Liver fibrosis assessment with multiphasic dual-energy CT: diagnostic performance of iodine uptake parameters

Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma

We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR]  7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression.

Non-contrast spectral CT vs chemical-shift MRI in discriminating lipid-poor adrenal lesions

OBJECTIVES

To compare the diagnostic performance of conventional non-contrast CT, dual-energy spectral CT, and chemical-shift MRI (CS-MRI) in discriminating lipid-poor adenomas (> 10-HU on non-contrast CT) from non-adenomas.

METHODS

A total of 110 patients (69 men; 41 women; mean age 66.5 ± 13.4 years) with 80 lipid-poor adenomas and 30 non-adenomas who underwent non-contrast dual-layer spectral CT and CS-MRI were retrospectively identified. For each lesion, non-contrast attenuation on conventional 120-kVp images, ΔHU-index ([attenuation difference between virtual monoenergetic 140-keV and 40-keV images]/conventional attenuation × 100), and signal intensity index (SI-index) were quantified. Each parameter was compared between adenomas and non-adenomas using the Mann-Whitney U-test. The area under the receiver operating characteristic curve (AUC) and sensitivity to achieve > 95% specificity for adenoma diagnosis were determined.

RESULTS

Conventional non-contrast attenuation was lower in adenomas than in non-adenomas (22.4 ± 8.6 HU vs 32.8 ± 48.5 HU), whereas ΔHU-index (148.0 ± 103.2 vs 19.4 ± 25.8) and SI-index (41.6 ± 19.6 vs 4.2 ± 10.2) were higher in adenomas (all, p < 0.001). ΔHU-index showed superior performance to conventional non-contrast attenuation (AUC: 0.919 [95% CI: 0.852-0.963] vs 0.791 [95% CI: 0.703-0.863]; sensitivity: 75.0% [60/80] vs 27.5% [22/80], both p < 0.001), and near equivalent to SI-index (AUC: 0.952 [95% CI: 0.894-0.984], sensitivity 85.0% [68/80], both p > 0.05). Both the ΔHU-index and SI-index provided a sensitivity of 96.0% (48/50) for hypoattenuating adenomas (≤ 25 HU). For hyperattenuating (> 25 HU) adenomas, SI-index showed higher sensitivity than ΔHU-index (66.7% [20/30] vs 40.0% [12/30], p = 0.022).

CONCLUSIONS

Non-contrast spectral CT and CS-MRI outperformed conventional non-contrast CT in distinguishing lipid-poor adenomas from non-adenomas. While CS-MRI demonstrated superior sensitivity for adenomas measuring > 25 HU, non-contrast spectral CT provided high discriminative values for adenomas measuring ≤ 25 HU.

CLINICAL RELEVANCE STATEMENT

Spectral attenuation analysis improves the diagnostic performance of non-contrast CT in discriminating lipid-poor adrenal adenomas, potentially serving as an alternative to CS-MRI and obviating the necessity for additional diagnostic workup in indeterminate adrenal incidentalomas, particularly for lesions measuring ≤ 25 HU.

KEY POINTS

Incidental adrenal lesion detection has increased as abdominal CT use has become more frequent. Non-contrast spectral CT and CS-MRI differentiated lipid-poor adenomas from non-adenomas better than conventional non-contrast CT. For lesions measuring ≤ 25 HU, spectral CT may obviate the need for additional evaluation.

Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers

A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.

Suboptimal performance of LI-RADS v2018 on gadoxetic acid-enhanced MRI for detecting hepatocellular carcinoma in liver transplant candidates

OBJECTIVES

To evaluate the diagnostic performance for hepatocellular carcinoma (HCC) detection of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 on gadoxetic acid-enhanced MRI, comparing liver transplant candidates (LT group) with patients who underwent surgical resection (SR group), and to determine significant clinical factors for diagnostic performance of LI-RADS v2018.

METHODS

Patients who underwent gadoxetic acid-enhanced MRI and subsequent SR or LT for HCC were retrospectively included between January 2019 and December 2020. The sensitivity and specificity of LI-RADS LR-5 for HCC were compared between the two groups using generalized estimating equations. The accuracy of patient allocation according to the Milan criteria was calculated for the LT group. Univariable and multivariable logistic regression analyses were performed to determine significant clinical factors associated with the sensitivity of LI-RADS.

RESULTS

Of the 281 patients, 237 were assigned to the SR group, and 44 were assigned to the LT group. The LT group showed significantly lower per-patient (48.5% vs. 79.6%, p < .001) and per-lesion sensitivity (31.0% vs. 75.9%, p < .001) than the SR group, whereas no significant difference in both per-patient (100.0% vs. 91.7%, p > .99) and per-lesion specificities (100.0% vs. 94.1%, p > .99). The accuracy of patient allocation was 50.0%. Sensitivity was significantly lower in patients with a smaller lesion size (p < .001), a larger lesion number (p = .002), and a higher Child-Pugh score (p = .009).

