0.16 × 10^-3 mm²/s, 0.76 ± 0.18 × 10^-3 mm²/s, respectively, and all differences were significant. The D* and f values of the three groups were 32.87 ± 14.70 × 10^-3 mm²/s, 41.68 ± 17.90 × 10^-3 mm²/s, 34.54 ± 18.60 × 10^-3 mm²/s and 0.22 ± 0.07, 0.23 ± 0.08, 0.18 ± 0.07, respectively, with no significant difference. When the cut-off values of ADC and D were 1.25 × 10^-3 mm²/s and 0.97 × 10^-3 mm²/s, respectively, their diagnostic sensitivities and specificities for distinguishing well differentiated HCC from moderately differentiated and poorly differentiated HCC were 73.3%, 85.5%, 86.7%, and 78.2%, and their areas under the ROC curve were 0.821 and 0.841, respectively. ADC and D values can be used preoperatively to predict the degree of histologic differentiation in HCC, and the D value has better diagnostic value.

A study of the correlations between IVIM-DWI parameters and the histologic differentiation of hepatocellular carcinoma

The present study aimed to investigate the value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in the preoperative prediction of the histologic differentiation of hepatocellular carcinoma (HCC). Seventy HCC patients were scanned with a 3.0 T magnetic resonance scanner. The values of apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and the fraction of the fast apparent diffusion coefficient (f) were measured. Analysis of variance was used to compare the differences in parameters between groups with different degrees of histologic differentiation. p < 0.05 was considered statistically significant. Receiver operating characteristic (ROC) curves were used to analyse the efficacy of IVIM-DWI parameters for predicting the histologic differentiation of HCC. The ADC and D values for well, moderately and poorly differentiated HCC were 1.35 ± 0.17 × 10⁻³ mm²/s, 1.16 ± 0.17 × 10⁻³ mm²/s, 0.98 ± 0.21 × 10⁻³ mm²/s, and 1.06 ± 0.15 × 10⁻³ mm²/s, 0.88 ± 0.16 × 10⁻³ mm²/s, 0.76 ± 0.18 × 10⁻³ mm²/s, respectively, and all differences were significant. The D* and f values of the three groups were 32.87 ± 14.70 × 10⁻³ mm²/s, 41.68 ± 17.90 × 10⁻³ mm²/s, 34.54 ± 18.60 × 10⁻³ mm²/s and 0.22 ± 0.07, 0.23 ± 0.08, 0.18 ± 0.07, respectively, with no significant difference. When the cut-off values of ADC and D were 1.25 × 10⁻³ mm²/s and 0.97 × 10⁻³ mm²/s, respectively, their diagnostic sensitivities and specificities for distinguishing well differentiated HCC from moderately differentiated and poorly differentiated HCC were 73.3%, 85.5%, 86.7%, and 78.2%, and their areas under the ROC curve were 0.821 and 0.841, respectively. ADC and D values can be used preoperatively to predict the degree of histologic differentiation in HCC, and the D value has better diagnostic value.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades

PURPOSE

The purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer.

METHODS

Fifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared.

RESULTS

D* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (-) group (D*:36.8 × 10-3 ± 5.3 × 10-3 mm2/s, f:29.5%, D*:29.8 × 10-3 ± 5.6 × 10-3 mm2/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (-) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease.

CONCLUSION

D* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.

Tumor attenuation and quantitative analysis of perfusion parameters derived from tri-phasic CT scans in hepatocellular carcinoma: Relationship with histological grade

The aim of the current study was to explore the value of tumor attenuation and quantitative analysis of perfusion parameters obtained from traditional tri-phasic CT scans in grading hepatocellular carcinoma (HCC).Totally 39 patients (42 lesion samples) with pathologically confirmed HCC who underwent tri-phasic CT scans were enrolled. HCC lesions were divided into non-poorly differentiated HCC (NP-HCC; n = 31) and poorly differentiated HCC (pHCC; n = 11). All lesions were divided into 5 groups according to the attenuation on different CT enhancement phase. The values of tumor attenuation on different scanning phases were measured. The following parameters were calculated: arterial enhancement fraction (AEF), portal venous supply coefficient (PVC), and hepatic arterial supply coefficient (HAC). The relationship of perfusion parameters with the histological grade of HCC was analyzed. Receiver operating characteristic curves were generated.No significant correlation was observed between the perfusion parameters and tumor grading. Only HAC showed a non-significant trend in different grades of HCC (pHCC < NP-HCC; P = .07). The pHCC cases had significantly decreased values of tumor attenuation on the unenhanced phase (TAu), tumor attenuation on the portal phase portal phase (TAp), and equilibrium phase (TAe) (P < .01). The difference of tumor attenuation between the portal phase and the unenhanced phase (TAp-TAu) of the pHCC cases was decreased than that of the NP-HCC cases (P < .01), whereas the difference of attenuation between the equilibrium phase and portal phase (TAe-TAp) was significantly higher in the pHCC cases than that in the NP-HCC cases (P < .01). TAe-TAp had the highest area under the curve. The number of tumor enhancement pattern in Group 5 of HCCs with a diameter of 3 cm or more was significantly more than that of HCCs with a diameter of less than 3 cm or with other different enhancement patterns (P < .01).Histological HCC grading cannot be predicted by the perfusion parameters derived from traditional tri-phasic CT scans, whereas the tumor attenuation on different phases and the tumor attenuation differences among different phases, especially the mean value of TAe-TAp, might be useful for non-invasive prediction on the degree of HCC differentiation.

Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma

BACKGROUND

Pre-operative non-invasive histological evaluation of hepatocellular carcinoma (HCC) remains a challenge. Tumor perfusion is significantly associated with the development and aggressiveness of HCC. The purpose of the study was to evaluate the clinical value of quantitative liver perfusion parameters and corresponding histogram parameters derived from traditional triphasic enhanced computed tomography (CT) scans in predicting histological grade of HCC.

METHODS

Totally, 52 patients with HCC were enrolled in this retrospective study and underwent triple-phase enhanced CT imaging. The blood perfusion parameters were derived from triple-phase CT scans. The relationship of liver perfusion parameters and corresponding histogram parameters with the histological grade of HCC was analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal ability of the parameters to predict the tumor histological grade.

RESULTS

The variance of arterial enhancement fraction (AEF) was significantly higher in HCCs without poorly differentiated components (NP-HCCs) than in HCCs with poorly differentiated components (P-HCCs). The difference in hepatic blood flow (HF) between total tumor and total liver flow (ΔHF = HFtumor - HFliver) and relative flow (rHF = ΔHF/HFliver) were significantly higher in NP-HCCs than in P-HCCs. The difference in portal vein blood supply perfusion (PVP) between tumor and liver tissue (ΔPVP) and the ΔPVP/liver PVP ratio (rPVP) were significantly higher in patients with NP-HCCs than in patients with P-HCCs. The area under ROC (AUC) of ΔPVP and rPVP were both 0.697 with a high sensitivity of 84.2% and specificity of only 56.2%. The ΔHF and rHF had a higher specificity of 87.5% with an AUC of 0.681 and 0.673, respectively. The combination of rHF and rPVP showed the highest AUC of 0.732 with a sensitivity of 57.9% and specificity of 93.8%. The combined parameter of ΔHF and rPVP, rHF and rPVP had the highest positive predictive value of 0.903, and that of rPVP and ΔPVP had the highest negative predictive value of 0.781.

