Multiphasic Contrast-enhanced MRI Research Articles

Delta-radiomics Analysis Based on Multi-phase Contrast-enhanced MRI to Predict Early Recurrence in Hepatocellular Carcinoma After Percutaneous Thermal Ablation

Rationale and ObjectivesIt is critical to predict early recurrence (ER) after percutaneous thermal ablation (PTA) for hepatocellular carcinoma (HCC). We aimed to develop and validate a delta-radiomics nomogram based on multi-phase contrast-enhanced magnetic resonance imaging (MRI) to preoperatively predict ER of HCC after PTA. Materials and MethodsWe retrospectively enrolled 164 patients with HCC and divided them into training, temporal validation, and other-scanner validation cohorts (n=110, 29, and 25, respectively). The volumes of interest of the intratumoral and/or peritumoral regions were delineated on preoperative multi-phase MR images. Original radiomics features were extracted from each phase, and delta-radiomics features were calculated. Logistic regression was used to train the corresponding radiomics models. The clinical and radiological characteristics were evaluated and combined to establish a clinical-radiological model. A fusion model comprising the best radiomics scores and clinical-radiological risk factors was constructed and presented as a nomogram. The performance of each model was evaluated and recurrence-free survival (RFS) was assessed. ResultsChild-Pugh grade B, high-risk tumor location, and an incomplete/absent tumor capsule were independent predictors of ER. The optimal radiomics model comprised 12 delta-radiomics features with areas under the curve (AUCs) of 0.834, 0.795, and 0.769 in the training, temporal validation, and other-scanner validation cohorts, respectively. The nomogram showed the best predictive performance with AUCs as 0.893, 0.854, and 0.827 in the three datasets. There was a statistically significant difference in RFS between the risk groups calculated using the delta-radiomics model and nomogram. ConclusionsThe nomogram combined with the delta-radiomic score and clinical-radiological risk factors could non-invasively predict ER of HCC after PTA.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.

Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective. To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI). This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index. A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts). Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice. Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards. • Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.

Multi-average high-acceleration modified volumetric interpolated breath-hold examination (VIBE) for free-breathing multiphase contrast-enhanced liver MRI: a comparative study with breath-hold VIBE.

Breath-hold volumetric interpolated breath-hold examination (BH-VIBE) of multiphase contrast-enhanced liver magnetic resonance imaging (MPCE-LMRI) requires good cooperative individuals to comply with multiple breath-holds. To develop a free-breathing modified VIBE (FB-mVIBE) as a substitute of BH-VIBE in MPCE-LMRI. We modified VIBE with a high acceleration factor (2 × 2) and four averages to produce the mVIBE scan. A total of 90 individuals (40 men; mean age = 54.6 ± 10.0 years) who had received MPCE-LMRI as part of a voluntary health check-up for oncology survey were enrolled. Each participant was scanned in four phases (pre-contrast, arterial phase, venous phase, and delay phase), and each phase had two sequential scans. To encounter the timing effect of contrast enhancement, three scan orders were designed: BH-VIBE and FB-mVIBE (group A, n = 30); BH-VIBE and FB-VIBE (group B, n = 30); and FB-mVIBE and BH-VIBE (group C, n = 30). The comparisons included the objective measurements and 25 visual-score by two abdominal radiologists independently. Consistency between raters was observed for all three sequences (intraclass correlation coefficient [ICC] = 0.741-0.829). For rater 1, the mean scores of FB-mVIBE (23.67 ± 1.32) were equal to those of BH-VIBE (23.83 ± 1.98) in groups C and B (P = 0.852). The mean scores of FB-mVIBE (22.07 ± 3.02), but significantly higher than those of FB-VIBE (14.7 ± 3.41) in groups A and B (P <0.001). Similar scores were found for rater 2. The objective measurement of FB-mVIBE were equal to or higher than BH-VIBE and markedly superior to FB-VIBE. FB-mVIBE is a practical alternative to BH-VIBE for individuals who cannot cooperate with multiple breath-holds for MPCE-LMRI.

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma.

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors. Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram. Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment.

Optimizing Imaging of Pediatric Liver Lesions: Guidelines from the Pediatric LI-RADS Working Group.

