Correlations between the minimum and mean apparent diffusion coefficient values of hepatocellular carcinoma and tumor grade

Abdominal Diffusion-Weighted MRI With Simultaneous Multi-Slice Acquisition: Agreement and Reproducibility of Apparent Diffusion Coefficients Measurements

BACKGROUND

Simultaneous multi-slice diffusion-weighted imaging (SMS-DWI) can shorten acquisition time in abdominal imaging.

PURPOSE

To investigate the agreement and reproducibility of apparent diffusion coefficient (ADC) from abdominal SMS-DWI acquired with different vendors and different breathing schemes.

STUDY TYPE

Prospective.

SUBJECTS

Twenty volunteers and 10 patients.

FIELD STRENGTH/SEQUENCE

3.0 T, SMS-DWI with a diffusion-weighted echo-planar imaging sequence.

ASSESSMENT

SMS-DWI was acquired using breath-hold and free-breathing techniques in scanners from two vendors, yielding four scans in each participant. Average ADC values were measured in the liver, pancreas, spleen, and both kidneys. Non-normalized ADC and ADCs normalized to the spleen were compared between vendors and breathing schemes.

STATISTICAL TESTS

Paired t-test or Wilcoxon signed rank test; intraclass correlation coefficient (ICC); Bland-Altman method; coefficient of variation (CV) analysis; significance level: P < 0.05.

RESULTS

Non-normalized ADCs from the four SMS-DWI scans did not differ significantly in the spleen (P = 0.262, 0.330, 0.166, 0.122), right kidney (P = 0.167, 0.538, 0.957, 0.086), and left kidney (P = 0.182, 0.281, 0.504, 0.405), but there were significant differences in the liver and pancreas. For normalized ADCs, there were no significant differences in the liver (P = 0.315, 0.915, 0.198, 0.799), spleen (P = 0.815, 0.689, 0.347, 0.423), pancreas (P = 0.165, 0.336, 0.304, 0.584), right kidney (P = 0.165, 0.336, 0.304, 0.584), and left kidney (P = 0.496, 0.304, 0.443, 0.371). Inter-reader agreements of non-normalized ADCs were good to excellent (ICCs ranged from 0.861 to 0.983), and agreement and reproducibility were good to excellent depending on anatomic location (CVs ranged from 3.55% to 13.98%). Overall CVs for abdominal ADCs from the four scans were 6.25%, 7.62%, 7.08, and 7.60%.

DATA CONCLUSION

The normalized ADCs from abdominal SMS-DWI may be comparable between different vendors and breathing schemes, showing good agreement and reproducibility. ADC changes above approximately 8% may potentially be considered as a reliable quantitative biomarker to assess disease or treatment-related changes.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Understanding the role of radiologists in complex treatment decisions for patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and represents a significant global health burden. Management of HCC can be challenging due to multiple factors, including variable expectations for treatment outcomes. Several treatment options are available, each with specific eligibility and ineligibility criteria, and are provided by a multidisciplinary team of specialists. Radiologists should be aware of the types of treatment options available, as well as the criteria guiding the development of individualized treatment plans. This awareness enables radiologists to contribute effectively to patient-centered multidisciplinary tumor boards for HCC and play a central role in reassessing care plans when the treatment response is deemed inadequate. This comprehensive review aims to equip radiologists with an overview of HCC staging systems, treatment options, and eligibility criteria. The review also discusses the significance of imaging in HCC diagnosis, treatment planning, and monitoring treatment response. Furthermore, we highlight the crucial branch points in the treatment decision-making process that depend on radiological interpretation.

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.

Radiomics nomogram in aiding preoperatively dilatation and curettage in differentiating type II and type I endometrial cancer

AIM

To established a radiomics nomogram for improving the dilatation and curettage (D&C) result in differentiating type II from type I endometrial cancer (EC) preoperatively.