CONCLUSION

LI-RADS v2018 on gadoxetic acid-enhanced MRI might be insufficient in liver transplant candidates and other diagnostic imaging tests should be considered in patients with these significant clinical factors.

CLINICAL RELEVANCE STATEMENT

In liver transplant candidates with a smaller lesion size, a larger lesion number, and a higher Child-Pugh score, imaging tests other than gadoxetic acid-enhanced MRI may be clinically useful to determine the transplant eligibility.

KEY POINTS

• The sensitivity of the Liver Imaging Reporting and Data System (LI-RADS) was lower in liver transplant candidates than in those who underwent surgical resection. • With the use of gadoxetic acid-enhanced MRI, the accuracy of patient allocation for liver transplantation on the basis of the Milan criteria was suboptimal. • The sensitivity of LI-RADS v2018 was significantly associated with lesion size, lesion number, and Child-Pugh classification.

Prediction of pancreatic fibrosis by dual-energy CT-derived extracellular volume fraction: Comparison with MRI

OBJECTIVES

To investigate the correlation between dual-energy CT (DECT) and MRI measurements of the extracellular volume fraction (ECV) and to assess the accuracy of both methods in predicting pancreatic fibrosis (PF).

METHODS

We retrospectively analyzed 43 patients who underwent pancreatectomy and preoperative pancreatic DECT and MRI between November 2018 and May 2022. The ECV was calculated using the T1 relaxation time (for MR-ECV) or absolute enhancement (for DECT-ECV) at equilibrium phase (180 s after contrast injection in our study). Pearson coefficient and Bland-Altman analysis were used to compare the correlation between the two ECVs, Spearman correlations were used to investigate the association between imaging parameters and PF, Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the ECVs for advanced fibrosis (F2-F3), and multivariate logistic regression analysis was used to examine the relationship between PF and imaging parameters.

RESULTS

There was a strong correlation between DECT- and MR-derived ECVs (r = 0.948; p < 0.001). The two ECVs were positively correlated with PF (DECT: r = 0.647, p < 0.001; MR: r = 0.614, p < 0.001), and the mean values were 0.34 ± 0.08 (range: 0.22-0.62) and 0.35 ± 0.09 (range: 0.24-0.66), respectively. The area under the operating characteristic curve (AUC) for subjects with advanced fibrosis diagnosed by ECV was 0.86 for DECT-ECV and 0.87 for MR-ECV. Multivariate logistic regression analysis showed that the DECT-ECV was an independent predictor of PF.

CONCLUSIONS

The ECV could be an effective predictor of histological fibrosis, and DECT is equivalent to MRI for characterizing pancreatic ECV changes.

Liver fibrosis estimated using extracellular volume fraction obtained from dual-energy CT as a risk factor for hepatocellular carcinoma after sustained virologic response: A preliminary case-control study

PURPOSE

To assess hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) through clinical data analyses, including evaluation of liver fibrosis using the extracellular volume fraction (ECV) obtained from dual-energy computed tomography (DECT).

METHODS

Ninety-two patients (52 men and 40 women; mean age, 69.9 years) with hepatitis C virus infection after SVR underwent DECT of the liver (3-minute equilibrium-phase images) between January 2020 and March 2022. The ECV was calculated by measuring iodine density; fibrous markers, including ECV, fibrosis-4 index, aspartate aminotransferase to platelet ratio index, and platelet count, were statistically analyzed (p < 0.05). The risk factors associated with HCC were analyzed using univariate and multivariate logistic regression analyses.

RESULTS

The ECV (26.1 ± 4.6 %) in patients with HCC (n,21) was significantly larger than the ECV (20.7 ± 3.3 %) in patients without HCC (n = 71) (p < 0.001). The cutoff value for the ECV was 24.3 %. The area under the operating characteristic curve of the ECV was 0.857, which was higher than that of the serum fibrosis markers. Older age, SVR achieved with interferon, alpha-fetoprotein level (>5 ng/mL), advanced fibrosis before treatment (>F3), and ECV were associated with HCC according to the univariate analysis. Multivariate analyses showed that ECV was the only factor independently associated with HCC (odds ratio 0.619, 95 % confidence interval 0.482-0.795, p < 0.001).