CONCLUSION

Liver perfusion parameters and corresponding histogram parameters (including ΔHF, rHF, ΔPVP, rPVP, and AEFvariance) in patients with HCC derived from traditional triphasic CT scans may be helpful to non-invasively and pre-operatively predict the degree of the differentiation of HCC.

The Role of 18F-FDG PET/MRI in the Assessment of Primary Intrahepatic Neoplasms

RATIONALE AND OBJECTIVES

To evaluate the results of 18F-FDG PET/MRI in relation to the histopathologic subtypes and histologic grades of mass-forming primary intrahepatic neoplasms.

MATERIALS AND METHODS

We retrospectively evaluated 18F-FDG positron emission tomography/magnetic resonance imaging (PET/MRI) results for 39 patients with histopathologically confirmed primary hepatic neoplasms, 15 with mass-forming intrahepatic cholangiocarcinoma (ICC) and 24 with hepatocellular carcinoma (HCC). The apparent diffusion coefficient (ADC) and standardized uptake value (SUV) were analyzed in relation to the histopathologic diagnosis and histologic grade, including calculating the sensitivity and specificity of the imaging findings.

RESULTS

The median SUV of ICC (6.0 [interquartile range, 5-10]) was significantly higher than that of HCC (4.0 [2.62-6.50]) (p = 0.002). An area under the curve (AUC) of 0.79 (95% confidence interval 0.649-0.932) had a sensitivity of 86.7% and a specificity of 67% at the best cut-off SUV of 4.41 to differentiate between ICC and HCC. ADC values did not differ significantly between HCCs and ICCs (p = 0.283). Both SUV and ADC values differed significantly between low-grade (well- and moderately differentiated) and high-grade (poorly differentiated) HCCs. Combining ADC and SUV further improved differentiation of low- from high-grade HCCs to a significant level (0.929). The SUV did not differ significantly between ICC histologic grades (p = 0.280), while the ADC differed significantly only between well and poorly differentiated ICCs (p = 0.004).

CONCLUSION

Assessing primary hepatic neoplasms with 18F-Fluorodeoxyglucose PET/MRI may help to predict tumor grade and differentiate between types of intrahepatic neoplasms.

Diffusion-Weighted Imaging Reflects Tumor Grading and Microvascular Invasion in Hepatocellular Carcinoma

BACKGROUND

To date, there are inconsistent data about relationships between diffusion-weighted imaging (DWI) and tumor grading/microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Our purpose was to systematize the reported results regarding the role of DWI in prediction of tumor grading/MVI in HCC.

METHOD

MEDLINE library, Scopus, and Embase data bases were screened up to December 2019. Overall, 29 studies with 2,715 tumors were included into the analysis. There were 20 studies regarding DWI and tumor grading, 8 studies about DWI and MVI, and 1 study investigated DWI, tumor grading, and MVI in HCC.

RESULTS

In 21 studies (1,799 tumors), mean apparent diffusion coefficient (ADC) values (ADC_mean) were used for distinguishing HCCs. ADC_mean of G1-3 lesions overlapped significantly. In 4 studies (461 lesions), minimum ADC (ADC_min) was used. ADC_min values in G1/2 lesions were over 0.80 × 10^-3 mm²/s and in G3 tumors below 0.80 × 10^-3 mm²/s. In 4 studies (241 tumors), true diffusion (D) was reported. A significant overlapping of D values between G1, G2, and G3 groups was found. ADC_mean and MVI were analyzed in 9 studies (1,059 HCCs). ADC_mean values of MIV+/MVI- lesions overlapped significantly. ADC_min was used in 4 studies (672 lesions). ADC_min values of MVI+ tumors were in the area under 1.00 × 10^-3 mm²/s. In 3 studies (227 tumors), D was used. Also, D values of MVI+ lesions were predominantly in the area under 1.00 × 10^-3 mm²/s.

CONCLUSION

ADC_min reflects tumor grading, and ADC_min and D predict MVI in HCC. Therefore, these DWI parameters should be estimated for every HCC lesion for pretreatment tumor stratification. ADC_mean cannot predict tumor grading/MVI in HCC.

Texture Analysis of Hepatocellular Carcinoma on Magnetic Resonance Imaging: Assessment for Performance in Predicting Histopathologic Grade

OBJECTIVE

The aim of the study was to evaluate the performance of texture analysis for discriminating the histopathological grade of hepatocellular carcinoma (HCC) on magnetic resonance imaging.

METHODS

Preoperative magnetic resonance imaging data from 101 patients with HCC, including T2-weighted imaging, arterial phase, and apparent diffusion coefficient mapping, were analyzed using texture analysis software (TexRAD). Differences among the histological groups were analyzed using the Mann-Whitney U test. The performance of texture features was evaluated using receiver operating characteristic analysis.

RESULTS

Entropy was the most significantly relevant texture feature for distinguishing each histological grade group of HCC (P < 0.05). In ROC analysis, entropy with spatial scale filter 3 (area under curve the receiver operating characteristic curve [AUC], 0.778), mean with coarse filter (spatial scale filter 5; AUC, 0.670), and skewness without filtration (AUC, 0.760) had the highest AUC value on T2-weighted imaging, arterial phase, and apparent diffusion coefficient maps, respectively.

CONCLUSIONS

Magnetic resonance imaging texture analysis demonstrated potential for predicting the histopathological grade of HCCs.

Pretreatment Apparent Diffusion Coefficient as a Predictor of Response to Transcatheter Arterial Chemoembolization Immediately Combined with Radiofrequency Ablation for Treatment of Solitary Large Hepatocellular Carcinoma

Purpose

To evaluate whether the pretreatment apparent diffusion coefficient (ADC) measured with diffusion weighted imaging (DWI) of tumor can be used as an imaging biomarker for predicting prognosis in solitary large hepatocellular carcinomas (HCCs) treated with transcatheter arterial chemoembolization (TACE) immediately combined with radiofrequency ablation (RFA).

Patients and Methods

In this single institution retrospective study, 40 solitary large HCCs that underwent treatment with TACE immediately combined with RFA were analyzed. All patients underwent abdominal dynamic contrast-enhanced magnetic resonance imaging within one month before treatment with DWI, and ADC values in the lesions were measured by two independent radiologists. Associations among patients’ preoperative ADC values and objective response (OR), progression-free survival (PFS) and overall survival (OS) were examined. Survival curves were drawn with the Kaplan–Meier method, and differences were determined with the Log rank test. The Cox proportional-hazards model was used for univariate and multivariate analyses of PFS and OS.