MATERIAL AND METHODS

EC patients (n=875) were enrolled retrospectively and divided randomly into a training cohort (n=437) and a test cohort (n=438), according to the ratio of 1:1. Radiomics signatures were extracted and selected from apparent diffusion coefficient (ADC) maps. A multivariate logistic regression analysis was used to identify the independent clinical risk factors. An ADC based-radiomics nomogram was built by integrating the selected radiomics signatures and the independent clinical risk factors. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) were calculated to compare the discrimination performances between the radiomics nomogram and the D&C result.

RESULTS

Receiver operating characteristic (ROC) curves showed that the clinical risk factors, the D&C, and the ADC based-radiomics nomogram yielded areas under the ROC curves (AUCs) of 0.70 (95% CI: 0.64-0.76), 0.85 (95% CI: 0.80-0.89), and 0.93 (95% CI: 0.90-0.96) in the training cohort and 0.64 (95% CI: 0.57-0.71), 0.82 (95% CI: 0.77-0.87) and 0.91 (95% CI: 0.87-0.95) in the test cohort, respectively. The DCA, NRI, and IDI demonstrated the clinically usefulness of the ADC based-radiomics nomogram.

CONCLUSION

The ADC-based radiomics nomogram could be used to improve the D&C result in differentiating type II from type I EC preoperatively.

Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study

PURPOSE

To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI.

METHODS

This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60 days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test.

RESULTS

In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78).

CONCLUSION

The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions

Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.

Role of MRI-Derived Radiomics Features in Determining Degree of Tumor Differentiation of Hepatocellular Carcinoma

Background: To investigate radiomics ability in predicting hepatocellular carcinoma histological degree of differentiation by using volumetric MR imaging parameters. Methods: Volumetric venous enhancement and apparent diffusion coefficient were calculated on baseline MRI of 171 lesions. Ninety-five radiomics features were extracted, then random forest classification identified the performance of the texture features in classifying tumor degree of differentiation based on their histopathological features. The Gini index was used for split criterion, and the random forest was optimized to have a minimum of nine participants per leaf node. Predictor importance was estimated based on the minimal depth of the maximal subtree. Results: Out of 95 radiomics features, four top performers were apparent diffusion coefficient (ADC) features. The mean ADC and venous enhancement map alone had an overall error rate of 39.8%. The error decreased to 32.8% with the addition of the radiomics features in the multi-class model. The area under the receiver-operator curve (AUC) improved from 75.2% to 83.2% with the addition of the radiomics features for distinguishing well- from moderately/poorly differentiated HCCs in the multi-class model. Conclusions: The addition of radiomics-based texture analysis improved classification over that of ADC or venous enhancement values alone. Radiomics help us move closer to non-invasive histologic tumor grading of HCC.

Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation

Background

High early recurrence (ER) of hepatocellular carcinoma (HCC) after microwave ablation (MWA) represents a sign of aggressive behavior and severely worsens prognosis. The aim of this study was to estimate the outcome of HCC following MWA and develop a response algorithmic strategy based on multiparametric MRI and clinical variables.

Methods

In this retrospective study, we reviewed the records of 339 patients (mean age, 62 ± 12 years; 106 men) treated with percutaneous MWA for HCC between January 2014 and December 2017 that were evaluated by multiparametric MRI. These patients were randomly split into a development and an internal validation group (3:1). Logistic regression analysis was used to screen imaging features. Multivariate Cox regression analysis was then performed to determine predictors of ER (within 2 years) of MWA. The response algorithmic strategy to predict ER was developed and validated using these data sets. ER rates were also evaluated by Kaplan–Meier analysis.

Results

Based on logistic regression analyses, we established an image response algorithm integrating ill-defined margins, lack of capsule enhancement, pre-ablative ADC, ΔADC, and EADC to calculate recurrence scores and define the risk of ER. In a multivariate Cox regression model, the independent risk factors of ER (p < 0.05) were minimal ablative margin (MAM) (HR 0.57; 95% CI 0.35 – 0.95; p < 0.001), the recurrence score (HR: 9.25; 95% CI 4.25 – 16.56; p = 0.021), and tumor size (HR 6.21; 95% CI 1.25 – 10.82; p = 0.014). Combining MAM and tumor size, the recurrence score calculated by the response algorithmic strategy provided predictive accuracy of 93.5%, with sensitivity of 92.3% and specificity of 83.1%. Kaplan–Meier estimates of the rates of ER in the low-risk and high-risk groups were 6.8% (95% CI 4.0 – 9.6) and 30.5% (95% CI 23.6 – 37.4), respectively.

