Quantitative evaluation of Gd-EOB-DTPA uptake in focal liver lesions by using T1 mapping: differences between hepatocellular carcinoma, hepatic focal nodular hyperplasia and cavernous hemangioma

MRI-based bone marrow radiomics for predicting cytogenetic abnormalities in multiple myeloma

AIM

To develop a radiomics signature applied to magnetic resonance imaging (MRI)-images to predict cytogenetic abnormalities in multiple myeloma (MM).

MATERIALS AND METHODS

Patients with newly diagnosed MM were enrolled retrospectively from March 2019 to September 2022. They were categorised into the high-risk cytogenetics (HRC) group and standard-risk cytogenetics (SRC) group. The patients were allocated randomly at a ratio of 7:3 into training and validation cohorts. Volumes of interest (VOI) was drawn manually on fat suppression T2-weighted imaging (FS-T2WI) and copied to the same location of the T1-weighted imaging (T1WI) sequence. Radiomics features were extracted from two sequences and selected by reproducibility and redundant analysis. The least absolute shrinkage selection operation (LASSO) algorithm was applied to build the radiomics signatures. The performance of the radiomics signatures to distinguish HRC with SRC was evaluated by ROC curves. The area under the curve (AUC), specificity, and sensitivity were also calculated.

RESULTS

A total of 105 MM patients were enrolled in this study. The four and 11 most significant and relevant features were selected separately from T1WI and FS-T2WI sequences to build the radiomics signatures based on the training cohort. Compared to the T1WI sequence, the radiomics signature based on the FS-T2WI sequence achieved better performance with AUCs of 0.896 and 0.729 in the training and validation cohorts respectively. A sensitivity of 0.833, specificity of 0.667, and Youden index of 0.500 were achieved for the FS-T2WI radiomics signature in the validation cohort.

CONCLUSIONS

The radiomics signature based on MRI provides a non-invasive and convenient tool to predict cytogenetic abnormalities in MM patients.

Imaging of pediatric liver tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper

Primary hepatic malignancies are relatively rare in the pediatric population, accounting for approximately 1%-2% of all pediatric tumors. Hepatoblastoma and hepatocellular carcinoma are the most common primary liver malignancies in children under the age of 5 years and over the age of 10 years, respectively. This paper provides consensus-based imaging recommendations for evaluation of patients with primary hepatic malignancies at diagnosis and follow-up during and after therapy.

Hepatocellular carcinoma: State of the art diagnostic imaging

Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.

Water-Fat Separated T1 Mapping in the Liver and Correlation to Hepatic Fat Fraction

(1) Background: T1 mapping in magnetic resonance imaging (MRI) of the liver has been proposed to estimate liver function or to detect the stage of liver disease, among others. Thus far, the impact of intrahepatic fat on T1 quantification has only been sparsely discussed. Therefore, the aim of this study was to evaluate the potential of water-fat separated T1 mapping of the liver. (2) Methods: A total of 386 patients underwent MRI of the liver at 3 T. In addition to routine imaging techniques, a 3D variable flip angle (VFA) gradient echo technique combined with a two-point Dixon method was acquired to calculate T1 maps from an in-phase (T1_in) and water-only (T1_W) signal. The results were correlated with proton density fat fraction using multi-echo 3D gradient echo imaging (PDFF) and multi-echo single voxel spectroscopy (PDFF_MRS). Using T1_in and T1_W, a novel parameter FF_T1 was defined and compared with PDFF and PDFF_MRS. Furthermore, the value of retrospectively calculated T1_W (T1_W_calc) based on T1_in and PDFF was assessed. Wilcoxon test, Pearson correlation coefficient and Bland-Altman analysis were applied as statistical tools. (3) Results: T1_in was significantly shorter than T1_W and the difference of both T1 values was correlated with PDFF (R = 0.890). FF_T1 was significantly correlated with PDFF (R = 0.930) and PDFF_MRS (R = 0.922) and yielded only minor bias compared to both established PDFF methods (0.78 and 0.21). T1_W and T1_W_calc were also significantly correlated (R = 0.986). (4) Conclusion: T1_W acquired with a water-fat separated VFA technique allows to minimize the influence of fat on liver T1. Alternatively, T1_W can be estimated retrospectively from T1_in and PDFF, if a Dixon technique is not available for T1 mapping.