Results

Forty solitary large HCCs (mean 9.54 cm, range 5.04–16.06 cm) were successfully treated with TACE in immediate combination with RFA (OR 75%). The ADC values were significantly higher in the response group than the non-response group (1.51±0.32×10⁻³ mm²/s vs 1.09±0.17×10⁻³ mm²/s; P<0.001). As predicted on the basis of the ADC values, the optimal cutoff value for the efficacy of TACE combined with RFA was 1.32×10⁻³ mm²/s, with a predictive sensitivity of 0.63 and a specificity of 1.00. Patients with high ADC had longer PFS than those with low ADC (14.9 months vs 5.3 months; P<0.001) and had significantly longer survival rates (22.6 months vs 12.1 months; P=0.004).

Conclusion

Preoperative ADC values <1.32×10⁻³ mm²/s are an independent predictor of poorer prognosis in patients with solitary large HCCs who have undergone TACE immediately combined with RFA.

Quantitative analysis of multiphase magnetic resonance images may assist prediction of histopathological grade of small hepatocellular carcinoma

Background

The aim of the study was to investigate whether preoperative quantitative analysis of multiphase magnetic resonance images may assist in predicting the pathological grade of small hepatocellular carcinoma (HCC).

Methods

A total of 49 patients with small HCCs (≤3 cm) underwent multiphase magnetic resonance imaging (MRI) and were retrospectively reviewed. Routine unenhanced and post gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were preoperatively performed. Signal intensity (SI) was measured within the designated region of interest (ROI) including those of the lesion and paraspinous muscles. The lesion-to-paraspinous muscle relative contrast ratio (RCR) on T2-weighted (T2W) imaging, diffusion-weighted (DW) imaging, and dynamic phase Gd-BOPTA-enhanced T1W (T1-weighted) imaging were calculated, and statistical analysis was performed to determine the predictive power for the histological grade.

Results

In all, 49 cases were included comprising 3 well-differentiated (WD) HCCs, 36 moderately differentiated (MD) HCCs, and 10 poorly differentiated (PD) HCCs. There was a negative correlation between the RCR and pathological grade of small HCC in the arterial phase [correlation coefficient (ρ)=-0.305, P<0.05]. However, there was no correlation between RCR in other phases and pathological grade (P>0.05 for all). There was also no correlation between tumor margin, tumor location, cystic/necrotic change, intratumoral fat, enhancement pattern, tumor capsule, tumor boundary or tumor size, and any of the differentiation categories (P>0.05 for all).

Conclusions

The lesion-to-paraspinous muscle RCR on arterial phase Gd-BOPTA-enhanced T1W imaging may be useful for the prediction of the histological characteristics of small HCC.

Value of Intravoxel Incoherent Motion for Hepatocellular Carcinoma Grading

BACKGROUND

To evaluate the diagnostic accuracy of intravoxel incoherent motion (IVIM) parameters in estimation of hepatocellular carcinoma (HCC) grading.

MATERIALS AND METHODS

Twenty-nine patients with histopathologically diagnosed as 42 HCC at explant were included in this retrospective study. All patients were examined by 1.5T magnetic resonance imaging with the use of 4-channel phased array body coil. In addition to routine pre- and postcontrast sequences, IVIM (16 different b factors varying from 0 to 1300 s/mm²) and conventional diffusion-weighted imaging (3 different b factors of 50, 400, 800 s/mm²) were obtained with single-shot echo planar spin echo sequence. Apparent diffusion coefficient (ADC) and IVIM parameters including mean D (true diffusion coefficient), D* (pseudo-diffusion coefficient associated with blood flow), and f (perfusion fraction) values were calculated. Histopathologically, HCC was classified as low (grade 1, 2) and high (grade 3, 4) grade in accordance with the Edmondson-Steiner score. Quantitatively, ADC, D, D*, and f values were compared between the low- and high-grade groups by Student t test. The relationship between the parameters and histologic grade was analyzed using the Spearman's correlation test. To evaluate the diagnostic performance of the parameters, receiver operating characteristic analysis was performed.

RESULTS

High-grade HCCs had significantly lower ADC and D values than low grade groups (P = .005 and P = .026, retrospectively); ADC and D values were inversely correlated with tumor grade (r = -0.519, P = .011, r = -0.510, P = .026, respectively). High-grade HCCs had significantly higher f values when compared with the low-grade group (P = .005). The f values were positively correlated with tumor grade (r = 0.548, P = .007). The best discriminative parameter was f value. Cut-off value of 32% of f values showed sensitivity of 75.6% and a specificity of 73.5%.

CONCLUSION

ADC values and IVIM parameters such as f values appear to reflect the grade of HCCs.

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

Diagnostic accuracy of quantitative diffusion parameters in the pathological grading of hepatocellular carcinoma: A meta-analysis

BACKGROUND

Accurate preoperative assessment of the pathological grade of hepatocellular carcinoma (HCC) could greatly benefit prognostic predictions.

PURPOSE

To assess and compare the diagnostic accuracy of the apparent diffusion coefficient (ADC) and tissue diffusivity (D) for the noninvasive pathological grading of HCC.

STUDY TYPE

Meta-analysis.

DATA SOURCES

PubMed/Medline, EMBASE, the Web of Science, and the Cochrane Library were searched to find related original articles published up to May 30, 2019.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted imaging (DWI) and/or intravoxel incoherent motion (IVIM) were performed with a 1.5T or 3.0T scanner.

ASSESSMENT

The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodologic quality.

STATISTICAL TESTS

The bivariate random-effects model was used to obtain the pooled sensitivity and specificity, and the area under summary receiver operating characteristic curve (AUROC) was obtained. Subgroup analyses were performed.

RESULTS

A total of 16 original articles (1428 HCCs) were included. Most studies had a low to unclear risk of bias and minimal concerns regarding applicability. For the discrimination of well-differentiated HCCs, the pooled sensitivity and specificity of the ADC value were 85% and 92%, respectively. For the discrimination of poorly differentiated HCCs, the pooled sensitivity and specificity of the ADC value and D were 84% and 80%, and 92% and 77%, respectively. The summary AUROC of D (0.94) was significantly higher than that of ADC (0.89) (z = -2.718, P = 0.007). The subgroup analyses identified three covariates including size, number of included lesions in the studies, and blindness to the reference standard as possible sources of heterogeneity.

DATA CONCLUSION

This meta-analysis showed that the ADC and D values had a high to excellent accuracy for the noninvasive pathological grading of HCCs and that the D value was superior to the ADC value for discriminating poorly differentiated HCCs.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1581-1593.

Amide proton transfer-weighted MRI for predicting histological grade of hepatocellular carcinoma: comparison with diffusion-weighted imaging

Background

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver, preoperative grading of HCC is of great clinical significance. Amide proton transfer-weighted (APTw) imaging, as a novel contrast mechanism in the field of molecular imaging, provided new diagnostic ideas for the grading of HCC.

Methods

Between May 2017 and April 2018, 32 consecutive patients with pathologically confirmed HCC were enrolled, including 19 high-grade HCCs and 13 low-grade HCCs. DWI and APTw scanning was performed on a 3T MRI scanner. Two observers drew regions of interest independently by referring to the axial T₂-weighted imaging, and APTw and apparent diffusion coefficient (ADC) values were obtained. Inter- and intra-observer agreements were assessed with the intraclass correlation coefficients (ICCs). The independent sample t test was used to compare the APTw and ADC values between the high- and low-grade HCC tumor parenchyma. The receiver operating characteristic curve was used to analyze the diagnostic efficacy of high- from low-grade HCC tumors. Spearman correlation analysis was used to assess the relationship between APTw and ADC values and HCC histological grades.

