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Background: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance., Purpose: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients., Materials and Methods: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation., Results: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948)., Conclusion: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits., (© 2024. Italian Society of Medical Radiology.)

Purpose: Characterizing the composition of hepatocellular carcinoma (HCC) and peritumoral micro-environment may provide sensitive biomarkers. We aimed to predict the recurrence-free survival (RFS) of HCC (≤ 5 cm) with habitat imaging of HCC and its peritumoral micro-environment., Material and Methods: A total of 264 patients with HCC were included. Taking advantage of the enhancement ratio at the arterial and hepatobiliary phase of contrast-enhanced MRI, all HCCs and their peritumoral tissue of 3 mm and 4 mm were encoded with different habitats. Besides, the quantitative fraction of each habitat of HCC and peritumoral tissue were calculated. Univariable and multivariable Cox regression analysis was performed to select the prognostic factors. The nomogram-based predictor was established. Kaplan-Meier analysis was conducted to stratify the recurrence risk. Fivefold cross-validation was performed to determine the predictive performance with the concordance index (C-Index). Decision curve analysis was used to evaluate the net benefit., Results: Qualitatively, the spatial distribution of the habitats varied for different survival outcomes. Quantitatively, the fraction of habitat 3 in peritumoral tissue of 4 mm (f 3 -P 4 ) was selected as independent risk factors (OR = 89.2, 95% CI = 14.5-549.2, p < 0.001) together with other two clinical variables. Integrating both clinical variables and f 3 -P 4 , a nomogram was constructed and showed high predictive efficacy (C-Index: 0.735, 95% CI 0.617-0.854) and extra net benefit according to the decision curve. Furthermore, patients with low f 3 -P 4 or risk score given by nomogram have far longer RFS than those with high f 3 -P 4 or risk score (stratification by f 3 -P 4 : 131.9 vs 55.0 months and stratification by risk score:131.9 vs 34.1 months)., Conclusion: Habitat imaging of HCC and peritumoral microenvironment can be used for effectively and non-invasively estimating the RFS, which holds potential in guiding clinical management and decision making., (© 2023. Italian Society of Medical Radiology.)

Background: Chest computed tomography (CT) has shown tremendous clinical potential for screening, diagnosis, and surveillance of COVID-19. However, safety concerns are warranted due to repeated exposure of X-rays over a short period of time. Recent advances in MRI suggested that ultrashort echo time MRI (UTE-MRI) was valuable for pulmonary applications., Purpose: To evaluate the effectiveness of UTE-MRI for assessing COVID-19., Study Type: Prospective., Population: In all, 23 patients with COVID-19 and with an average interval of 2.81 days between hospital admission and image examination., Field Strength/sequence: 3T; Respiratory-gated three-dimensional radial UTE pulse sequence., Assessment: Image quality score. Patient- and lesion-based interobserver and intermethod agreement for identifying the representative image findings of COVID-19., Statistical Tests: Wilcoxon-rank sum test, Kendall's coefficient of concordance (Kendall's W), intraclass coefficients (ICCs), and weighted kappa statistics., Results: There was no significant difference between the image quality of CT and UTE-MRI (CT vs. UTE-MRI: 4.3 ± 0.4 vs. 4.0 ± 0.5, P = 0.09). Moreover, both patient- and lesion-based interobserver agreement of CT and UTE-MRI for evaluating the image signs of COVID-19 were determined as excellent (ICC: 0.939-1.000, P < 0.05; Kendall's W: 0.894-1.000, P < 0.05.). In addition, the intermethod agreement of two image modalities for assessing the representative findings of COVID-19 including affected lobes, total severity score, ground glass opacities (GGO), consolidation, GGO with consolidation, the number of crazy paving pattern, and linear opacities, as well as pseudocavity were all determined as substantial or excellent (kappa: 0.649-1.000, P < 0.05; ICC: 0.913-1.000, P < 0.05)., Data Conclusion: Pulmonary MRI with UTE is valuable for assessing the representative image findings of COVID-19 with a high concordance to CT., Evidence Level: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:397-406., (© 2020 International Society for Magnetic Resonance in Medicine.)

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]