CONCLUSION

Liver fibrosis estimated using ECV can be a predictive marker in patients with HCC after SVR.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Dual-energy CT quantification of extracellular liver volume predicts short-term disease progression in patients with hepatitis B liver cirrhosis-acute decompensation

BACKGROUND

Liver cirrhosis-acute decompensation (LC-AD) has rapid short-term disease progression and difficult early risk stratification. The purpose is to develop and validate a model based on dual-energy CT quantification of extracellular liver volume (ECV_IC-liver) for predicting the occurrence of acute-on-chronic liver failure (ACLF) within 90 days in patients with hepatitis B (HBV) LC-AD.

METHODS

The retrospective study included patients with HBV LC-AD who underwent dual-energy CT scans of the liver from January 2018 to March 2022 and were randomized to training group (215 patients) and validation group (92 patients). The primary outcome was the need for readmission within 90 days due to ACLF. Based on the training group data, independent risk factors for disease progression in clinical and dual-energy CT parameters were identified and modeled by logistic regression analysis. Based on the training and validation groups data, receiver operating characteristic (ROC) curves, calibration curves, and decision analysis curves (DCA) were used to verify the discrimination, calibration, and clinical validity of the nomogram.

RESULTS

Chronic liver failure consortium-acute decompensation score (CLIF-C ADs) (p = 0.008) and ECV_IC-liver (p < 0.001) were independent risk factors for ACLF within 90 days. The AUC of the model combined ECV_IC-liver and CLIF-C ADs were 0.893 and 0.838 in the training and validation groups, respectively. The calibration curves show good agreement between predicted and actual risks. The DCA indicates that the model has good clinical application.

CONCLUSION

The model combined ECV_IC-liver and CLIF-C ADs can early predict the occurrence of ACLF within 90 days in HBV LC-AD patients.

Visualization Score of Gadoxetic Acid-Enhanced Magnetic Resonance Imaging: The Effect on the Diagnostic Accuracy for Hepatocellular Carcinoma

BACKGROUND

The visualization score of hepatobiliary-phase (HBP) images has been introduced as an image quality index for gadoxetic acid-enhanced MRI. It may be associated with hepatic function and could have an implication on the diagnostic accuracy for hepatocellular carcinoma (HCC).

PURPOSE

To investigate the association between the visualization score of gadoxetic acid-enhanced MRI and clinical factors and to evaluate its effect on the diagnostic accuracy for HCC ≤ 3.0 cm.

STUDY TYPE

Retrospective.

POPULATION

A total of 493 focal lesions from 397 patients.

FIELD STRENGTH/SEQUENCE

A 5-T or 3.0 -T with pre/postcontrast T1-weighted 3D gradient echo sequence, and T2-weighted fast spin-echo sequence ASSESSMENT: Child-Pugh classification and albumin-bilirubin (ALBI) score were assessed. Three readers evaluated the visualization score of each MRI examination (A, no or minimal; B, moderate; and C, severe limitations), and major features (arterial-phase hyperenhancement, washout, enhancing capsule, threshold growth) and ancillary features of each focal lesion.

STATISTICAL TESTS

Univariable and multivariable logistic regression analyses were performed to determine significant clinical factors associated with a suboptimal visualization score (B or C). Generalized estimating equations were used to compare the sensitivity and specificity for diagnosing HCC between the two group (visualization score A vs. B or C). A P value < 0.05 was considered statistically significant.

RESULTS

Of the 397 MRI examinations, the incidence of suboptimal visualization score was 13%. A suboptimal visualization score was significantly associated with Child-Pugh classification B or C (adjusted odds ratio [OR] = 15.2) and ALBI grade 2 or 3 (OR = 4.7). Compared with the visualization score A group, the suboptimal visualization score group showed significantly lower sensitivity (56.8% vs. 75.2%) and less frequent washout in HCC (62.2% vs. 84.0%).

DATA CONCLUSION

The visualization score on gadoxetic acid-enhanced MRI can be an important image quality index and the diagnostic accuracy for HCC ≤ 3.0 cm may not be sufficient in the suboptimal visualization score group.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 3.

Noninvasive Liver Fibrosis Staging: Comparison of MR Elastography with Extracellular Volume Fraction Analysis Using Contrast-Enhanced CT