Conclusion

A response algorithmic strategy based on multiparametric MRI and clinical variables was useful for predicting the ER of HCC after MWA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00471-5.

A Scoring System for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Quantitative Functional MRI

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a histopathological marker and risk factor for HCC recurrence. We integrated diffusion-weighted imaging (DWI) and magnetic resonance (MR) image findings of tumors into a scoring system for predicting MVI. In total, 228 HCC patients with pathologically confirmed MVI who underwent surgical resection or liver transplant between November 2012 and March 2021 were enrolled retrospectively. Patients were divided into a right liver lobe group (n = 173, 75.9%) as the model dataset and a left liver lobe group (n = 55, 24.1%) as the model validation dataset. Multivariate logistic regression identified two-segment involved tumor (Score: 1; OR: 3.14; 95% CI: 1.22 to 8.06; p = 0.017); ADC_min ≤ 0.95 × 10⁻³ mm²/s (Score: 2; OR: 10.88; 95% CI: 4.61 to 25.68; p = 0.000); and largest single tumor diameter ≥ 3 cm (Score: 1; OR: 5.05; 95% CI: 2.25 to 11.30; p = 0.000), as predictive factors for the scoring model. Among all patients, sensitivity was 89.66%, specificity 58.04%, positive predictive value 68.87%, and negative predictive value 84.41%. For validation of left lobe group, sensitivity was 80.64%, specificity 70.83%, positive predictive value 78.12%, and negative predictive value 73.91%. The scoring model using ADC_min, largest tumor diameter, and two-segment involved tumor provides high sensitivity and negative predictive value in MVI prediction for use in routine functional MR.

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.

Pathologic, Molecular, and Prognostic Radiologic Features of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with variable biologic aggressiveness based on the tumor grade, presence or absence of vascular invasion, and pathologic and molecular classification. Knowledge and understanding of the prognostic implications of different pathologic and molecular phenotypes of HCC are emerging, with therapeutics that promise to provide improved outcomes in what otherwise remains a lethal cancer. Imaging has a central role in diagnosis of HCC. However, to date, the imaging algorithms do not incorporate prognostic features or subclassification of HCC according to its biologic aggressiveness. Emerging data suggest that some imaging features and further radiologic, pathologic, or radiologic-molecular phenotypes may allow prediction of the prognosis of patients with HCC. An invited commentary by Bashir is available online. Online supplemental material is available for this article. ^©RSNA, 2021.

Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice

Magnetic resonance imaging (MRI) is highly important for the detection, characterization, and follow-up of focal liver lesions. Several quantitative MRI-based methods have been proposed in addition to qualitative imaging interpretation to improve the diagnostic work-up and prognostics in patients with focal liver lesions. This includes DWI with apparent diffusion coefficient measurements, intravoxel incoherent motion, perfusion imaging, MR elastography, and radiomics. Multiple research studies have reported promising results with quantitative MRI methods in various clinical settings. Nevertheless, applications in everyday clinical practice are limited. This review describes the basic principles of quantitative MRI-based techniques and discusses the main current applications and limitations for the assessment of focal liver lesions.

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.

Can LI-RADS imaging features at gadoxetic acid-enhanced MRI predict aggressive features on pathology of single hepatocellular carcinoma?

PURPOSE

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC.

MATERIALS AND METHODS

From July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid-enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model.

RESULTS

Twenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size > 5 cm (OR = 9.852; p < 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p < 0.001), and serum AFP level > 400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence.

CONCLUSION

LI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.