A differential diagnosis based on a patient's age, clinical presentation, and serum α-fetoprotein level will help guide the initial imaging workup in children with a liver lesion. Children vary significantly in size, the ability to stay still, and the ability to breath hold for imaging examinations. Choosing and tailoring imaging techniques and protocols for each indication and age group is important for optimal care with minimal invasiveness. The need for sedation or anesthesia can be obviated by using techniques like feed and bundle, distraction, contrast-enhanced US, and motion-insensitive sequences for MRI. US is often the first imaging modality used in children with a suspected abdominal mass. Once a hepatic lesion is confirmed, multiphasic contrast-enhanced MRI is recommended for most lesions as the next imaging modality allowing full characterization of the lesion and assessment of the liver parenchyma. Contrast-enhanced CT can also be performed for assessment of pediatric focal liver lesions, especially in patients who have a contraindication to MRI. Contrast-enhanced US has shown promise to decrease the need for MRI or CT in some lesions such as hemangioma and focal nodular hyperplasia. Children with a history of malignancy can develop multiple types of hepatic lesions at various stages, including infections during an immunocompromised state, manifesting as focal liver lesions. Based on available limited data in the literature and the collective experiences of the Liver Imaging and Reporting Data System Pediatric Working Group, the authors provide guidelines for the imaging workup of pediatric focal liver lesions with an indication- and age-based approach and discuss the selection and performance of various imaging techniques and modalities. ©RSNA, 2022 See the invited commentary by Chojniak and Boaventura in this issue.

Case 304: Porphyria Cutanea Tarda

In 2017, a 72-year-old woman was seen in the gastroenterology department with a 2-month history of mild and intermittent abdominal pain without other accompanying symptoms. Her medical history was unremarkable, except for a previous visit due to facial photodermatitis 3 years earlier. Diazepam for a sleeping disorder was the only chronic medication recorded. Results of physical examination, blood count, and basic metabolic panels including assessment of renal and liver function were normal; only the ferritin level was slightly elevated (265 ng/mL [595 pmol/L]; normal range, 10-120 ng/mL [22-269 pmol/L]). Abdominal US was performed, followed by multiphasic contrast-enhanced CT and liver MRI due to the findings of the first study. A diagnosis was not established in that moment, and acetaminophen was prescribed for pain relief. As the symptoms continued, laboratory tests and imaging studies were repeated 2 years later, with similar findings and no notable changes.

Case 304.

History In 2017, a 72-year-old woman was seen in the gastroenterology department with a 2-month history of mild and intermittent abdominal pain without other accompanying symptoms. Her medical history was unremarkable, except for a previous visit due to facial photodermatitis 3 years earlier. Diazepam for a sleeping disorder was the only chronic medication recorded. Results of physical examination, blood count, and basic metabolic panels including assessment of renal and liver function were normal; only the ferritin level was slightly elevated (265 ng/mL [595 pmol/L]; normal range, 10-120 ng/mL [22-269 pmol/L]). Abdominal US was performed, followed by multiphasic contrast-enhanced CT and liver MRI due to the findings of the first study. A diagnosis was not established in that moment, and acetaminophen was prescribed for pain relief. As the symptoms continued, laboratory tests and imaging studies were repeated 2 years later, with similar findings and no notable changes (Figs 1-5).

Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions

PurposeThis study aimed to investigate whether models built from radiomics features based on multiphase contrast-enhanced MRI can identify microscopic pre-hepatocellular carcinoma lesions.MethodsWe retrospectively studied 54 small hepatocellular carcinoma (SHCC, diameter < 2 cm) patients and 70 patients with hepatocellular cysts or haemangiomas from September 2018 to June 2021. For the former, two MRI scans were collected within 12 months of each other; the 2nd scan was used to confirm the diagnosis. The volumes of interest (VOIs), including SHCCs and normal liver tissues, were delineated on the 2nd scans, mapped to the 1st scans via image registration, and enrolled into the SHCC and internal-control cohorts, respectively, while those of normal liver tissues from patients with hepatocellular cysts or haemangioma were enrolled in the external-control cohort. We extracted 1132 radiomics features from each VOI and analysed their discriminability between the SHCC and internal-control cohorts for intra-group classification and the SHCC and external-control cohorts for inter-group classification. Five radial basis-function, kernel-based support vector machine (SVM) models (four corresponding single-phase models and one integrated from the four-phase MR images) were established.ResultsAmong the 124 subjects, the multiphase models yielded better performance on the testing set for intra-group and inter-group classification, with areas under the receiver operating characteristic curves of 0.93 (95% CI, 0.85–1.00) and 0.97 (95% CI, 0.92–1.00), accuracies of 86.67% and 94.12%, sensitivities of 87.50% and 94.12%, and specificities of 85.71% and 94.12%, respectively.ConclusionThe combined multiphase MRI-based radiomics feature model revealed microscopic pre-hepatocellular carcinoma lesions.