3D variable flip angle T1 mapping for differentiating benign and malignant liver lesions at 3T: comparison with diffusion weighted imaging

BACKGROUND

Different methods have been used to improve the imaging diagnosis of focal liver lesions (FLL). Among them, magnetic resonance imaging (MRI) has received more attention since it provides significant amount of information without radiation exposure. However, atypical imaging characteristics of FLL on MRI may complicate the differential diagnosis between benign and malignant FLL. This study aimed to compare the diagnostic value of T1 mapping and diffusion-weighted imaging (DWI) for differentiating of benign and malignant FLLs.

METHODS

This retrospective study enrolled 294 FLLs, including 150 benign and 144 malignant lesions. Whole liver T1 mapping sequences were obtained before and 2 min after the administration of Gd-DTPA to acquire native T1 and enhanced T1 and ΔT1%. Additionally, DWI sequence was conducted to generate apparent diffusion coefficient (ADC) maps. These quantitative parameters were compared using one-way analysis of variance, and the diagnostic accuracy of T1 mapping and ADC for FLLs was calculated by area under the curve (AUC).

RESULTS

Significant differences were observed regarding the native T1, enhanced T1, ΔT1%, and ADC between benign and malignant FLLs. Furthermore, the sensitivity and specificity of the parameters are as follows: native T1 0.797/0.702 (cut off value 1635.5 ms); enhanced T1, 0.911/0.976 (cutoff value 339.2 ms); ΔT1%, 0.901/0.905 (cutoff value 70.8%); and ADC, 0.975/0.952 (cutoff value 1.21 × 10^-3 mm²/s). The ideal cutoff values for native T1 and ADC in identifying cyst and haemangioma were 2422.9 ms (AUC 0.990, P < 0.01) and 2.077 × 10^-3 mm²/s (AUC 0.949, P < 0.01), respectively, with a sensitivity and specificity of 0.963/1 and 0.852/0.892, respectively. ADC was significantly positively correlated with T1 and ΔT1%, and significantly negatively correlated with enhanced T1.

CONCLUSION

The 3D Variable flip angle T1 mapping technique with Gd-DTPA has a high clinical potential for identifying benign and malignant FLLs. The enhanced T1 and ΔT1% values have similar diagnostic accuracy compared with DWI in evaluating FLLs. Native T1 shows better performance than DWI in distinguishing benign liver lesions, specifically, cysts, and haemangioma.

Effects of T2 * on accuracy and precision of dynamic T1 measurements using the single reference variable flip angle method - a simulation study

PURPOSE

To study in simulation and in theory the accuracy and precision of dynamic T₁ measurements obtained using the previously published single-reference variable flip angle (SR-VFA) technique, with a focus on the effects of dynamic changes in T₂ * on the calculation.

METHODS

Monte Carlo simulations were performed over 1000 noisy iterations for the VFA method, the SR-VFA method, and a proposed method, SR-VFA with a T₂ * correction (SR-VFA-T₂ *). Dynamic T₁ estimates were calculated analytically for each method, with signals modeled by the steady-state spoiled gradient echo equation. The mean and standard deviation of these estimates were calculated and compared to truth, while varying repetition time (TR), baseline and dynamic T₁ , echo time (TE), baseline and dynamic T₂ *, flip angles, and the number of averages on baseline scans. Additionally, the variance of T₁ in the SR-VFA and SR-VFA-T₂ * methods was derived analytically based on the theory of propagation of errors. This equation was used to produce an inverse-variance weighted linear combination to improve T₁ mapping precision in the SR-VFA-T₂ * method. Flip angle sensitivity of dynamic T₁ precision in the SR-VFA and SR-VFA-T₂ * methods was also performed.

RESULTS

Substantial bias can be produced by the SR-VFA method when the ratio of the T₂ * decay of the dynamic signal versus that of the baseline signals deviates from 1, with a 0.01 deviation leading to approximately a 1% bias in cases of high SNR and TR ≫ T₁ . This bias can be corrected by estimating the baseline and dynamic T₂ * values in this ratio via multi-echo measurements. The bias and precision of the SR-VFA-T₂ * method, when normalized to scan time, is found to rival and sometimes improve upon the two flip angle VFA method when an inverse variance weighted linear combination is applied across its multi-echo T₁ maps. The analytic variance equation presented is found to be accurate within 1% relative to the Monte Carlo simulations over a broad parameter space. Flip angle ranges that maximize SR-VFA and SR-VFA-T₂ * T₁ precision over a broad parameter space are given, and each is defined relative to TR and T₁ .