Associations Between Apparent Diffusion Coefficient Value With Pathological Type, Histologic Grade, and Presence of Lymph Node Metastases of Esophageal Carcinoma

Objective:

To investigate the application value of apparent diffusion coefficient value in the pathological type, histologic grade, and presence of lymph node metastases of esophageal carcinoma.

Materials and Methods:

Eighty-six patients with pathologically confirmed esophageal carcinoma were divided into different groups according to pathological type, histological grade, and lymph node status. All patients underwent conventional magnetic resonance imaging and diffusion-weighted imaging scan, and apparent diffusion coefficient values of tumors were measured. Independent sample t test and 1-way variance were used to compare the difference of apparent diffusion coefficient value in different pathological types, histologic grades, and lymph node status. Correlation between the apparent diffusion coefficient value and the histologic grade was evaluated using Spearman rank correlation test. Receiver operating characteristic curve of apparent diffusion coefficient value was generated to evaluate the differential diagnostic efficiency of poorly and well/moderately differentiated esophageal carcinoma.

Results:

No significant difference was observed in apparent diffusion coefficient value between esophageal squamous cell carcinoma and adenocarcinoma and in patients between those with and without lymph node metastases (P > .05). The differences of apparent diffusion coefficient value were statistically significant between different histologic grades of esophageal carcinoma (P < .05). The apparent diffusion coefficient value was positively correlated with histologic grade (rs = 0.802). The apparent diffusion coefficient value ≤1.25 × 10⁻³ mm²/s as the cutoff value for diagnosis of poorly differentiated esophageal carcinoma with the sensitivity of 84.3%, and the specificity was 94.3%.

Conclusions:

The performance of apparent diffusion coefficient value was contributing to predict the histologic grade of esophageal carcinoma, which might increase lesions characterization before choosing the best therapeutic alternative. However, they do not correlate with pathological type and the presence of lymph node metastases of esophageal carcinoma.

A comparative study of monoexponential versus biexponential models of diffusion-weighted imaging in differentiating histologic grades of hepatitis B virus-related hepatocellular carcinoma

PURPOSE

To compare the diagnostic value of apparent diffusion coefficient (ADC) and intravoxel incoherent motion metrics in discriminating histologic grades of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) infection.

METHODS

117 chronic HBV patients with 120 pathologically confirmed HCCs after surgical resection or liver transplantation were enrolled in this retrospective study. Diffusion-weighted imaging was performed using eleven b values (0-1500 s/mm²) and two b values (0, 800 s/mm²) successively on a 3.0 T system. ADC_{0, 800}, ADC_total, diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were calculated. The parameters of three histologically differentiated subtypes were investigated using Kruskal-Wallis test, Spearman rank correlation, and receiver-operating characteristic analysis. Interobserver agreement was assessed using the intraclass correlation coefficient.

RESULTS

There was excellent agreement for ADC_total/D/f, good agreement for ADC_0,800, and moderate agreement for D*. ADC_total, ADC_{0, 800,}D, and f were significantly different for well, moderately, and poorly differentiated HCCs (P < 0.001), and they were all inversely correlated with histologic grades: r = - 0.633, - 0.394, - 0.435, and - 0.358, respectively (P < 0.001). ADC_total demonstrated higher performance than ADC_0,800 in diagnosing both well and poorly differentiated HCCs (P < 0.001 and P = 0.04, respectively). ADC_total showed higher performance than D and f in diagnosing well differentiated HCCs (P < 0.001) and similar performance in diagnosing poorly differentiated HCCs (P = 0.06 and 0.13, respectively).

CONCLUSIONS

ADC_total showed better diagnostic performance than ADC_0,800, D, and f to discriminate histologic grades of 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.

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.

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.

Conventional MR and diffusion-weighted imaging of musculoskeletal soft tissue malignancy: correlation with histologic grading

AIM

To evaluate proven soft tissue musculoskeletal malignancies blinded to their Fédération Nationale des Centres de Lutte Contre le Cancer histologic grades to identify the predictive values of conventional MR findings and best fit region of interest (ROI) apparent diffusion coefficient (ADC) measurements.