Ultrasound (US) LI-RADS: Outcomes of Category US-3 Observations.

OBJECTIVE. The purpose of the study is to evaluate the outcomes of ultrasound (US) LI-RADS category US-3 observations detected at US performed for hepatocellular carcinoma (HCC) screening and surveillance on the basis of subsequently performed multi-phase MRI or CT or histopathology. MATERIALS AND METHODS. In this retrospective analysis, 267 patients at high risk for HCC (161 men and 106 women; mean [± SD] age, 58.6 ± 12.2 years) underwent screening liver US between January 2017 and June 2019 and were assigned US-3 observations on a prospective clinical basis using the US LI-RADS algorithm. The results of follow-up imaging studies and/or histopathology were analyzed. RESULTS. Visualization scores assigned at US were A (40.8% [109/267]), B (52.8% [141/267]), and C (6.4% [17/267]). Reasons for US-3 observations included a measurable mass of 1 cm or larger (88.8% [237/267]; mean size, 1.8 ± 1.0 cm; range, 1.0-6.9 cm), an area of parenchymal distortion of 1 cm or greater (7.9% [21/267]; mean size, 1.8 ± 0.9 cm; range, 1.0-4.0 cm), or a new venous thrombus (3.4% [9/267]). Confirmatory testing with multiphase contrast-enhanced MRI or CT or with histopathology was available for 81.6% (218/267) of patients. Causes of US-3 observations included no abnormality at MRI or CT (41.3% [90/218]), a benign lesion (32.6% [71/218]), a LI-RADS category 3 (LR-3) observation at MRI or CT (5.5% [12/218]), a LI-RADS category 4 or 5 (LR-4 or LR-5) observation at MRI or CT or identification of HCC at histopathology (18.8% [41/218]), and an LR-M (denoting probably or definitely malignant but without specific features for HCC) observation at MRI or CT or other malignancy at histopathology (1.8% [4/218]). The PPV of a US-3 observation for probable or definite HCC was 18.8%, and for any malignancy it was 20.6%. CONCLUSION. In the HCC screening population, approximately one in five US-3 observations represents probable or definite HCC at multiphase MRI or CT or HCC at histopathology. These findings support current US LI-RADS guidelines to pursue further evaluation with multiphase cross-sectional imaging for US-3 observations.

A novel computer-aided diagnostic system for accurate detection and grading of liver tumors

Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F_{1} score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of 88%pm 5%, 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

Using LI‐RADS With Contrast‐Enhanced Ultrasound

Using LI‐RADS With Contrast‐Enhanced Ultrasound

Validation of the Korean Liver Cancer Association-National Cancer Center 2018 Criteria for the Noninvasive Diagnosis of Hepatocellular Carcinoma Using Magnetic Resonance Imaging.

This study aimed to assess the validity and diagnostic performance of the imaging criteria of Korean Liver Cancer Association-National Cancer Center (KLCA-NCC) 2018 using magnetic resonance imaging (MRI) in high-risk patients for HCC. This retrospective study included 142 treatment-naïve patients (81 patients who underwent MRI with extracellular contrast agent and 61 who underwent MRI with hepatobiliary agent; 183 lesions including 149 HCCs) with a high risk of HCC who underwent multiphasic contrast-enhanced MRI from January to December 2015. All lesions were categorized according to the KLCA-NCC 2018 imaging diagnostic criteria by two readers, and per-lesion diagnostic performances were compared. According to the KLCA-NCC 2018, none (0%) of the 13 benign category lesions, 11 (44.0%) of 25 indeterminate category lesions, 15 (93.8%) of 16 probable HCC category lesions, and 97 (99.0%) of 98 definite HCC category lesions were ultimately diagnosed as HCCs. The sensitivity and specificity of definite HCC category were 65.1% and 97.1%, respectively, and those of the combination of definite and probable HCC categories were 75.2% and 94.1%, respectively. The sensitivity of the combination of definite and probable HCC categories was significantly higher than that of definite HCC (P<0.001), but the specificity was not significantly lower (P>0.999). The noninvasive imaging diagnosis of KLCA-NCC 2018 on MRI is reliable and useful for diagnosing HCC in high-risk patients. Combining definite and probable HCC categories of KLCA-NCC 2018 improves the sensitivity while maintaining a high specificity.