CONCLUSIONS

Multi-echo SR-VFA-T₂ * T₁ mapping is found in simulation and theory to be a promising alternative to the VFA method that maintains speed of the SR-VFA method with accuracy and precision similar to the VFA method. This article is protected by copyright. All rights reserved.

Quantitative analysis of gadoxetic acid-enhanced MRI for the differential diagnosis of focal liver lesions: Comparison between estimated intralesional gadoxetic acid retention by T1 mapping and conventional processing methods

PURPOSE

To compare the estimated quantity of intratumor gadoxetic acid retention using T1 mapping of gadoxetic acid-enhanced magnetic resonance imaging (MRI) versus conventional processing methods for the differential diagnosis of focal liver lesions.

METHODS

Seventy patients with hepatic lesions (colorectal metastasis (CRM) [n = 28], hepatocellular carcinoma (HCC) [n = 20], hemangioma [n = 12], and intrahepatic cholangiocarcinoma (ICC) [n = 10]) underwent gadoxetic acid-enhanced MRI, including pre- and post-contrast T1-weighted imaging and T1 mapping. Quantitative analyses included the lesion-to-liver signal intensity ratio (SIR) on hepatobiliary phase images, the pre- and post-contrast lesion T1 value difference (ΔT1 [ms]), and the lesion retention index (LRI [%]), which was the estimated intralesional gadoxetic acid retention calculated on pre- and post-contrast T1 maps using a two-compartment pharmacokinetic model. Results were compared between the four subcategories of focal liver lesions using the Kruskal-Wallis test, followed by the post-hoc Dunn's test and receiver operating characteristic (ROC) analysis to distinguish between pairs of the four lesion subcategories.

RESULTS

This study identified significant differences in the LRI of the four lesion subcategories (p <  0.01), without significant differences in ΔT1 or SIR. Post-hoc analysis demonstrated significant differences in CRM vs. hemangioma (p <  0.01), hemangioma vs. ICC (p <  0.01), and HCC vs. ICC (p =  0.047) for the LRI.

CONCLUSIONS

The quantity of intratumor gadoxetic acid retention estimated using pre- and post- contrast T1 mapping could distinguish focal liver lesions, unlike conventional processing methods, and captured unique lesion characteristics.

Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning-Based Radiomics

Objective

To determine whether machine learning based on conventional magnetic resonance imaging (MRI) sequences have the potential for the differential diagnosis of multiple myeloma (MM), and different tumor metastasis lesions of the lumbar vertebra.

Methods

We retrospectively enrolled 107 patients newly diagnosed with MM and different metastasis of the lumbar vertebra. In total 60 MM lesions and 118 metastasis lesions were selected for training classifiers (70%) and subsequent validation (30%). Following segmentation, 282 texture features were extracted from both T1WI and T2WI images. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the following machine learning models were selected: Support‐Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Networks (ANN), and Naïve Bayes (NB) using 10-fold cross validation, and the performances were evaluated using a confusion matrix. Matthews correlation coefficient (MCC), sensitivity, specificity, and accuracy of the models were also calculated.

Results

To differentiate MM and metastasis, 13 features in the T1WI images and 9 features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.605) with accuracy, sensitivity, and specificity of 0.815, 0.879, and 0.790, respectively, in the validation cohort. To differentiate MM and metastasis subtypes, eight features in the T1WI images and seven features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.560, 0.412, 0.449), respectively, with accuracy = 0.648; sensitivity 0.714, 0.821, 0.897 and specificity 0.775, 0.600, 0.640 for the MM, lung, and other metastases, respectively, in the validation cohort.

Conclusions

Machine learning–based classifiers showed a satisfactory performance in differentiating MM lesions from those of tumor metastasis. While their value for distinguishing myeloma from different metastasis subtypes was moderate.

Quantitative evaluation of focal liver lesions with T1 mapping using a phase-sensitive inversion recovery sequence on gadoxetic acid-enhanced MRI

Purpose

To determine the usefulness of T1 values measured using a phase-sensitive inversion recovery (PSIR) sequence for the diagnosis of focal liver lesions.

