Improved differentiation between hepatic hemangioma and metastases on diffusion-weighted MRI by measurement of standard deviation of apparent diffusion coefficient

Effect of an Ultrahigh b Value of 3000 s/mm2 and the Minimal Echo-time on Image Quality and the T2 Shine-through Effect in Diffusion-weighted Imaging of the Liver at 3T: Phantom and Clinical Pilot Studies

PURPOSE

To assess the effect of an ultrahigh b value of 3000 s/mm² and the minimal TE of 53 ms on image quality and T2 shine-through effect in liver diffusion-weighted imaging (DWI) using a 3-Tesla MRI scanner with a peak gradient of 100 mT/m.

METHODS

At b values of 1000 and 3000 s/mm² and at the minimal (44-53 ms) and routine TEs (70 ms), DWI of our original phantom and liver DWI in 10 healthy volunteers and 26 patients with 35 hepatic hemangiomas were acquired with this scanner, and the quantified SNR of the phantom and the hepatic parenchyma in the volunteers and the contrast-to-noise ratio (CNR) of the hepatic hemangiomas were calculated; two independent readers qualitatively graded the overall image quality in the volunteers and determined the presence or absence of the T2 shine-through effect related to the hemangiomas in the patients. We compared the SNR and subjective overall image quality between the minimal and routine TEs and the CNR and incidence of the T2 shine-through effect between b values of 1000 and 3000 s/mm². Inter-reader agreement was also evaluated.

RESULTS

The SNR at both b values was significantly higher, and the subjective overall image quality at a b value of 3000 s/mm² was significantly better at the minimal TE than at the routine TE (P < 0.05 for all). The CNR at both TEs and the incidence of the T2 shine-through effect at the minimal TE were significantly lower at a b value of 3000 s/mm² than at a b value of 1000 s/mm² (P < 0.05 for all). Inter-reader agreement was excellent.

CONCLUSION

Liver DWI at the ultrahigh b value can reduce the T2 shine-through effect with improvement of image quality using the minimal TE.

Distinction Between Hepatocellular Carcinoma and Hypervascular Liver Metastases in Non-cirrhotic Patients Using Gadoxetate Disodium-Enhanced Magnetic Resonance Imaging

Purpose: This study aims to identify the hallmarks of gadoxetate disodium-enhanced magnetic resonance imaging distinguishing hepatocellular carcinoma (HCC) from hypervascular liver metastases (HLMs). Methods: Between January 2008 and October 2020, among patients who underwent gadoxetate disodium-enhanced MRI, those who met the following criteria were retrospectively included: without chronic hepatitis or liver stiffness ≤ 2.5 kPa on magnetic resonance elastography or F0/F1 on pathological assessment. Two blinded radiologists reviewed the imaging findings to judge the presence or absence of the enhancing capsule, nonperipheral washout, corona enhancement, hypointensity in the transitional/hepatobiliary phase (HBP), hyperintensity on T2-weighted/diffusion-weighted imaging (DWI), mosaic architecture, and blood products/fat in mass. The lesion-to-liver signal intensity ratios in HBP and DWI were also calculated. Univariate and multivariate analyses were performed to identify the imaging hallmarks distinguishing HCC from HLM. Interobserver agreement was calculated using kappa values and intraclass correlation coefficients (ICCs). Results: The final study cohort comprised 72 lesions in 44 patients (mean age, 65.0±11.9 years). Univariate analysis revealed higher frequencies of the following features in HCC than in HLM (P < .10): nonperipheral washout, corona enhancement, transitional phase hypointensity, mosaic architecture, and fat in mass (P = .002-.073). Multivariate analysis revealed that nonperipheral washout and mosaic architecture favored the diagnosis of HCC over that of HLM with odds ratios of 7.66 and 14.6, respectively (P = .038 and .029, respectively). The interobserver agreement for each item was moderate or substantial (kappa or ICC = .447-.792). Conclusion: Peripheral washout and mosaic architecture may be reliable imaging hallmarks for distinguishing HCC from HLM.

Differentiating Liver Hemangioma from Metastatic Tumor Using T2-enhanced Spin-echo Imaging with a Time-reversed Gradient-echo Sequence in the Hepatobiliary Phase of Gadoxetic Acid-enhanced MR Imaging

Purpose: To evaluate the utility of T2-enhanced spin-echo imaging using the time-reversed gradient echo sequence (T2FFE imaging) in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI (Gd-EOB-MRI) for differentiating hemangiomas from metastatic tumors.

Methods: A total of 61 patients with 133 liver lesions, including 37 hemangiomas and 96 metastatic tumors, were scanned by Gd-EOB-MRI. Four data sets were independently analyzed by two readers: (1) 3D fat-suppressed T2-weighted imaging (FS-T2WI) alone; (2) the combination of 3D FS-T2WI and T2FFE imaging in the HBP of Gd-EOB-MRI; (3) the combination of 3D FS-T2WI, diffusion-weighted imaging (DWI) with the b-value of 1000 s/mm² and the apparent diffusion coefficient (ADC); and (4) a dynamic study of Gd-EOB-MRI. After classifying the lesion sizes as ≤ 10 mm or > 10 mm, we conducted a receiver-operating characteristic analysis to compare diagnostic accuracies among the four data sets for differentiating hemangiomas from metastatic tumors.