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning.

Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically. In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR). 73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations. Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS.

Acute kidney injury after multiphase imaging for lesions detected on hepatocellular carcinoma surveillance in patients with cirrhosis

ObjectiveThe risk difference between multiphase multidetector contrast-enhanced CT and MRI for developing acute kidney injury (AKI) has not been previously evaluated in patients with cirrhosis undergoing hepatocellular carcinoma (HCC) surveillance....

Multiphase Liver MRI for Identifying the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma.

Background The recently described "macrotrabecular-massive" (MTM) histologic subtype of hepatocellular carcinoma (HCC) (MTM-HCC) represents an aggressive form of HCC and is associated with poor survival. Purpose To investigate whether preoperative MRI can help identify MTM-HCCs in patients with HCC. Materials and Methods This retrospective study included patients with HCC treated with surgical resection between January 2008 and February 2018 and who underwent preoperative multiphase contrast material-enhanced MRI. Least absolute shrinkage and selection operator (LASSO)-penalized and multivariable logistic regression analyses were performed to identify clinical, biologic, and imaging features associated with the MTM-HCC subtype. Early recurrence (within 2 years) and overall recurrence were evaluated by using Kaplan-Meier analysis. Multivariable Cox regression analysis was performed to determine predictors of early and overall recurrence. Results One hundred fifty-two patients (median age, 64 years; interquartile range, 56-72 years; 126 men) with 152 HCCs were evaluated. Twenty-six of the 152 HCCs (17%) were MTM-HCCs. LASSO-penalized logistic regression analysis identified substantial necrosis, high serum α-fetoprotein (AFP) level (>100 ng/mL), and Barcelona Clinic Liver Cancer (BCLC) stage B or C as independent features associated with MTM-HCCs. At multivariable analysis, substantial necrosis (odds ratio = 32; 95% confidence interval [CI] = 8.9, 114; P < .001), high serum AFP level (odds ratio = 4.4; 95% CI = 1.3, 16; P = .02), and BCLC stage B or C (odds ratio = 4.2; 95% CI = 1.2, 15; P = .03) were independent predictors of MTM-HCC subtype. Substantial necrosis helped identify 65% (17 of 26; 95% CI: 44%, 83%) of MTM-HCCs (sensitivity) with a specificity of 93% (117 of 126; 95% CI: 87%, 97%). In adjusted models, only the presence of satellite nodules was independently associated with both early (hazard ratio = 3.7; 95% CI: 1.5, 9.4; P = .006) and overall (hazard ratio = 3.0; 95% CI: 1.3, 7.2; P = .01) tumor recurrence. Conclusion At multiphase contrast-enhanced MRI, substantial necrosis helped identify macrotrabecular-massive hepatocellular carcinoma subtype with high specificity. © RSNA, 2020.

Diagnostic Accuracy of CEUS LI-RADS for the Characterization of Liver Nodules 20 mm or Smaller in Patients at Risk for Hepatocellular Carcinoma.