MATERIALS AND METHODS

Fifty-one consecutive patients with different histologic grades were evaluated by four readers (R1-4) of different experience levels. Quantitatively, the maximum longitudinal size, tumor to muscle signal intensity ratios, and ADC measurements and, qualitatively, the spatial location of the tumor, its signal alterations, heterogeneity, intralesional hemorrhage or fat, and types of enhancement were assessed. Intraclass correlation, weighted kappa, ANOVA, and Fisher exact tests were used.

RESULTS

There were 22/51 (43%) men (mean age ± SD = 52 ± 16 years) and 29/51 (57%) women (mean age ± SD = 54± 17 years), with the majority of tumors 38/51 (75%) in the lower extremities. Histologic grades were I in 8/51 (16%), II in 17/51 (33%), and III in 26/51 (51%), respectively. The longitudinal dimensions were different among three grades (p = 0.0015), largest with grade I. More central enhancements and deep locations were seen in grade III tumors (p = 0.0191, 0.0246). The ADC mean was significantly lower in grade III than in grade I or II (p < 0.0001 and p = 0.04). The ADC min was significantly lower in grade III than in grade I (p = 0.02). Good to excellent agreements were seen for T1/T2 tumor/muscle ratios, longitudinal dimension, and ADC (ICC = 0.60-0.98).

CONCLUSION

Longitudinal tumor dimension, central enhancement, and ADC values differentiate histology grades in musculoskeletal soft tissue malignancy with good to excellent inter-reader reliability.

KEY POINTS

• The longitudinal tumor dimension of grade III malignancy is smaller than that of grade I (p < 0.0001), and higher-grade tumors are located deeper (p = 0.0246). • The ADC mean is significantly lower in grade III than in grade I or grade II (p < 0.0001 and p = 0.04). • The ADC minimum is significantly lower in grade III than in grade I (p = 0.02).

Volumetric Apparent Diffusion Coefficient Histogram Analysis in Differentiating Intrahepatic Mass-Forming Cholangiocarcinoma From Hepatocellular Carcinoma

BACKGROUND

Accurate differentiation between intrahepatic mass-forming cholangiocarcinoma (IMCC) and hepatocellular carcinoma (HCC) is needed because treatment and prognosis differ significantly.

PURPOSE

To explore whether volumetric apparent diffusion coefficient (ADC) histogram analysis can provide additional value to dynamic enhanced MRI in differentiating IMCC from HCC.

STUDY TYPE

Retrospective.

POPULATION

In all, 131 patients with pathologically proven IMCC (n = 33) or HCC (n = 98).

FIELD STRENGTH/SEQUENCE

3.0T MRI/conventional T₁ -weighted imaging (T₁ WI), T₂ WI, and diffusion-weighted imaging (DWI) with b value of 800 sec/mm² , dynamic enhanced MRI with gadobenate dimeglumine.

ASSESSMENT

Dynamic enhanced MR images were analyzed by two independent reviewers using a five-point scale to determine the diagnosis. Volumetric ADC assessments were performed independently by two radiologists to obtain different histogram parameters for each lesion. Quantitative histogram parameters were compared between the IMCC group and HCC group. Diagnostic performance of dynamic enhanced MRI, volumetric ADC histogram analysis, and the combination of both were analyzed.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) analysis, independent Student's t-test, or Mann-Whitney U-test, receiver operator characteristic (ROC) curves analysis, and McNemar test.

RESULTS

The sensitivity and specificity for dynamic enhanced MRI to differentiate IMCC from HCC were 82.1% and 82.6%, respectively. For all volumetric ADC histogram parameters, the 75th percentile ADC (ADC_75% ) had the highest AUC (0.791) in differentiating IMCC from HCC, with sensitivity and specificity of 69.7% and 77.6%, respectively. When combining dynamic enhanced MRI with ADC_75% , the sensitivity and specificity were 82.1% and 91.9%, respectively. Compared to dynamic enhanced MRI alone, the specificity for combined dynamic enhanced MRI and ADC_75% was significantly increased (P = 0.008).