Results: The areas under the curves (AUCs) of the four data sets of two readers were: (1) ≤ 10 mm (0.85 and 0.91) and > 10 mm (0.88 and 0.97), (2) ≤ 10 mm (0.94 and 0.94) and > 10 mm (0.96 and 0.95), (3) ≤ 10 mm (0.90 and 0.87) and > 10 mm (0.89 and 0.95), and (4) ≤ 10 mm (0.62 and 0.67) and > 10 mm (0.76 and 0.71), respectively. Data sets (2) and (3) showed no significant differences in AUCs, but both showed significantly higher AUCs compared to that of (4) regardless of the lesion size (P < 0.05).

Conclusion: The combination of 3D FS-T2WI and T2FFE imaging in the HBP of Gd-EOB-MRI achieved an accuracy equivalent to that of the combination of 3D FS-T2WI, DWI, and ADC and might be helpful in differentiating hemangiomas from metastatic tumors.

The humbling hemangioma: uncommon CT and MRI imaging features and mimickers of hepatic hemangiomas

Cavernous hemangiomas are among the most common liver lesions encountered in abdominal imaging. While classical imaging characteristics usually aid the radiologist in confidently arriving at its diagnosis, atypical hemangiomas can prove to be difficult to distinguish from other more worrisome hepatic lesions such as metastases and hepatocellular carcinoma. Furthermore, some malignant lesions can display features that simulate hemangiomas. The radiologist must be aware of these pitfalls to make an accurate diagnosis, when possible.

Is the standard deviation of the apparent diffusion coefficient a potential tool for the preoperative prediction of tumor grade in endometrial cancer?

BACKGROUND

The tumor histological grade is closely related to the prognosis of endometrial cancer (EC). The use of the apparent diffusion coefficient (ADC), tumor volume, and MRI-based texture analysis has allowed exciting advances in predicting EC grade before surgery. However, whether this constitutes a simple, convenient, and powerful diagnostic method remains unknown.

PURPOSE

To explore the utility of standard deviation (SD) of the ADC (ADC_SD) for predicting the tumor grade in patients with EC.

MATERIAL AND METHODS

We retrospectively evaluated 138 patients with EC. All patients underwent unenhanced MRI and diffusion-weighted imaging (DWI). The mean ADC value (ADC_mean) and SD were obtained using a freehand region of interest traced on the ADC map. Spearman's linear correlation coefficients were calculated to analyze the correlations between the indexes (including ADC_SD and the ADC_mean) and the Ki-67 index. The Kruskal-Wallis and Mann-Whitney U tests were used to compare differences in the index results among tumor grades.

RESULTS

A significant difference in ADC_SD was observed among the tumor grades (P=0.000), and the ADC_SD value was significantly higher for high-grade EC than for low-grade tumors (289.7 vs. 216.3×10^-6mm² /s, P=0.000). A statistically significant positive correlation was observed between ADC_SD and the Ki-67 index (r=0.364, P=0.000). According to the receiver operating characteristic curve, ADC_SD ≥240.2×10^-6mm² /s predicted high-grade EC with a sensitivity, specificity, and accuracy of 73.1%, 80.2%, and 77.5%, respectively.

CONCLUSION

Based on the intratumor heterogeneity of EC, ADC_SD represents a potential method for the preoperative prediction of high-grade EC, although further studies are needed.

Advance computer analysis of magnetic resonance imaging (MRI) for early brain tumor detection

PURPOSE

The brain tumor grows inside the skull and interposes with regular brain functioning. The tumor growth may possibly result in cancer at a later stage. The early detection of brain tumor is crucial for successful treatment of fatal disease. The tumor presence is normally detected by Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) images. The MRI/CT images are highly complex and involve huge data. This requires highly tedious and time-consuming process for detection of small tumors for the neurologists. Thus, there is a need to develop an effective and less time-consuming imaging technique for early detection of brain tumors.

MATERIALS AND METHODS

This paper mainly focuses on early detecting and localizing the brain tumor region using segmentation of patient's MRI images. The Matlab software experiments are performed on a set of fifteen tumorous MRI images. In the proposed work, four image segmentation modalities namely watershed transform, k-means clustering, thresholding and Fuzzy C Means Clustering techniques with median filtering have been implemented.

RESULTS

The results are verified by quantitative comparison of results in terms of image quality evaluation parameters-Entropy, standard deviation and Naturalness Image Quality Evaluator. A remarkable rise in the entropy and standard deviation values has been noticed.

CONCLUSIONS

The watershed transform segmentation with median filtering yields the best quality brain tumor images. The noteworthy improvement in visibility of the MRI images may highly increase the possibilities of early detection and successful treatment of brain tumor disease and thereby assists the clinicians to decide the precise therapies.