Results

There were significant differences between the APTw or ADC values for the high- and low-grade HCCs (P=0.034 and 0.010). Both APTw and DWI had good diagnostic performance in differentiating the high- from the low-grade HCCs, with areas under the curves of 0.814 and 0.745, respectively. Moderate correlations existed between APTw values and histological grades (r=0.534; P=0.002), as well as ADC values and histological grades (r=-0.417; P=0.018).

Conclusions

The APTw imaging is a useful imaging biomarker that complements DWI for the more accurate and comprehensive HCC characterization.

Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma

PURPOSE

The study evaluated the relationship between the histological grade of hepatocellular carcinoma (HCC) and the histogram-derived parameters of apparent diffusion coefficient (ADC) obtained from the whole-lesion assessment of diffusion-weighted magnetic resonance (MR) imaging in the liver.

METHODS

A total of 51 patients were included. The parameters were correlated with the Edmondson-Steiner grades by using the Spearman correlation coefficient (ρ). The differences of ADC parameters between different tumor histological grades were compared using the Mann-Whitney U test. The extent to which each parameter aided in differentiating tumors with poor performance (III, IV) and fair performance (I, II) was assessed by using the area under the receiver operating characteristic curve (Az).

RESULTS

The 25th percentile ADC exhibits the most negative correlation with histological grade (ρ = - 0.397), followed by the 30th percentile ADC (ρ = - 0.395), the minimum ADC value (ρ = - 0.390) and the 20th percentile ADC (ρ = - 0.385), whereas the minimum ADC value yielded the highest Az (0.763) in the discrimination of tumor foci with poor differentiation from fairly differentiated HCCs. The minimum ADC of 4.15 × 10^-3 mm²/s or lower was considered to indicate poorly differentiated performance, and the corresponding sensitivity and specificity were 66.7 and 90.9%, respectively.

CONCLUSION

The 25th percentile ADC showed a stronger correlation with the histological grade of HCC than other ADC parameters, and the minimum ADC value might be an optimal metric for determining poor and fair differentiations of HCC in DWI.

Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks

PURPOSE

To effectively grade hepatocellular carcinoma (HCC) based on deep features derived from diffusion weighted images (DWI) with multiple b-values using convolutional neural networks (CNN).

MATERIALS AND METHODS

Ninety-eight subjects with 100 pathologically confirmed HCC lesions from July 2012 to October 2018 were included in this retrospective study, including 47 low-grade and 53 high-grade HCCs. DWI was performed for each subject with a 3.0T MR scanner in a breath-hold routine with three b-values (0,100, and 600 s/mm² ). First, logarithmic transformation was performed on original DWI images to generate log maps (logb0, logb100, and logb600). Then, a resampling method was performed to extract multiple 2D axial planes of HCCs from the log map to increase the dataset for training. Subsequently, 2D CNN was used to extract deep features of the log map for HCCs. Finally, fusion of deep features derived from three b-value log maps was conducted for HCC malignancy classification. Specifically, a deeply supervised loss function was devised to further improve the performance of lesion characterization. The data set was split into two parts: the training and validation set (60 HCCs) and the fixed test set (40 HCCs). Four-fold cross validation with 10 repetitions was performed to assess the performance of deep features extracted from single b-value images for HCC grading using the training and validation set. Receiver operating characteristic curve (ROC) and area under the curve (AUC) values were used to assess the characterization performance of the proposed deep feature fusion method to differentiate low-grade and high-grade in the fixed test set.

RESULTS

The proposed fusion of deep features derived from logb0, logb100, and logb600 with deeply supervised loss function generated the highest accuracy for HCC grading (80%), thus outperforming the method of deep feature derived from the ADC map directly (72.5%), the original b0 (65%), b100 (68%), and b600 (70%) images. Furthermore, AUC values of the deep features of the ADC map, the deep feature fusion with concatenation, and the proposed deep feature fusion with deeply supervised loss function were 0.73, 0.78, and 0.83, respectively.

CONCLUSION

The proposed fusion of deep features derived from the logarithm of the three b-value images yields high performance for HCC grading, thus providing a promising approach for the assessment of DWI in lesion characterization.

Small hepatocellular carcinoma: using MRI to predict histological grade and Ki-67 expression

AIMS

To investigate the predictive indicators of small aggressive hepatocellular carcinomas by examining the association between preoperative magnetic resonance imaging (MRI) parameters and Ki-67 expression and histological grade.

MATERIALS AND METHODS

Sixty patients with small hepatocellular carcinomas (tumour diameter: ≤3 cm, tumour numbers: ≤2) who underwent curative resection or biopsy after contrast-enhanced and diffusion-weighted MRI were evaluated retrospectively. Signal intensity (SI) of the whole lesion and erector spinae muscle was measured quantitatively. Tumour-to-muscle SI ratio was calculated. The association between these MRI parameters and histological grade and Ki-67 level was then investigated.

RESULTS

There was a significant correlation between tumour-to-muscle SI ratio and histological grade in tissues captured during the non-enhanced T1-weighted (p=0.001), arterial phase (p=0.001), and portal venous phase (p=0.036) of dynamic contrast-enhanced MRI and apparent diffusion coefficient (p=0.027). Arterial inhomogeneous enhancement was also correlated with high-Ki-67 expression (p=0.032).

CONCLUSIONS

Preoperative MRI may serve as a non-invasive tool for prediction of small, aggressive hepatocellular carcinomas, which may otherwise be treated conservatively.

Questionable correlation of the apparent diffusion coefficient with the histological grade and microvascular invasion in small hepatocellular carcinoma

AIM

To evaluate the correlation between the apparent diffusion coefficient (ADC) and various histopathological parameters in small hepatocellular carcinomas (HCCs).

MATERIALS AND METHODS

In 143 surgically resected small HCCs, the mean and minimum ADC values, tumour-to-liver ADC ratio, and normalised ADC (ADC of the HCC/ADC of the spleen) were correlated to the tumour grade, microvascular invasion (MVI), cellularity, fatty change, degree of fibrosis, and lymphocytic infiltration using linear regression analysis, the Wilcoxon rank sum test, or Spearman's rank correlation.

RESULTS

No significant correlation was found between the ADC parameters and tumour grade. In the univariate analysis, the ADC ratio of the tumour was significantly correlated with MVI as well as the degree of fibrosis and lymphocyte infiltration of the HCC (p=0.017, 0.042, and 0.002, respectively). The ADC of the tumour was significantly correlated with the degree of lymphocyte infiltration of the HCC (p=0.049). In the multivariate analysis, the ADC ratio of the tumour was an independent parameter for MVI and the degree of lymphocyte infiltration of the HCC (p=0.034 and <0.001, respectively), and the ADC of the tumour was an independent parameter for the degree of lymphocyte infiltration of the HCC (p=0.009). There was no significant correlation between the other ADCs and pathological tumour parameters.

