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Objectives: Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Methods: 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size., Results: The results suggested a significantly higher fraction of impeded diffusion (F ID ) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, F ID , tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by F ID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164)., Conclusion: IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC)., Methods: Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (D t ), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs)., Results: MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f 4 ) and a significantly lower fraction of habitat 2 (f 2 ) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model., Conclusions: DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy., Clinical Relevance Statement: The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management., Key Points: • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue., Methods: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C 1 , C 2 , C 3 , C 1 C 2 , F 1 , F 2 , F 3 , F 1 F 2 ) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model., Results: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C 2 ) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C 1 , C 2 , C 1 C 2 , F 1 , F 2 , F 3 ) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F 1 , C 1 C 2 , and C 2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively)., Conclusions: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions., (© 2024. The Author(s).)

Background: Recently, a prototype 5.0 T whole-body MRI scanner was developed. A 5.0 T diffusion-weighted imaging (DWI) may help overcome the issues that limit 3.0 T DWI., Purpose: To evaluate the feasibility of 5.0 T high-field DWI in the upper abdomen and assess the agreement of the apparent diffusion coefficient (ADC) with that from 3.0 T abdominal DWI., Study Type: Prospective proof of concept., Population: Nine volunteers (mean ± SD age: 37.3 ± 7.0 years, 8 M), eight healthy and one with liver and kidney cysts., Field Strength/sequence: 3.0 T and 5.0 T; respiratory-triggered spin-echo echo-planar-imaging (SE-EPI)-based DWI sequence., Assessment: Subjective image quality scores. The ADC values in abdominal organs (liver, pancreas, spleen, and kidney) were measured by two observers for evaluating the interobserver and interfield agreement., Statistical Tests: Wilcoxon-rank sum test, Bland-Altman analysis, intraclass correlation coefficients (ICCs), and coefficients of variation (CVs)., Results: The 5.0 T DWI displayed an increase in subjective image quality score compared to 3.0 T DWI without the significant difference (3.0 T DWI: 3.50 ± 0.47, 5.0 T DWI: 3.72 ± 0.42, P = 0.157). Both the interfield and interobserver agreements of ADC values were substantial to excellent (ICCs = 0.640-0.902). For all four upper abdominal organs, there were no significant differences between the ADC values measured by two observers and between the ADC values of 3.0 T and 5.0 T DWI (P = 0.134-1.000). The CVs of ADC measurements from 3.0 T and 5.0 T DWI were all less than 15.0% (6.7%-14.2%)., Data Conclusion: The substantial to excellent agreements between the ADC values measured with 3.0 T and 5.0 T DWI for liver, pancreas, spleen, and kidney suggested that 5.0 T DWI can be applied for abdominal imaging. The ADC values from 5.0 T abdominal DWI hold the potential to serve as the quantitative markers for clinical investigations., Evidence Level: 2 TECHNICAL EFFICACY: Stage 1., (© 2022 International Society for Magnetic Resonance in Medicine.)

Purpose: Restriction spectrum imaging (RSI) is a novel diffusion MRI model that separates water diffusion into several microscopic compartments. The restricted compartment correlating to the tumor cellularity is expected to be a potential indicator of rectal cancer aggressiveness. Our aim was to assess the ability of RSI model for rectal tumor grading., Methods: Fifty-eight patients with different rectal cancer grading confirmed by biopsy were involved in this study. DWI acquisitions were performed using single-shot echo-planar imaging (SS-EPI) with multi-b-values at 3 T. We applied a three-compartment RSI model, along with ADC model and diffusion kurtosis imaging (DKI) model, to DWI images of 58 patients. ROC and AUC were used to compare the performance of the three models in differentiating the low grade (G1 + G2) and high grade (G3). Mean ± standard deviation, ANOVA, ROC analysis, and correlation analysis were used in this study., Results: The volume fraction of restricted compartment C 1 from RSI was significantly correlated with grades (r = 0.403, P = 0.002). It showed significant difference between G1 and G3 (P = 0.008) and between G2 and G3 (P = 0.01). As for the low-grade and high-grade discrimination, significant difference was found in C 1 (P < 0.001). The AUC of C 1 for differentiation between low-grade and high-grade groups was 0.753 with a sensitivity of 72.0% and a specificity of 70.0%., Conclusion: The three-compartment RSI model was able to discriminate the rectal cancer of low and high grades. The results outperform the traditional ADC model and DKI model in rectal cancer grading., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer., Methods: A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance., Results: For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer ( p < 0.05). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and K app (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer., Conclusion: The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Zhijun Geng et al.)