Method

The study enrolled 87 patients who underwent gadoxetic acid-enhanced magnetic resonance imaging (MRI) for assessment of 38 hepatocellular carcinomas, 33 hepatic hemangiomas, 30 metastatic liver tumors, and 14 hepatic cysts. PSIR was performed before and 15 min after contrast agent administration, and then the respective T1 values were measured and the T1 reduction rate was calculated. Wilcoxon matched-pairs signed-rank test was used to compare T1 values pre- and post-contrast administration in each tumor. The Kruskal-Wallis test and Dunn's post-hoc test were used to compare T1 values among all tumors pre- and post-contrast administration and the T1 reduction rate among all tumors.

Results

The T1 values measured before and after contrast enhancement were 1056 ± 292 ms and 724 ± 199 ms for hepatocellular carcinoma, 1757 ± 723 ms and 1033 ± 406 ms for metastatic liver tumor, 2524 ± 908 ms and 1071 ± 390 ms for hepatic hemangioma, and 3793 ± 207 ms and 3671 ± 241 ms for liver cysts, respectively. The T1 values obtained before and after contrast administration showed significant differences for all tumors except liver cysts (P <  0.0001). T1 reduction rate was not significantly different between hepatocellular carcinoma and metastatic liver tumor, but was significantly different among other tumors (P <  0.05).

Conclusions

T1 mapping using the PSIR sequence is useful to differentiate focal liver lesions.

Native T1 Mapping Magnetic Resonance Imaging as a Quantitative Biomarker for Characterization of the Extracellular Matrix in a Rabbit Hepatic Cancer Model

To characterize the tumor extracellular matrix (ECM) using native T1 mapping magnetic resonance imaging (MRI) in an experimental hepatic cancer model, a total of 27 female New Zealand white rabbits with hepatic VX2 tumors were examined by MRI at different time points following tumor implantation (day 14, 21, 28). A steady-state precession readout single-shot MOLLI sequence was acquired in a 3 T MRI scanner in prone position using a head-neck coil. The tumors were segmented into a central, marginal, and peritumoral region in anatomical images and color-coded T1 maps. In histopathological sections, stained with H&E and Picrosirius red, the regions corresponded to central tumor necrosis and accumulation of viable cells with fibrosis in the tumor periphery. Another region of interest (ROI) was placed in healthy liver tissue. T1 times were correlated with quantitative data of collagen area staining. A two-way repeated-measures ANOVA was used to compare cohorts and tumor regions. Hepatic tumors were successfully induced in all rabbits. T1 mapping demonstrated significant differences between the different tumor regions (F(1.43,34.26) = 106.93, p < 0.001) without interaction effects between time points and regions (F(2.86,34.26) = 0.74, p = 0.53). In vivo T1 times significantly correlated with ex vivo collagen stains (area %), (center: r = 0.78, p < 0.001; margin: r = 0.84, p < 0.001; peritumoral: r = 0.73, p < 0.001). Post hoc tests using Sidak’s correction revealed significant differences in T1 times between all three regions (p < 0.001). Native T1 mapping is feasible and allows the differentiation of tumor regions based on ECM composition in a longitudinal tumor study in an experimental small animal model, making it a potential quantitative biomarker of ECM remodeling and a promising technique for future treatment studies.

Hepatocellular carcinoma grading and recurrence prediction using T1 mapping on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging

The aim of the present study was to explore the value of T₁ mapping on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for grading hepatocellular carcinoma (HCC) and predicting its recurrence rate. A retrospective study was performed that included 75 patients (66 men and 9 women; mean age, 52.89 years; age range, 23-79 years) with HCC who had undergone Gd-EOB-DTPA-enhanced MRI with T₁ mapping before surgery. The T₁ relaxation time of the 81 lesions and non-tumorous liver parenchyma in 75 patients with HCC were measured before Gd-EOB-DTPA was injected and then at 5, 10 and 20 min after administration, respectively. T_{1[lesion (L)-hepatic parenchyma (H)]/H} (%) was calculated as the increment rate of the T₁ value in the lesions relative to the non-tumorous liver parenchyma. One-way analysis of variance and Spearman's correlation analysis was used to compare the differences and relationship of T₁ mapping values among the three grades of HCC. A total of 81 lesions were divided into well-differentiated HCC (grades I; n=21), moderately differentiated HCC (grades II; n=40) and poorly differentiated HCC (grades III; n=20) according to the histopathology. The T_1(L-H)/H (%) value among grades I, II and III HCC on pre-contrast results and on post-contrast results at the 5-, 10- and 20-min hepatobiliary phase (HBP) were significantly different (P<0.05), and T_1(L-H)/H (%) was correlated with the histological grade of HCC at each time point (r=0.637, r=0.554, r=0.499 and r=0.560, respectively, P<0.001). A total of 41 recurrence cases [grade I (n=5), grade II (n=23) and grade III (n=13)] were verified by imaging (CT, MRI or ultrasound) or reoperation. Patients with grade III and grade II HCC had higher recurrence rates compared with that in patients with grade I HCC (P<0.05; median recurrence times were 258 days, 605 days and undefined, respectively). According to the optimal cut-off point for the T_1(L-H)/H (%) of the three grades of HCC, patients with HCC in the low T_1(L-H)/H (%) value group (≤155.15%) had lower cumulative recurrence rates compared with that in the medium (T_1(L-H)/H (%) >155.15% and T1_(L-H)/H (%) ≤241.20%) and high (T_1(L-H)/H (%) >241.20%) value groups at the 20-min HBP (P<0.05; median recurrence times were undefined, 530 days and 447 days, respectively). These results indicate that the parameters of T₁ mapping would be beneficial for predicting the grading and recurrence of HCC.

Hepatocellular Carcinoma: State of the Art Imaging and Recent Advances

The incidence of hepatocellular carcinoma (HCC) is increasing, with this trend expected to continue to the year 2030. Hepatocarcinogenesis follows a predictable course, which makes adequate identification and surveillance of at-risk individuals central to a successful outcome. Moreover, imaging is central to this surveillance, and ultimately to diagnosis and management. Many liver study groups throughout Asia, North America and Europe advocate a surveillance program for at-risk individuals to allow early identification of HCC. Ultrasound is the most commonly utilized imaging modality. Many societies offer guidelines on how to diagnose HCC. The Liver Image Reporting and Data System (LIRADS) was introduced to standardize the acquisition, interpretation, reporting and data collection of HCC cases. The LIRADS advocates diagnosis using multiphase computed tomography or magnetic resonance imaging (MRI) imaging. The 2017 version also introduces contrast-enhanced ultrasound as a novel approach to diagnosis. Indeed, imaging techniques have evolved to improve diagnostic accuracy and characterization of HCC lesions. Newer techniques, such as T1 mapping, intravoxel incoherent motion analysis and textural analysis, assess specific characteristics that may help grade the tumor and guide management, allowing for a more personalized approach to patient care. This review aims to analyze the utility of imaging in the surveillance and diagnosis of HCC and to assess novel techniques which may increase the accuracy of imaging and determine optimal treatment strategies.

[Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging]

OBJECTIVE

To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE).

METHODS

Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE. In multivariate analysis, the performance of 3 feature selection algorithms (namely minimum redundancy-maximum relevance, mRmR; neighborhood component analysis, NCA; and sequence forward selection, SFS) was compared. The optimal feature subset was determined according to the optimal feature selection algorithm and used for testing the 3 classifier algorithms (namely the support vector machine, RBF-SVM; linear discriminant analysis, LDA; and logistic regression). All the tests were repeated 5 times with 10-fold cross validation experiments.

RESULTS

More than 50% of the radiomic features exhibited strong distinguishing ability, among which gray level co-occurrence matrix feature S (3, -3) SumEntrp showed a good classification performance with an AUC of 0.72 (P<0.01), a sensitivity of 0.83 and a specificity of 0.57. For the multivariate analysis, 15 features were selected based on the SFS algorithm, which produced better results than the other two algorithms. Testing of these 15 selected features for their average cross-validation performance with RBF-SVM classifier yielded a test accuracy of 0.82∓0.09, an AUC of 0.86∓0.12, a sensitivity of 0.88∓0.11, and a specificity of 0.76∓0.18.

CONCLUSION

The radiomic features based on gadoxetate disodium-enhanced magnetic resonance images allow efficient differential diagnosis of HCC and HHE, and can potentially provide important assistance in clinical diagnosis of the two diseases.