DATA CONCLUSION

Volumetric ADC histogram analysis provides additional value to dynamic enhanced MRI in differentiating IMCC from HCC.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:975-983.

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.

Differentiation Between Hepatocellular Carcinoma Showing Hyperintensity on the Hepatobiliary Phase of Gadoxetic Acid-Enhanced MRI and Focal Nodular Hyperplasia by CT and MRI

OBJECTIVE

The purpose of this study is to identify points useful in the imaging differentiation of hepatocellular carcinoma (HCC) showing hyperintensity on the hepatobiliary phase of gadoxetic acid-enhanced MRI and focal nodular hyperplasia (FNH) and FNH-like nodules.

MATERIALS AND METHODS

We enrolled consecutive 51 pathologically diagnosed HCCs that were hyperintense on hepatobiliary phase imaging (47 patients, including 44 with cirrhosis) and 10 FNHs and eight FNH-like nodules (16 patients, including five with cirrhosis). Imaging findings of dynamic CT and gadoxetic acid-enhanced MRI were assessed by two radiologists and compared between HCC and FNH.

RESULTS

The apparent diffusion coefficient (ADC) was lower in hyperintense HCC than in FNH (p = 0.004). The enhancement patterns of hyperintense HCC and FNH at dynamic CT were significantly different (p < 0.0001), with 95.9% of HCCs and 22.2% of FNHs showing arterial phase enhancement with a washout pattern, and 4.1% of HCCs and 77.8% of FNHs showing arterial phase enhancement without a washout pattern. The frequency of coronalike enhancement was 84.3% in hyperintense HCCs versus 11.1% in FNHs (p < 0.0001). The signal distribution on the hepatobiliary phase was significantly different between hyperintense HCCs and FNHs (p = 0.0002). The frequency of a capsulelike rim was 88.2% versus 22.2%, that of a mosaic appearance was 72.5% versus 11.1%, and that of a central scar was 0% versus 55.6% in hyperintense HCCs versus FNHs (all p < 0.0001). Multivariate logistic regression analysis showed that ADC ratio (p = 0.03; odds ratio, 0.12) and enhancement pattern at dynamic CT (p = 0.04; odds ratio, 16.21) were the independent factors for differentiation between hyperintense HCC and FNH.

CONCLUSION

For the diagnosis of hyperintense HCC differentiated from FNH and FNH-like nodule, arterial phase enhancement and washout pattern at dynamic CT and decrease of ADC ratio would be important findings.

Prediction of the histopathologic findings of intrahepatic cholangiocarcinoma: qualitative and quantitative assessment of diffusion-weighted imaging

OBJECTIVE

To correlate qualitative and quantitative diffusion weighted imaging (DWI) characteristics of intrahepatic cholangiocarcinoma (ICC) with histopathologic tumour grade and fibrosis content.

METHODS

Fifty-one patients (21M/30F; mean age 61y) with ICC and MRI including DWI were included in this IRB-approved multicentre retrospective study. Qualitative tumour features were assessed. Tumour apparent diffusion coefficient (ADC) mean, minimum, and normalized (nADC_liver) values were computed. Tumour grade [well(G1), moderately(G2), or poorly differentiated(G3)] and tumour fibrosis content [minimal(1), moderate(2), or abundant(3)] were categorized pathologically. Imaging findings and ADC values were compared with pathologic measures. Utility of ADC values for predicting tumour grade was assessed using ROC analysis.

RESULTS

51 ICCs (mean size 6.5±1.1 cm) were assessed. 33/51(64%) of ICCs demonstrated diffuse hyperintensity and 15/51(29%) demonstrated target appearance on DWI. Infiltrative morphology (p=0.02) and tumour size (p=0.04) were associated with G3. ADCmean and nADCmean of G3 (1.32±0.47x10^-3 mm²/sec and 0.97±0.95) were lower than G1+G2 (1.57±0.39x10^-3 mm²/sec and 1.24±0.49; p=0.03 and p=0.04). ADCmean and nADCmean were inversely correlated with tumour grade (p<0.025). No correlation was found between ADC and tumour fibrosis content. AUROC, sensitivity and specificity of nADCmean for G3 versus G1+G2 were 0.71, 89.5% and 55.5%.