Purpose: To compare the accuracy of liver fibrosis staging with MR elastography and of staging with extracellular volume fraction (fECV) analysis using contrast-enhanced CT. Methods: This retrospective study included 60 patients who underwent both MR elastography and contrast-enhanced CT before liver surgery between October 2013 and July 2020. Two radiologists independently measured liver stiffness of MR elastography and fECV of CT images. Accuracy for liver fibrosis staging was assessed using receiver operating characteristic (ROC) analysis. Correlations between liver stiffness or fECV and liver fibrosis were also evaluated by means of the Spearman rank correlation coefficient. Results: The areas under the ROC curves for MR elastography for each stage differentiation of ≥F1 (0.85, 0.82 for the two radiologists), ≥F2 (0.88, 0.89), ≥F3 (0.87, 0.86), and F4 (0.84, 0.83) were greater than those for fECV analysis with CT (0.64, p = 0.06, 0.69, p = 0.2; 0.62, p < 0.005, 0.63, p < 0.005; 0.62, p < 0.005, 0.62, p < 0.01; and 0.70, p = 0.08, 0.71, p = 0.2, respectively). The correlation coefficients between liver stiffness and liver fibrosis in A0 (0.67, 0.69 for the two radiologists), A1 (0.64, 0.66) and A2 group (0.58, 0.51) were significantly higher than those between fECV and liver fibrosis (0.28, 0.30; 0.27, 0.31; and 0.23, 0.07; p < 0.05 for all comparisons). Conclusion: MR elastography allows for more accurate liver fibrosis staging compared with fECV analysis with CT. In addition, MR elastography may be less affected than fECV analysis by the inflammatory condition.

Quantitative dual-energy CT techniques in the abdomen

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

Extracellular Volume Fraction Derived From Dual-Layer Spectral Detector Computed Tomography for Diagnosing Cervical Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer: A Preliminary Study

Objectives

The current study evaluates the performance of dual-energy computed tomography (DECT) derived extracellular volume (ECV) fraction based on dual-layer spectral detector CT for diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid cancer (PTC) and compares it with the value of ECV derived from conventional single-energy CT (SECT).

Methods

One hundred and fifty-seven cervical LNs (81 non-metastatic and 76 metastatic) were recruited. Among them, 59 cervical LNs (27 non-metastatic and 32 metastatic) were affected by cervical root artifact on the contrast-enhanced CT images in the arterial phase. Both the SECT-derived ECV fraction (ECV_S) and the DECT-derived ECV fraction (ECV_D) were calculated. A Pearson correlation coefficient and a Bland–Altman analysis were performed to evaluate the correlations between ECV_D and ECV_S. Receiver operator characteristic curves analysis and the Delong method were performed to assess and compare the diagnostic performance.

Results

ECV_D correlated significantly with ECV_S (r = 0.925; p <0.001) with a small bias (−0.6). Metastatic LNs showed significantly higher ECV_D (42.41% vs 22.53%, p <0.001) and ECV_S (39.18% vs 25.45%, p <0.001) than non-metastatic LNs. By setting an ECV_D of 36.45% as the cut-off value, optimal diagnostic performance could be achieved (AUC = 0.813), which was comparable with that of ECV_S (cut-off value = 34.99%; AUC = 0.793) (p = 0.265). For LNs affected by cervical root artifact, ECV_D also showed favorable efficiency (AUC = 0.756), which was also comparable with that of ECV_S (AUC = 0.716) (p = 0.244).

Conclusions

ECV_D showed a significant correlation with ECV_S. Compared with ECV_S, ECV_D showed comparable performance in diagnosing metastatic cervical LNs in PTC patients, even though the LNs were affected by cervical root artifacts on arterial phase CT.

Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: A clinical and phantom study

PURPOSE

This clinical and phantom study aimed to evaluate the impact of deep learning-based reconstruction (DLR) on image quality and its radiation dose optimization capability for multiphase hepatic CT relative to hybrid iterative reconstruction (HIR).

METHODS

Task-based image quality was assessed with a physical evaluation phantom; the high- and low-contrast detectability of HIR and DLR images were computed from the noise power spectrum and task-based transfer function at five different size-specific dose estimate (SSDE) values in the range 5.3 to 18.0-mGy. For the clinical study, images of 73 patients who had undergone multiphase hepatic CT under both standard-dose (STD) and lower-dose (LD) examination protocols within a time interval of about four-months on average, were retrospectively examined. STD images were reconstructed with HIR, while LD with HIR (LD-HIR) and DLR (LD-DLR). SSDE, quantitative image noise, and contrast-to-noise ratio (CNR) were compared between protocols. The noise magnitude, noise texture, streak artifact, image sharpness, interface smoothness, and overall image quality were subjectively rated by two independent radiologists.

RESULTS

In phantom study, the high- and low-contrast detectability of DLR images obtained at 5.3-mGy and 7.3-mGy, respectively, were slightly higher than those obtained with HIR at the STD protocol dose (18.0-mGy). In clinical study, LD-DLR yielded lower image noise, higher CNR, and higher subjective scores for all evaluation criteria than STD (all, p ≤ 0.05), despite having 52.8% lower SSDE (8.0 ± 2.5 vs. 16.8 ± 3.4-mGy).

CONCLUSIONS

DLR improved the subjective and objective image quality of multiphase hepatic CT compared with HIR techniques, even at approximately half the radiation dose.