CONCLUSION

The tumour grade of small HCCs was not correlated with ADC parameters. The tumour-to-liver ADC ratio was a significant independent parameter for the degree of lymphocyte infiltration and MVI of small HCCs.

Efficacy of apparent diffusion coefficient in predicting aggressive histological features of papillary thyroid carcinoma

PURPOSE

We aimed to evaluate preoperative diffusion-weighted magnetic resonance imaging (DWI) for predicting aggressive histological features in papillary thyroid cancer (PTC).

METHODS

This prospective study included 141 PTC patients, who underwent DWI prior to thyroidectomy; 88 patients with 88 PTC lesions were finally analyzed. Multiple comparisons of mean and minimum apparent diffusion coefficient (ADC) values (ADCmean and ADCmin) and ADC of the solid component (ADCsolid) between the lowly aggressive PTC, highly aggressive PTC without hobnail, and hobnail variant PTC groups were performed by one-way ANOVA or the Welch test. The nonparametric Kruskal-Wallis H-test was used to assess lesion size differences. Receiver-operating characteristic (ROC) curve analysis was also performed.

RESULTS

ADC values in the lowly aggressive PTC group were found to be significantly higher than those in the highly aggressive PTC without hobnail group (ADCmean: 1.35±0.20×10-3 mm2/s vs. 1.16±0.17×10-3 mm2/s, P = 0.003; ADCmin: 1.10±0.17×10-3 mm2/s vs. 0.88±0.16×10-3 mm2/s, P < 0.001; ADCsolid: 1.26±0.23×10-3 mm2/s vs. 1.04±0.17×10-3 mm2/s, P < 0.001). No significant differences for the ADCmean, ADCmin, and ADCsolid were observed between the lowly aggressive and hobnail variant PTC groups (all P > 0.05). Lesion sizes in the hobnail variant PTC group was significantly elevated compared with the lowly aggressive PTC group (2.19±1.21 cm vs. 0.93±0.37 cm, P < 0.001). Areas under the curves (AUCs) for ADCmean, ADCmin, and ADCsolid between the lowly aggressive PTC and highly aggressive PTC group without hobnail were 0.758, 0.851, and 0.787, respectively. The AUC for size between the lowly aggressive and hobnail variant PTC group was 0.896.

CONCLUSION

ADCmin from DWI could potentially provide quantitative information to differentiate lowly aggressive PTC from highly aggressive PTC lesions without hobnail variants.

Current status of imaging biomarkers predicting the biological nature of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is heterogeneous in terms of its biological nature. Various factors related to its biological nature, including size, multifocality, macroscopic morphology, grade of differentiation, macro/microvascular invasion, bile duct invasion, intra-tumoral fat and molecular factors, and their value as prognostic imaging biomarkers have been reported. And recently, genome-based molecular HCC classification correlated with clinical outcome has been elucidated. The imaging biomarkers suggesting a less aggressive nature of HCC are smaller size, solitary tumor, smooth margin suggesting small nodular type with indistinct margin and simple nodular type with distinct margin, capsule, imaging biomarkers predicting early or well-differentiated grade, intra-tumoral fat detection, and low fluorodeoxyglucose (FDG) accumulation. The imaging biomarkers suggesting an aggressive HCC nature are larger size, multifocality, non-smooth margin suggesting simple nodular type with extranodular growth, confluent multinodular, and infiltrative type, imaging biomarkers predicting poor differentiation, macrovascular tumor thrombus, predicting microvascular invasion imaging biomarkers, bile duct dilatation or tumor thrombus, and high FDG accumulation. In the genome-based molecular classification, CTNNB-1 mutated HCC shows a less aggressive nature, while CK19/EpCAM positive HCC and macrotrabecular massive HCC show an aggressive one. Better understanding of these imaging biomarkers can contribute to devising more appropriate treatment plans for HCC.

Intravoxel incoherent motion diffusion-weighted imaging for assessment of histologic grade of hepatocellular carcinoma: comparison of three methods for positioning region of interest

OBJECTIVES

To prospectively compare the diagnostic performances of three methods of region of interest (ROI) placement for the measurements of intravoxel incoherent motion (IVIM) diffusion-weighted MR imaging in differentiating the histologic grade of hepatocellular carcinoma (HCC).

METHODS

Eighty-seven patients with 91 newly diagnosed HCCs were studied using IVIM imaging. Two attending radiologists separately identified the selection of tumour tissue for ROI positioning. Three different ROI positioning methods, namely the whole tumour volume (WTV) method, three-ROI method and one-section method, were used for the measurement. Kruskal-Wallis rank test or one-way ANOVA was used to compare the difference in IVIM parameters and ADC across the three different ROI positioning methods. Spearman correlation analysis was used to determine the correlation between each parameter and Edmondson-Steiner (E-S) grade. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance.

RESULTS

For the ADC and ADC_slow, the mean value measured by using the WTV method was significant higher than the one-section and three-ROI methods (all p < 0.01). For the ADC_slow, the highest area under curve (AUC) with a value of 0.969 was obtained by using the WTV method, followed by the one-section method (AUC = 0.938) and three-ROI method (AUC = 0.873). Additionally, for the ADC, AUC values were 0.861 for WTV method, 0.840 for one-section method and 0.806 for three-ROI method.

CONCLUSIONS

Different ROI positioning methods used significantly affect the IVIM parameters and ADC measurements. Measurements of ADC_slow value derived from WTV method entailed the highest diagnostic performance in grading HCC.

KEY POINTS

• Diffusion MRI is useful for non-invasively differentiating the histologic grade of hepatocellular carcinoma. • Different ROI positioning methods used significantly affect the IVIM parameters and ADC measurements. • IVIM model is advantageous over mono-exponential model for assessing the histologic grade of hepatocellular carcinoma.

Amide proton transfer imaging to predict tumor response to neoadjuvant chemotherapy in locally advanced rectal cancer

BACKGROUND AND AIM

The amount of proteins and peptides can be estimated with amide proton transfer (APT) imaging. Previous studies demonstrated the usefulness of APT imaging to predict tumor malignancy. We determined whether APT imaging can predict the tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced rectal cancer (LARC).

METHODS

Seventeen patients with LARC who underwent a pretherapeutic magnetic resonance examination including APT imaging and NAC (at least two courses) were enrolled. The APT-weighted imaging (WI) signal intensity (SI) (%) was defined as magnetization transfer ratio asymmetry (MTR_asym ) at the offset of 3.5 ppm. Each tumor was histologically evaluated for the degree of degeneration and necrosis and then classified as one of five histological Grades (0, none; 1a, less than 1/3; 1b, 1/3 to 2/3; 2, more than 2/3; 3, all). We compared the mean APTWI SIs of the tumors between the Grade 0/1a/1b (low-response group) and Grade 2/3 (high-response group) by Student's t-test. We used receiver operating characteristics curves to determine the diagnostic performance of the APTWI SI for predicting the tumor response.