Background American College of Radiology contrast agent-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) was developed to improve the accuracy of hepatocellular carcinoma (HCC) diagnosis at contrast agent-enhanced US. However, to the knowledge of the authors, the diagnostic accuracy of the system in characterization of liver nodules 20 mm or smaller has not been fully evaluated. Purpose To evaluate the diagnostic accuracy of CEUS LI-RADS in diagnosing HCC in liver nodules 20 mm or smaller in patients at risk for HCC. Materials and Methods Between January 2015 and February 2018, consecutive patients at risk for HCC presenting with untreated liver nodules 20 mm or less were enrolled in this retrospective double-reader study. Each nodule was categorized according to the CEUS LI-RADS and World Federation for Ultrasound in Medicine and Biology (WFUMB)-European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) criteria. Diagnostic performance of CEUS LI-RADS and WFUMB-EFSUMB characterization was evaluated by using tissue histologic analysis, multiphase contrast-enhanced CT and MRI, and imaging follow-up as reference standard and compared by using McNemar test. Results The study included 175 nodules (mean diameter, 16.1 mm ± 3.4) in 172 patients (mean age, 51.8 years ± 10.6; 136 men). The sensitivity of CEUS LR-5 versus WFUMB-EFSUMB criteria in diagnosing HCC was 73.3% (95% confidence interval [CI]: 63.8%, 81.5%) versus 88.6% (95% CI: 80.9%, 94%), respectively (P < .001). The specificity of CEUS LR-5 versus WFUMB-EFSUMB criteria was 97.1% (95% CI: 90.1%, 99.7%) versus 87.1% (95% CI: 77%, 94%), respectively (P = .02). No malignant lesions were found in CEUS LR-1 and LR-2 categories. Only two nodules (of 41; 5%, both HCC) were malignant in CEUS LR-3 category. The incidences of HCC in CEUS LR-4, LR-5, and LR-M were 48% (11 of 23), 98% (77 of 79), and 75% (15 of 20), respectively. Two of 175 (1.1%) histologic analysis-confirmed intrahepatic cholangiocarcinomas were categorized as CEUS LR-M by CEUS LI-RADS and misdiagnosed as HCC by WFUMB-EFSUMB criteria. Conclusion The contrast-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) algorithm was an effective tool for characterization of small (≤20 mm) liver nodules in patients at risk for hepatocellular carcinoma (HCC). Compared with World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology criteria, CEUS LR-5 demonstrated higher specificity for diagnosing small HCCs with lower sensitivity. Published under a CC BY 4.0 license. See also the editorial by Crocetti in this issue.

Prevalence and clinical significance of discordant LI-RADS® observations on multiphase contrast-enhanced MRI in patients with cirrhosis.

To determine the prevalence and clinical significance of discordant LI-RADS® (Liver Imaging Reporting and Data System) liver observations on multiphase contrast-enhanced (MCE) magnetic resonance imaging (MRI) in patients with cirrhosis. This cross-sectional study included 93 cirrhosis patients who underwent 1.5 or 3 T MCE MRI for evaluation of hepatocellular carcinoma (HCC). Two abdominal radiologists independently reviewed T1-, T2-, diffusion-weighted unenhanced images as well as MCE T1-weighted fat-suppressed images and reported liver observations using LI-RADS®. Concordance were recorded for detection (co-detected by both radiologists or not), size category (< 10; 10-19; ≥ 20mm), and LI-RADS® category assignment as reportable (LR-3/4/5/M) and actionable (LR-4/5/M). The overall concordance (i.e., concordant in detection, size, and LR-category) was calculated with 95% confidence interval [CI], and separately for detection, size, and LR-category. Clinical significance of discordance was assessed as impact on follow-up imaging, referral for biopsy, liver transplant eligibility, or treatment modality. Reportable and actionable observations were overall concordant between two radiologists only in 32.3% [24.6, 41.0] and 40.1% [29.5, 51.5] of cases, respectively. Poor overall concordance was related to detection concordance of 52.0% [44.3, 59.5] and 62.5% [52.3, 71.8], as well as LR-category concordance of 73.7% [61.6, 83.1] and 70.9% [57.3, 81.6], for reportable and actionable observations, respectively. Discordant LI-RADS® observations would have impacted clinical management in 30 subjects (43.5%), most (66.7%) of whom were due to discordant detection. Discordant MRI LI-RADS® observations are common in patients with cirrhosis and may have potential implications for patient management.