Purpose: Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. Results: MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10–3 vs 0.572 ± 0.148 × 10–3 mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10–3 vs 1.471 ± 0.322 × 10–3 mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. Conclusion: VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann–Whitney U tests or independent Student's t-tests were conducted to identify DWI-derived parameters that exhibited significant differences. Spearman or Pearson correlation tests were performed to assess the relationships among different DWI-derived biological markers. Subsequently, four machine learning classifier-based models were trained using various DWI-derived parameters as input features. Finally, diagnostic performance was evaluated using ROC analysis with 5-fold cross-validation. Results: With the exception of the pseudo-diffusion coefficient (Dp), IVIM-derived and DKI-derived parameters all demonstrated significant differences between low-grade and high-grade rectal cancer. The logistic regression-based machine learning classifier yielded the most favorable diagnostic efficacy (AUC: 0.902, 95% Confidence Interval: 0.754–1.000; Specificity: 0.856; Sensitivity: 0.925; Youden Index: 0.781). Conclusions: utilizing multiple DWI-derived biological markers in conjunction with a strategy employing multiple machine learning classifiers proves valuable for the noninvasive grading of rectal cancer. [ABSTRACT FROM AUTHOR]

Objective: This study sought to determine the diagnostic performance of magnetic resonance elastography (MRE) for pancreatic solid masses, compared with diffusion-weighted imaging (DWI) and serum CA19-9, to establish a threshold for differentiating between pancreatic ductal adenocarcinoma (PDAC) and benign tumors in pancreas. Materials and methods: Between July 2021 to January 2023, 75 adult patients confirmed with pancreatic solid tumors were enrolled in this prospective and consecutive study. All patients underwent MRE and DWI examinations that were both performed with a spin echo-EPI sequence. Stiffness maps and apparent diffusion coefficient (ADC) maps were generated, with MRE-derived mass stiffness and stiffness ratio (computing as the ratio of mass stiffness to the parenchyma stiffness) and DWI-derived ADC values obtained by placing regions of interest over the focal tumors on stiffness and ADC maps. Further analysis of comparing diagnostic performances was assessed by calculating the area under ROC curves. Results: PDAC had significantly higher tumor stiffness [3.795 (2.879–4.438) kPa vs. 2.359 (2.01–3.507) kPa, P = 0.0003], stiffness ratio [1.939 (1.562–2.511) vs. 1.187 (1.031–1.453), P < 0.0001] and serum CA19-9 level [276 (31.73–1055) vs. 10.45 (7.825–14.15), P < 0.0001] than other pancreatic masses. Mass stiffness, stiffness ratio and serum CA19-9 showed good diagnostic performance for differentiation with AUC of 0.7895, 0.8392 and 0.9136 respectively. The sensitivity/specificity/positive predictive value/negative predictive value for differentiating malignant from benign pancreatic tumors with mass stiffness (cutoff, > 2.8211 kPa) and stiffness ratio (cutoff, > 1.5117) were 78.4/66.7/82.9/60% and 77.8/83.3/90.3/65.2% respectively. The combined performance of Mass stiffness, stiffness ratio and serum CA19-9 got an AUC of 0.9758. Conclusion: MRE holds excellent clinical potential in discriminating pancreatic ductal adenocarcinoma from other pancreatic solid masses according to their mechanical properties. [ABSTRACT FROM AUTHOR]

Background: Restriction spectrum imaging (RSI) is an advanced quantitative diffusion‐weighted magnetic resonance imaging (DWI) technique to assess breast cancer. Purpose: To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer. Study Type: Retrospective. Population: Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions. Field Strength/Sequence: 3‐T; RSI‐based DWI sequence with echo‐planar imaging technique. Assessment: The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f1, hindered diffusion f2, free diffusion f3, and signal fractions f1f2) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer. Statistical Tests: Mann–Whitney U test or Student's t‐test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P‐value <0.05 was considered statistically significant. Results: Malignant breast lesions showed significantly lower ADC and f3 values, and significantly higher f1 and f1f2 values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI‐derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f3 value was significantly differed between estrogen receptor (ER)‐positive and ER‐negative tumors. The ADC, f1, f3, and f1f2 values were significantly different progesterone receptor (PR)‐positive and PR‐negative status. Data Conclusion: The RSI‐derived parameters (f1, f3, and f1f2) may facilitate the differential diagnosis between benign and malignant breast lesions. Level of Evidence: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Purpose: To predict multiple prognostic factors of HCC including histopathologic grade, the expression of Ki67 as well as capsule formation with intravoxel incoherent motions imaging by extracting the histogram metrics. Patients and Methods: A total of 52 patients with HCC were recruited with the MR examinations undertaken at a 3T scanner. Histogram metrics were extracted from IVIM-derived parametric maps. Independent student t-test was performed to explore the differences in metrics across different subtypes of prognostic factors. Spearman correlation test was utilized to evaluate the correlations between the IVIM metrics and prognostic factors. ROC analysis was applied to evaluate the diagnostic performance. Results: According to the independent student t-test, there were 18, 4, and 8 IVIM-derived histogram metrics showing the capability for differentiating the subtypes of histopathologic grade, Ki67, and capsule formation, respectively, with P-values of less than 0.05. Besides, there existed a lot of significant correlations between IVIM metrics and prognostic factors. Finally, by integrating different histogram metrics showing significant differences between various subgroups together via establishing logistic regression based diagnostic models, greatest diagnostic power was obtained for grading HCC (AUC=0.917), diagnosing patients with highly expressed Ki67 (AUC=0.861) and diagnosing patients with capsule formation (AUC=0.839). Conclusion: Multiple prognostic factors including histopathologic grade, Ki67 expression status, and capsule formation can be accurately predicted with assistance of histogram metrics sourced from a single IVIM scan. [ABSTRACT FROM AUTHOR]