RESULTS

The mean APTWI SI of the low-response group (n = 12; 3.05 ± 1.61%) was significantly higher than that of the high-response group (n = 5; 1.14 ± 1.13%) (P = 0.029). The area under the curve for predicting the tumor response using the APTWI SI was 0.87. When ≥2.75% was used as an indicator of low-response status, 75% sensitivity and 100% specificity of the APTWI SI were obtained.

CONCLUSION

Pretherapeutic APT imaging can predict the tumor response to NAC in patients with LARC.

Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature

PURPOSE

This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade.

METHODS

Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The predicted values of pathological HCC grades using radiomics signatures, clinical factors (including age, sex, tumour size, alpha fetoprotein (AFP) level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) and the combined models were assessed.

RESULTS

Radiomics signatures could successfully categorise high-grade and low-grade HCC cases (p < 0.05) in both the training and test datasets. Regarding the performances of clinical factors, radiomics signatures and the combined clinical and radiomics signature (from the combined T1WI and T2WI images) models for HCC grading prediction, the areas under the curve (AUCs) were 0.600, 0.742 and 0.800 in the test datasets, respectively. Both the AFP level and radiomics signature were independent predictors of HCC grade (p < 0.05).

CONCLUSIONS

Radiomics signatures may be important for discriminating high-grade and low-grade HCC cases. The combination of the radiomics signatures with clinical factors may be helpful for the preoperative prediction of HCC grade.

KEY POINTS

• The radiomics signature based on non-contrast-enhanced MR images was significantly associated with the pathological grade of HCC. • The radiomics signatures based on T1WI or T2WI images performed similarly at predicting the pathological grade of HCC. • Combining the radiomics signature and clinical factors (including age, sex, tumour size, AFP level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) may be helpful for the preoperative prediction of HCC grade.

ADC similarity predicts microvascular invasion of bifocal hepatocellular carcinoma

PURPOSE

This study aimed to investigate whether ADC similarity can predict microvascular invasion (MVI) in patients with bifocal hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Between January 2015 and September 2015, 51 patients with two HCC lesions were included. All patients underwent conventional magnetic resonance imaging including diffusion-weighted imaging (DWI) before the HCC lesions were surgically resected; the tumor specimens were examined histopathologically. Similarity between two HCC lesions regarding DWI signal intensity (SI) and ADC value was calculated as the difference between the two lesions: Value Similarity = [1-(|value_{large lesion}-value_{small lesion}|)/(value_{large lesion} + value_{small lesion})] × 100%. Univariate and multivariate logistic regression analyses were performed to assess the presence of MVI.

RESULTS

Risk factors significantly related to MVI of bifocal HCC in univariate analysis were cirrhosis (P = 0.010), histological grade (P = 0.040), DWI SI similarity (P = 0.027) and ADC similarity (P = 0.003). In multivariate analysis, cirrhosis (odds ratio 0.068, P = 0.022) and ADC similarity (odds ratio 1.204, P = 0.008) were independent risk factors for MVI of bifocal HCC.

CONCLUSION

In patients with two HCC lesions, highly similar ADC values for the two HCC lesions may be a preoperative predictor of MVI.

The current role and future prospectives of functional parameters by diffusion weighted imaging in the assessment of histologic grade of HCC

Hepatocellular carcinoma (HCC) is one of the most common human solid malignancies worldwide. Although the MRI is the technique that is best adapted to characterize HCC, there is not an agreement regarding the study protocol and even what the role of Diffusion-weighted imaging (DWI). The possibility that imaging study can correlate to histologic grade to selecting the therapeutic strategy would be valuable in helping to direct the proper management of HCC. Apparent Diffusion Coefficient (ADC) and IVIM-derived perfusion fraction (fp) and tissue diffusivity (Dt) values of HCC showed significantly better diagnostic performance in differentiating high-grade HCC from low-grade HCC, and significant correlation was observed between ADC, fp, Dt and histological grade.

CT-MRI LI-RADS v2017: A Comprehensive Guide for Beginners

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the second leading cause of cancer-related deceases worldwide. Early diagnosis is essential for correct management and improvement of prognosis. Proposed for the first time in 2011 and updated for the last time in 2017, the Liver Imaging-Reporting and Data System (LI-RADS) is a comprehensive system for standardized interpretation and reporting of computed tomography (CT) and magnetic resonance imaging (MRI) liver examinations, endorsed by the American College of Radiology to achieve congruence with HCC diagnostic criteria in at-risk populations. Understanding its algorithm is fundamental to correctly apply LI-RADS in clinical practice. In this pictorial review, we provide a guide for beginners, explaining LI-RADS indications, describing major and ancillary features and eventually elucidating the diagnostic algorithm with the use of some clinical examples.

Differentiating malignant from benign hyperintense nodules on unenhanced T1-weighted images in patients with chronic liver disease: using gadoxetic acid-enhanced and diffusion-weighted MR imaging

PURPOSE

To evaluate value of gadoxetic acid-enhanced and diffusion-weighted (DW) MRI for distinguishing malignant from benign hyperintense nodules on unenhanced T1-weighted images (T1WIs) in patients with chronic liver disease.

MATERIALS AND METHODS

Forty-two patients with 37 malignant and 41 benign hyperintense nodules on unenhanced T1WIs who underwent gadoxetic acid-enhanced and DW MRI, followed by histopathological examination, were included. Qualitative and quantitative analyses were conducted. Significant findings on univariate and multivariate analyses were identified and their diagnostic performances were analyzed for predicting hyperintense hepatocellular carcinomas (HCCs).

RESULTS

In univariate analysis, hyperintensity on T2WI, arterial enhancement, washout, hypointensity on hepatobiliary phase, and diffusion restriction were more frequently observed (P < 0.05) in hyperintense HCCs. Tumor-to-liver SI ratio on hepatobiliary phase and minimum apparent diffusion coefficient (ADC_min) were significantly lower in hyperintense HCCs (P < 0.05). In multivariate analysis, hyperintensity on T2WI (OR, 13.58; P = 0.02), arterial enhancement (OR, 8.21; P = 0.002), and ADC_min ≤ 0.83 × 10^-3 mm²/s (OR, 6.88; P = 0.008) were independently significant factors for predicting hyperintense HCCs. When two of three criteria were combined, 75.7% (28/37) of hyperintense HCCs were identified with a specificity of 92.7%, and when all three criteria were satisfied, the specificity was 97.6%.

CONCLUSION

Gadoxetic acid-enhanced and DW MRI may be helpful for differentiating malignant from benign hyperintense nodules on unenhanced T1WI.

Hepatocellular Carcinoma: Retrospective Evaluation of the Correlation Between Gadobenate Dimeglumine-Enhanced Magnetic Resonance Imaging and Pathologic Grade

OBJECTIVE

The aim of this study was to evaluate the usefulness of gadobenate dimeglumine-enhanced magnetic resonance imaging in characterizing the grade of hepatocellular carcinoma (HCC) using the signal intensity (SI) of the erector spinae as internal reference.