CONCLUSION

ADC quantification has reasonable accuracy for predicting ICC grade.

KEY POINTS

• ADC quantification was useful for predicting ICC tumour grade. • Infiltrative tumour morphology and size were associated with poorly differentiated ICCs. • ADC values depended more on ICC tumour grade than fibrosis content. • Ability to predict ICC tumour grade non-invasively could impact patient management.

Diffusion Quantification in Body Imaging

Diffusion-weighted imaging (DWI) is increasingly incorporated into routine body magnetic resonance imaging protocols. DWI can assist with lesion detection and even in characterization. Quantitative DWI has exhibited promise in the discrimination between benign and malignant pathology, in the evaluation of the biologic aggressiveness, and in the assessment of the response to treatment. Unfortunately, inconsistencies in DWI acquisition parameters and analysis have hampered widespread clinical utilization. Focusing primarily on liver applications, this article will review the basic principles of quantitative DWI. In addition to standard mono-exponential fitting, the authors will discuss intravoxel incoherent motion and diffusion kurtosis imaging that involve more sophisticated approaches to diffusion quantification.

Diagnosis of hepatocellular carcinoma: An update on international guidelines

Imaging is essential for the successful management of patients with or at risk of developing hepatocellular carcinoma (HCC). If ultrasound remains the key screening modality, computed tomography and magnetic resonance imaging (MRI) can play a major role in the characterization and noninvasive diagnosis of nodules in patients at risk of developing HCC. Each technique has succeeded in adapting to the wide histological spectrum of focal liver lesions. In this review, we discuss recent advancements in imaging techniques and evaluation - notably diffusion-weighted imaging, contrast-enhanced ultrasound, and liver-specific MRI contrast agents - as well as their addition to international guidelines and reporting systems such as the Liver imaging reporting and data system (LI-RADS).

Diffusion-weighted imaging (DWI) of hepatocellular carcinomas: a retrospective analysis of the correlation between qualitative and quantitative DWI and tumour grade

AIM

To evaluate the application of qualitative and quantitative diffusion-weighted imaging (DWI) in predicting the histological grade of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Two hundred and fifty-four patients with pathologically confirmed HCC who underwent hepatic DWI on a 1.5-T platform (b = 0, 600 s/mm²) were evaluated retrospectively. HCCs were divided into well-, moderately, and poorly differentiated groups. The relationships between naked-eye signal intensity (SI), SI values, apparent diffusion coefficient (ADC) values on DWI, and the histopathological differentiation of HCC were analysed. Receiver operating characteristic (ROC) curves were drawn to determine the optimal operating points (OOPs) of the SI and ADC values to predict the tumour grade.

RESULTS

A weak negative correlation (r=-0.350, p<0.05) was obtained between naked-eye SI and histological grade. There was a significant difference in mean SI values between well- (68.32±31.71) and moderately (102.39±45.55)/poorly (114.55±32.15) differentiated HCC but not between moderately and poorly differentiated HCC. The OOP of the SI value by ROC curve analysis was 66.5 to predict well-differentiated HCC. The mean ADC values of well-, moderately, and poorly differentiated HCC were 1.67±0.13×10^-3, 1.31±0.16×10^-3, and 1.08±0.11×10^-3 mm²/s, respectively, with significant differences between any two combinations of groups. The OOPs of ADC to diagnose well- and poorly differentiated HCC were 1.5×10^-3 and 1.24×10^-3 mm²/s, respectively.

CONCLUSION

Qualitative and quantitative SI and ADC values at DWI may be useful to estimate the histological grade of HCC preoperatively and non-invasively.