• FROC model can improve the distinguishing ability of benign and malignant breast lesions. • D resulted in higher diagnostic accuracy for predicting malignancy. • Combining multiple parameters is a advantage of the FROC diffusion model. To assess the feasibility of using three diffusion parameters (D, β, and μ) derived from fractional-order calculus (FROC) diffusion model for improving the differentiation between benign and malignant breast lesions. In this prospective study, 103 patients with breast lesions were enrolled. All subjects underwent diffusion-weighted imaging (DWI) with 12b values. Inter-observer agreement with respect to quantification of parameters by two radiologists was assessed using intraclass coefficient. Conventional apparent diffusion coefficient (ADC) and three FROC model parameters D, β, and μ were compared between the benign lesion and malignant lesion groups using the Mann-Whitney U test. Then, a comprehensive prediction model was created by using binary logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the parameters using histopathological diagnosis as the reference standard. The FROC parameters and ADC all exhibited significant differences between benign lesions and malignant lesions (P＜0.001). Among the individual parameters, the sensitivity of μ was higher than ADC (95.92% for μ vs 91.84% for ADC), and the specificity of β was higher than ADC (72.22% for β vs 70.37% for ADC). The combination of ADC and FROC parameters (D and β) generated the largest area under the ROC curve (0.841) when compared with individual parameters, indicating an improved performance for differentiating benign lesions from malignant lesions. This study demonstrated the feasibility of using the FROC diffusion model to improve the accuracy of identifying malignant breast lesions. [ABSTRACT FROM AUTHOR]

To assess the clinical potential of a set of new diffusion parameters (D, β, and μ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0–2000 s/mm2). The FROC model parameters including diffusion coefficient (D), fractional order parameter (β), a microstructural quantity (μ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t- test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the β and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. Among all the studied diffusion parameters, significant differences were found in D, β, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher β values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 μm2/ms vs. 1.03 ± 0.13 μm2/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 μm2/ms vs. 2.09 ± 0.70 μm2/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and β demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; β: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs. The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate Cytokeratin 19 expression in hepatocellular carcinoma (HCC) with diffusion parameters derived from mono-exponential model (MEM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) imaging, and fractional order calculus (FROC) model and compare their predictive performance.Method: 61 patients with pathologically confirmed primary HCC were included in this prospective study. All the DWIs were acquired using a 3.0 T MR scanner with 10b-values (0-2000 s/mm2). The apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC), heterogeneity index (α), apparent kurtosis coefficient (AK), apparent diffusion coefficient (AD), pseudo-diffusion coefficient (Dp), true diffusion coefficient (Dt), perfusion fraction (f), diffusion coefficient (D), fractional order parameter (β), and a microstructural quantity (μ) were calculated. The diagnostic efficacy of various diffusion parameters for predicting CK19 expression of HCC was compared.Results: ADC, DDC, Dt, Dp, AD, and D were significantly lower in CK19-positive HCCs than in CK19-negative HCCs (P ≤ 0.05). β was significantly higher in CK19-positive group (P = 0.001). AD (AUC = 0.845) had the greatest AUC values in differentiating CK19-positive and CK19-negative HCC with individual parameters. The combination of β, AD, and Dp generated the highest area under the ROC curve (AUC = 0.881) compared with individual parameters.Conclusions: ADC, DDC, Dt, Dp, D, and β may serve as noninvasive and quantitative imaging markers and holds great potential in accurately accessing CK19 status of HCC. More importantly, the combination of different diffusion parameters yielded better diagnostic performance. [ABSTRACT FROM AUTHOR]