ACR Ultrasound Liver Reporting and Data System: Multicenter Assessment of Clinical Performance at One Year

PurposeThe aim of this study was to evaluate the clinical performance of the ACR’s Ultrasound Liver Reporting and Data System (US LI-RADS™) for detecting hepatocellular carcinoma (HCC) in patients at high risk for HCC. MethodsIn this retrospective, multicenter study, 2,050 patients at high risk for HCC (1,078 men and 972 women; mean age, 57.7 years) at five sites in the United States had undergone screening liver ultrasound from January 2017 to February 2018, and US LI-RADS observation categories and visualization scores were assigned on a clinical basis. Ultrasound reports and patient records were retrospectively reviewed and follow-up imaging studies and/or pathologic reports recorded. Descriptive statistics were generated, and multivariate logistic regression analysis was used to analyze the relationship of clinical and reader-based predictors of limited visualization. Diagnostic performance data were calculated in the subset of patients with confirmatory testing. ResultsThe most common indications for HCC screening were cirrhosis (n = 1,054 [51.4%]), noncirrhotic hepatitis B virus infection (n = 555 [27.1%]), and noncirrhotic hepatitis C virus infection (n = 234 [11.4%]). US LI-RADS observation categories assigned were US-1 (negative) in 90.4% (n = 1,854), US-2 (subthreshold) in 4.6% (n = 95), and US-3 (positive) in 4.9% (n = 101). Visualization scores were A (no or minimal limitations) in 76.8% (n = 1,575), B (moderate limitations) in 18.9% (n = 388), and C (severe limitations) in 4.2% (n = 87). Confirmatory tests, including multiphase contrast-enhanced CT or MRI (n = 331) or histopathology (n = 18), were available for 349 patients (17.0%). The sensitivity of US LI-RADS in this subset of patients was 82.4%, specificity was 74.2%, positive predictive value was 35.3%, and negative predictive value was 96.1%. ConclusionsApproximately 90% of US LI-RADS screening examinations were negative, 5% subthreshold, and 5% positive. Visualization scores were diagnostically acceptable in the vast majority (>95%) of examinations. US LI-RADS emphasized sensitivity and negative predictive value, which are key characteristics of a screening test.

Quantitative Imaging Biomarkers for 90Y Distribution on Bremsstrahlung SPECT After Resin-Based Radioembolization.

Our purpose was to identify baseline imaging features in patients with liver cancer that correlate with 90Y distribution on postprocedural SPECT and predict tumor response to transarterial radioembolization (TARE). Methods: This retrospective study was approved by the institutional review board and included 38 patients with hepatocellular carcinoma (HCC) (n = 23; 18/23 men; mean age, 62.39 ± 8.62 y; 34 dominant tumors) and non-HCC hepatic malignancies (n = 15; 9/15 men; mean age, 61.13 ± 11.51 y; 24 dominant tumors) who underwent 40 resin-based TARE treatments (August 2012 to January 2018). Multiphasic contrast-enhanced MRI or CT was obtained before and Bremsstrahlung SPECT within 2 h after TARE. Total tumor volume (cm3) and enhancing tumor volume (ETV [cm3] and % of total tumor volume), and total and enhancing tumor burden (%), were volumetrically assessed on baseline imaging. Up to 2 dominant tumors per treated lobe were analyzed. After multimodal image registration of baseline imaging and SPECT/CT, 90Y distribution was quantified on SPECT as tumor-to-normal-liver ratio (TNR). Response was assessed according to RECIST1.1 and quantitative European Association for the Study of the Liver criteria. Clinical parameters were also assessed. Statistical tests included Mann-Whitney U, Pearson correlation, and linear regression. Results: In HCC patients, high baseline ETV% significantly correlated with high TNR on SPECT, demonstrating greater 90Y uptake in the tumor relative to the liver parenchyma (P < 0.001). In non-HCC patients, a correlation between ETV% and TNR was observed as well (P = 0.039). Follow-up imaging for response assessments within 1-4 mo after TARE was available for 23 patients with 25 treatments. The change of ETV% significantly correlated with TNR in HCC (P = 0.039) but not in non-HCC patients (P = 0.886). Additionally, Child-Pugh class B patients demonstrated significantly more 90Y deposition in nontumorous liver than Child-Pugh A patients (P = 0.021). Conclusion: This study identified ETV% as a quantifiable imaging biomarker on preprocedural MRI and CT to predict 90Y distribution on postprocedural SPECT in HCC and non-HCC. However, the relationship between the preferential uptake of 90Y to the tumor and tumor response after radioembolization could be validated only for HCC.