MATERIALS AND METHODS

Clinical data of 40 patients (a total of 44 lesions) confirmed by pathology for HCC were retrospectively reviewed. Gadobenate dimeglumine-enhanced magnetic resonance imaging was performed in all patients, and SI of lesions (SIles), liver parenchyma around the lesions (SIhep), erector spinae (SImus) and standard deviation of SI of the surrounding noise (SDnoi) on nonenhanced T2WI, nonenhanced T1WI, and contrast-enhanced T1WI (in both arterial and hepatobiliary phase [AP and HBP]) were measured, respectively. Contrast-to-noise ratio (CNR) were separately defined as CNR1 ([SIles - SIhep]/SDnoi) and CNR2 ([SIles - SImus]/SDnoi). Statistical analyses were performed using one-way analysis of variance, least significant difference test, logistic regression analysis, Spearman rank correlation, and receiver operating characteristic curves analysis.

RESULTS

Forty-four HCCs, including 3 well-differentiated HCCs, 26 moderately differentiated HCCs, and 15 poorly differentiated (PD) HCCs, were confirmed. On logistic regression analysis, only CNR2 in the HBP was predictor of PD HCCs (P = 0.015, odds ratio = 1.040). The size of lesions, CNR1 in the AP, CNR2 in the AP, and CNR2 in the HBP, showed significant correlations with the degree of differentiation (correlation coefficients = -0.371, 0.435, 0.503, and 0.512, respectively; P = 0.013, 0.003, 0.001, and 0.000, respectively). Contrast-to-noise ratio 2 in the HBP with the cutoff of less than 4.56 could distinguish moderately differentiated HCCs from PD HCC with the sensitivity and specificity of 84.6% and 60.0%, respectively.

CONCLUSIONS

Relatively low arterial enhancement and low CNR2 value in the HBP are predictive for poor histological grade of HCCs.

Apparent diffusion coefficient of hepatocellular carcinoma on diffusion-weighted imaging: Histopathologic tumor grade versus arterial vascularity during dynamic magnetic resonance imaging

Objectives

Apparent diffusion coefficient (ADC) has been suggested to reflect the tumor grades of hepatocellular carcinomas (HCCs); i.e., it can be used as a biomarker to predict the patients’ prognosis. To verify its feasibility as a biomarker, the present study sought to determine how the ADC values of HCC are affected by a tumor’s histopathologic grade and arterial vascularity.

Materials and methods

From 131 consecutive patients, 141 surgically resected HCCs (16 well-differentiated [wd-HCCs], 83 moderately-differentiated [md-HCCs], and 42 poorly-differentiated HCCs [pd-HCCs]) were subjected to a comparison of the tumors’ arterial vascularity (non-, slightly-, or markedly-hypervascular) determined on dynamic magnetic resonance imaging (MRI) and the ADC was measured retrospectively.

Results

The pd-HCCs (1.05±0.16 × 10⁻³ mm²/s) had a significantly lower ADC than md-HCCs (1.16±0.21 × 10⁻³ mm²/s; p = 0.010), but there was no significant difference compared to wd-HCCs (1.11±0.18 × 10⁻³ mm²/s; p = 0.968). The mean ADC was significantly higher in markedly hypervascular lesions (1.20±0.20 × 10⁻³ mm²/s) than in nonhypervascular lesions (0.95±0.14 × 10⁻³mm²/s; p<0.001) or slightly hypervascular lesions (1.04±0.15 × 10⁻³mm²/s; p<0.001). The ADC values and arterial vascularity were significantly correlated in wd-HCCs (p = 0.005) and md-HCCs (p<0.001). The mean ADC of pd-HCCs was significantly lower than those of other lesions, even in the markedly hypervascular lesion subgroup (p = 0.020).

Conclusion

Although pd-HCC constantly shows low ADCs regardless of arterial vascularities, ADCs cannot stably stratify histopathologic tumor grades due to the variable features of wd-HCCs; and the ADC should be used with caution as a tumor biomarker of HCC.

Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for predicting histological grade of hepatocellular carcinoma: Comparison with conventional diffusion-weighted imaging

AIM

To compare intravoxel incoherent motion (IVIM)-derived parameters with conventional diffusion-weighted imaging (DWI) parameters in predicting the histological grade of hepatocellular carcinoma (HCC) and to evaluate the correlation between the parameters and the histological grades.

METHODS

A retrospective study was performed. Sixty-two patients with surgically confirmed HCCs underwent diffusion-weighted magnetic resonance imaging with twelve b values (10-1200 s/mm²). The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were calculated by two radiologists. The IVIM and conventional DWI parameters were compared among the different grades by using analysis of variance (ANOVA) and the Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic efficiency of distinguishing between low-grade (grade 1, G1) and high-grade (grades 2 and 3, G2 and G3) HCC. The correlation between the parameters and the histological grades was assessed by using the Spearman correlation test. Bland-Altman analysis was used to evaluate the reproducibility of the two radiologists’ measurements.

RESULTS

The differences in the ADC and D values among the groups with G1, G2, and G3 histological grades of HCCs were statistically significant (P < 0.001). The D* and f values had no significant differences among the different histological grades of HCC (P > 0.05). The ROC analyses demonstrated that the D and ADC values had better diagnostic performance in differentiating the low-grade HCC from the high-grade HCC, with areas under the curve (AUCs) of 0.909 and 0.843, respectively, measured by radiologist 1 and of 0.911 and 0.852, respectively, measured by radiologist 2. The following significant correlations were obtained between the ADC, D, and D* values and the histological grades: r = -0.619 (P < 0.001), r = -0.628 (P < 0.001), and r = -0.299 (P = 0.018), respectively, as measured by radiologist 1; r = -0.622 (P < 0.001), r = -0.633 (P < 0.001), and r = -0.303 (P = 0.017), respectively, as measured by radiologist 2. The intra-class correlation coefficient (ICC) values between the two observers were 0.996 for ADC, 0.997 for D, 0.996 for D*, and 0.992 for f values, which indicated excellent inter-observer agreement in the measurements between the two observers.

CONCLUSION

The IVIM-derived D and ADC values show better diagnostic performance in differentiating high-grade HCC from low-grade HCC, and there is a moderate to good correlation between the ADC and D values and the histological grades.

Intravoxel incoherent motion (IVIM) in diffusion-weighted imaging (DWI) for Hepatocellular carcinoma: correlation with histologic grade

Purpose

To assess the correlation between DWI diffusion parameters obtained using Intravoxel Incoherent Motion Method (IVIM) and histological grade of Hepatocellular carcinoma (HCC).

Results

According to Edmondson-Steiner grade lesions were classified with grade 1 (14), grade 2 (30), grade 3 (18), and grade 4 (0). Apparent Diffusion Coefficient (ADC), perfusion fraction (fp), tissue diffusion coefficient (Dt) median values were statistically different in HCC groups with 1, 2, 3 histological grade (p<0.001). A significant correlation was reported between ADC, fp, Dt and histologic grade respectively of 0.687, 0.737 and 0.674. Receiver operating characteristic (ROC) analysis demonstrated that an ADC of 2.11×10-3 mm2/sec, an fp of 47.33% and an Dt of 0.94×10-3 mm²/sec were the optimal cutoff values to differentiate high histological grade (3) versus low histological grade (1-2), with a sensitivity and specificity for ADC of 100% and 100%, for fp of 100% and 89%, for Dt of 100% and 74%, respectively.