Background: Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoiding missed diagnosis and guiding treatment. Purpose: To investigate the clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) in evaluating patients with involuntary head motion due to brain diseases, compared with the conventional T2-FLAIR with parallel imaging (PI-FLAIR). Material and Methods: A total of 33 uncooperative patients with brain disease were prospectively enrolled. Two readers independently reviewed images acquired with ACS-SS-FLAIR and PI-FLAIR at a 3.0-T MR scanner. In the aspects of qualitative evaluation of image quality, overall image quality and lesion conspicuity of ACS-SS-FLAIR and PI-FLAIR were assessed and then statistically compared by paired Wilcoxon rank-sum test. For quantitative evaluation, the relative contrast of lesion-to-cerebral parenchyma were calculated and compared. Results: Overall image quality scores of ACS-SS-FLAIR evaluated by two readers were 2.94 ± 0.24 and 2.91 ± 0.29, respectively, both of which were significantly higher than that of PI-FLAIR, respectively (P < 0.001 and <0.001). Lesion conspicuity scores of were 2.74 ± 0.47 and 2.79 ± 0.44, both of which were significantly higher than that of PI-FLAIR, respectively (P < 0.001 and <0.001). In the quantitative evaluation for image quality, the relative contrast of lesion-to-cerebral parenchyma was 0.34 ± 0.09 in the ACS-SS-FLAIR sequence, significantly larger than that in the PI-FLAIR sequence (P = 0.001). Conclusion: The ACS-SS-FLAIR sequence is clinically feasible in the MRI workup of those patients with involuntary head motion due to brain diseases, showing shorter image acquisition time and better image quality compared with conventional PI-FLAIR. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To investigate the impact of preoperative gadoxetate disodium (EOB) MRI-based radiomics on predicting glypican 3 (GPC3)-positive expression and the relevant recurrence-free survival (RFS) of HCC ≤ 5 cm.Materials and Methods: Between January 2014 and October 2018, 259 patients with solitary HCC ≤ 5 cm who underwent hepatectomy and preoperative EOB-MRI were retrieved. Multivariate logistic regression was implemented to identify independent predictors for GPC3. By combining five feature selection strategies and three classifiers, 15 GPC3-oriented radiomics models could be constructed, the best of which with independent clinicoradiologic predictors was integrated into the comprehensive nomogram.Results: GPC3 was an independent risk factor of postoperative recrudescence for HCC. Alpha-fetoprotein >20 ng/mL, homogenous T2 signal and hypointensity on hepatobiliary phase were independently related to GPC3-positive expression in the clinicoradiologic model. With 10 features selected by support vector machines-recursive feature elimination, logistic regression-based classifier achieved the best performance among 15 radiomics models. After five-fold cross-validation, our comprehensive nomogram acquired better average area under receiver operating characteristic curves (training and validation cohorts: 0.931 vs. 0.943) than the clinicoradiologic algorithm (0.738 vs. 0.739) and the optimal radiomics model (0.943 vs. 0.931). Net reclassification indexes further demonstrated the superiority of GPC3 nomogram over clinicoradiologic and radiomics algorithms (46.54%, p < 0.001; 7.84%, p = 0.207). Meanwhile, higher radiomics score significantly shortened the median RFS (from >77.9 to 48.2 months, p = 0.044), which was analogue to that of the histological GPC3-positive phenotype (from >73.9 to 43.2 months, p < 0.001).Conclusions: Preoperative EOB-MRI radiomics-based nomogram satisfactorily distinguished GPC3 status and outcomes of solitary HCC ≤ 5 cm. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Accurately staging liver fibrosis is of great clinical significance. We aimed to evaluate the clinical potential of the non-Gaussian fractional order calculus (FROC) diffusion model in staging liver fibrosis.Materials and Methods: A total of 82 patients with chronic hepatitis B (CHB) were included in this prospective study. Diffusion weighted imaging (DWI)-derived parameters including the diffusion coefficient (D), fractional order parameter (β) and microstructural quantity (μ) sourced from FROC-DWI, and apparent diffusion coefficient (ADC) derived from mono-exponential DWI, as well as the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) were calculated. Their correlations with fibrosis stages and the diagnostic efficacy in predicting liver fibrosis were assessed and compared.Results: D (r = -0.667), β (r = -0.671), μ (r = -0.481), and ADC (r = -0.665) displayed significant correlations with fibrosis stages (p < 0.001). D, β and ADC (p < 0.01) were independently associated with fibrosis; and compared to inflammatory activity, fibrosis was the independent factor significantly correlated with D, β and ADC (p < 0.001). There were no significant differences between the area under curves of D, β, μ or their combinations and ADC for predicting different fibrosis stages (p > 0.05). The diagnostic performance of the combined index with four diffusion metrics was better than D, β, μ or ADC used alone (p < 0.05) as well as APRI or FIB-4 (p < 0.01) in fibrosis staging.Conclusion: FROC-DWI was valuable in staging liver fibrosis in patients with CHB, but there were no significant differences between the FROC-DWI parameters and the classical ADC. However, the combined DWI-derived index including D, β, μ and ADC offered the best diagnostic efficacy and may serve as a reliable tool for fibrosis evaluation, superior to APRI and FIB-4. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Combined immune and anti-angiogenic treatment has shown promising results for unresectable hepatocellular carcinoma (HCC), but with a high risk of early progression. In this study, we aimed to investigate whether pre-treatment magnetic resonance imaging (MRI) features and MRI-based nomogram could predict the risk of disease progression of unresectable HCC after first-line lenvatinib/anti-PD-1 antibody therapy.Materials and Methods: Thirty-seven HCC participants with qualified pre-treatment contrast-enhanced MRI were enrolled. All patients received combined lenvatinib and anti-PD-1 antibody treatment. Progression free survival rate was analyzed using the Kaplan-Meier method. Potential clinical-radiological risk factors for progression were analyzed using the log-rank tests and Cox regression model. The performance of MRI-based nomogram was evaluated based on C-index, calibration, and decision curve analyses.Results: The 6-month and 12-month cumulative progression free survival rates were 59.5% (95% confidence interval (CI), 43.6%-75.4%) and 48.0% (95% CI, 31.7%-64.3%). On multivariate analysis, no or incomplete tumor capsule (hazard ratio (HR) = 15.215 [95% CI 2.707-85.529], p = 0.002), heterogeneous signal on T2-weighted imaging (HR = 28.179 [95% CI 2.437-325.838]; p = 0.008) and arterial contrast-to-noise ratio ≤95.45 (HR = 5.113 [95% CI 1.538-17.00]; p = 0.008) were independent risk factors for disease progression. Satisfactory predictive performance of the nomogram incorporating the three independent imaging features was obtained with a C-index value of 0.880 (95% CI 0.824-0.937), and the combined nomogram had more favorable clinical prediction performance than any single feature.Conclusion: MRI features can be considered effective predictors of disease progression for unresectable HCC with first-line lenvatinib plus anti-PD-1 antibody therapy, and the combined MRI-based nomogram achieved a superior prognostic model, which may help to identify appropriate candidates for the therapy. [ABSTRACT FROM AUTHOR]