Material and Methods

A retrospective approved study was performed including 34 patients with 62 HCCs. IVIM was performed to obtain ADC, fp, pseudo-diffusion coefficient (Dp), Dt coefficients. Kruskal Wallis, Spearman Correlation Coefficient, ROC analysis were performed.

Conclusions

ADC and IVIM-derived fp showed significantly better diagnostic performance in differentiating high-grade from low-grade HCC, and significant correlation was observed between ADC, fp, Dt and histological grade.

Prediction of histological grade of hepatocellular carcinoma using quantitative diffusion-weighted MRI: a retrospective multivendor study

OBJECTIVE

To evaluate the usefulness of quantitative diffusion-weighted (DW) imaging acquired by multivendor magnetic resonance units for predicting grade of hepatocellular carcinoma (HCC).

METHODS

83 patients with 100 histologically diagnosed HCCs who underwent pre-operative liver DW imaging with b = 0 and1000 s mm^-2 or b = 0 and800 s mm^-2 at any of six institutions were included. Two radiologists independently measured the apparent diffusion coefficient (ADC) of the lesion as well as non-ADC parameters, such as the relative contrast ratio and the contrast-to-noise ratio (CNR) between the lesion and the liver parenchyma on high b-value DW images. The diagnostic performance of the DW parameters in discriminating poorly-differentiated HCCs was compared using receiver operating characteristic (ROC) analysis.

RESULTS

The areas under the receiver operating characteristic curves for the CNR (86.4% [95% confidence interval (CI) (77.2-95.6] and 83.9% [95% CI 71.2-96.6] for b = 1000 and 800 s mm^-2, respectively] and the relative contrast ratio (85.3% [95% CI 75.5-94.8] and 83.5% [95% CI 70.5-96.4]) tended to be superior to the ADC [71.1% [95% CI (56.9-85.2)] and 75.7% [95% CI (55.1-96.2)]; p < 0.05 for CNR vs ADC for b = 1000 s mm^-2, but not significant for other parameters) for discrimination of poorly-differentiated HCCs.

CONCLUSION

All DW parameters could discriminate HCC grade. Non-ADC parameters might be more useful than the ADC for predicting poorly-differentiated HCCs. Advances in knowledge: The utility of quantitative DW parameters for predicting HCC grade was demonstrated by using multivendor MR units.

Evaluating histologic differentiation of hepatitis B virus-related hepatocellular carcinoma using intravoxel incoherent motion and AFP levels alone and in combination

PURPOSE

To evaluate histologic differentiation of hepatitis B virus (HBV)-related hepatocellular carcinomas (HCCs) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM)-derived metrics and to compare findings with alpha-fetoprotein (AFP) levels alone and in combination.

MATERIALS AND METHOD

One hundred and six chronic HBV-related HCC patients who underwent IVIM diffusion-weighted magnetic resonance imaging with eleven b values were enrolled. Mean ADC, diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values were determined for all detected lesions. The metrics and AFP levels of different histologically differentiated groups were compared. Spearman's rank correlation was used to assess the statistical dependence among the histologically differentiated HCCs. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance of these metrics and AFP levels alone and in combination.

RESULTS

ADC, D, and f values and AFP levels were significantly different among well-, moderately, and poorly differentiated HCCs. The four metrics were significantly correlated with histologic differentiation. The area under the ROC curve (AUC-ROC) of ADC, D, f, and AFP for diagnosing well-differentiated HCCs was 0.903, 0.84, 0.782, and 0.806, respectively, and the AUC-ROC of above metrics for diagnosing poorly differentiated HCCs was 0.787, 0.726, 0.624, and 0.633, respectively. The combination of ADC and AFP provided an AUC-ROC of 0.945 for well-differentiated HCC. However, this did not provide better performance for diagnosing poorly differentiated HCC.

CONCLUSION

ADC, IVIM metrics, and AFP levels may be useful for evaluating histologic differentiation of HBV-related HCCs, and the combination of ADC and AFP provides better diagnostic performance for well-differentiated HCC.

Prediction of microvascular invasion of hepatocellular carcinoma with preoperative diffusion-weighted imaging: A comparison of mean and minimum apparent diffusion coefficient values

The aim of the study was to investigate the value of preoperative diffusion-weighted imaging (DWI) in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC), using and comparing mean and minimum apparent diffusion coefficient (ADC) values.Preoperative MR images of 318 patients with HCC confirmed by surgical pathology were retrospectively analyzed. All patients underwent preoperative DWI on a 1.5 Tesla MRI scanner. The mean and minimum ADC values of the tumors were measured. Interobserver agreements were assessed by the intraclass correlation coefficient (ICC). The ADC values were compared in HCCs between with and without MVI. ROC curves of ADC values were obtained and then compared in distinguishing HCCs with MVI from those without MVI.There were 211 HCCs with MVI and 107 HCCs without MVI. ICC for the measurements of the mean and minimum ADC values between both observers was 0.88 (95% CI 0.85 - 0.90) and 0.88 (95% CI 0.85 - 0.90), respectively. The mean and minimum ADC values of HCCs with MVI were lower than those of HCCs without MVI (P = .00, .00, respectively). With a cut-off value of 0.98 × 10 mm/s, the minimum ADC (MinADC) showed a sensitivity of 62.56% and a specificity of 65.42% in predicting MVI, whereas the mean ADC provided a sensitivity of 79.15% and a specificity of 50.47% with a cut-off value of 1.19 × 10 mm/s. No significant difference existed between MinADC and mean ADC for their diagnostic performances in the prediction of MVI (P = .48).DWI could preoperatively provide quantitative parameters for predicting MVI of HCC.

Does apparent diffusion coefficient predict the degree of liver regeneration of donor and recipient after living donor liver transplantation?

OBJECTIVE

To elucidate the relationship between the ADCs of the liver graft and the remnant liver and the degree of liver regeneration in LDLT.

MATERIALS AND METHODS

15 recipients and 15 corresponding donors underwent magnetic resonance imaging and computed tomography 1-2 weeks after living donor liver transplantation (LDLT). For diffusion-weighted imaging (DWI), a single-shot echo-planar sequence with b-factors of 0, 500, and 1000s/mm² was scanned. ADCs of the liver parenchyma were calculated at b factors of 0 and 500 and 1000 (ADC 0-500-1000) or 0 and 500 (ADC 0-500) or 500 and 1000 (ADC 500-1000). The liver volume ratio at LDLT, the mean ADCs and the regeneration rate were compared between the graft and the remnant liver using paired-t tests.

RESULTS

The mean liver volume ratio of the recipients (41.3±9.8%) tended to be smaller than that of the donors (51.8±13.8%). The mean ADC 0-500 of the remnant liver (1.72±0.33) was significantly higher than that of the graft (1.43±0.32). The regeneration rate of the graft (2.07±0.41) was significantly higher than that of the remnant liver (1.53±0.49).

CONCLUSION

ADC 0-500 can describe differences in blood perfusion between liver grafts and the remnant liver according to the degree of liver regeneration.