Purpose: The three-dimensional (3D) sequence of magnetic resonance imaging (MRI) plays a critical role in the imaging of musculoskeletal joints; however, its long acquisition time limits its clinical application. In such conditions, compressed sensing (CS) is introduced to accelerate MRI in clinical practice. We aimed to investigate the feasibility of an isotropic 3D variable-flip-angle fast spin echo (FSE) sequence with CS technique (CS-MATRIX) compared to conventional 2D sequences in knee imaging. Methods: Images from different sequences of both the accelerated CS-MATRIX and the corresponding conventional acquisitions were prospectively analyzed and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the structures within the knees were measured for quantitative analysis. The subjective image quality and diagnostic agreement were compared between CS-MATRIX and conventional 2D sequences. Quantitative and subjective image quality scores were statistically analyzed with the paired t-test and Wilcoxon signed-rank test, respectively. Diagnostic agreements of knee substructure were assessed using Cohen's weighted kappa statistic. Results: For quantitative analysis, images from the CS-MATRIX sequence showed a significantly higher SNR than T2-fs 2D sequences for visualizing cartilage, menisci, and ligaments, as well as a higher SNR than proton density (pd) 2D sequences for visualizing menisci and ligaments. There was no significant difference between CS-MATRIX and 2D T2-fs sequences in subjective image quality assessment. The diagnostic agreement was rated as moderate to very good between CS-MATRIX and 2D sequences. Conclusion: This study demonstrates the feasibility and clinical potential of the CS-MATRIX sequence technique for detecting knee lesions The CS-MATRIX sequence allows for faster knee imaging than conventional 2D sequences, yielding similar image quality to 2D sequences. [ABSTRACT FROM AUTHOR]

To assess the value of pre-treatment MRI in predicting treatment response to combined targeted immunotherapy in advanced hepatocellular carcinoma (HCC). Totally 35 HCC participants who underwent pre-treatment contrast-enhanced MRI and received combined tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody treatment were enrolled. Univariable and multivariable logistic regression analyses were carried out for comparing clinical and MRI characteristics between patients with therapeutic response and those without. A predictive model based on MRI data and the corresponding nomogram were developed using data generated by multivariate analysis, and the diagnostic performance was evaluated. A cutoff for the combined index was measured by receiver operating characteristic curve analysis, and progression-free survival (PFS) rates were compared between cases with high and low combined index values. Fifteen (42.86%) cases achieved overall response during treatment. Multivariable analysis revealed that homogeneous signal (odds ratio [OR] = 13.51, P = 0.010) and no arterial peritumoral enhancement (APE; OR = 10.29, P = 0.024) independently predicted treatment response. The combined model including both significant MRI parameters showed a satisfactory predictive performance with the largest area under the curve of 0.837 (95%CI 0.673–0.939), and both sensitivity and specificity of 80.0%. HCCs with high-combined index had higher PFS rate compared with those showing a low value (P = 0.034). The combination of pre-treatment MRI features of homogeneous signal and no APE could be used for predicting treatment response to combined targeted immunotherapy in advanced HCC. • Pretreatment MRI can predict the response of targeted-immunotherapy in advanced HCC. • Homogeneous signal and no APE are related to therapeutic response. • The MRI-based combined model may help to identify appropriate therapeutic candidates. [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]