MR Prediction of Malignant Switch With the Cyst Fluid's T2 Value in Intraductal Papillary Mucinous Neoplasm of the Pancreas: A Preliminary Study.

BACKGROUND/AIM: We investigated whether the malignant switch of intraductal papillary mucinous neoplasm (IPMN) of the pancreas can be predicted by using the T1ϱ, T2, and apparent diffusion coefficient (ADC) values of cyst fluid. PATIENTS AND METHODS: We retrospectively analyzed the magnetic resonance (MR) images of 60 patients (26 males, 34 females, mean age 61 years) with branch-duct type and mixed-type IPMNs. The IPMNs were diagnosed clinically in 39 patients and histologically in 21 patients. The malignant potential was classified by MR imaging based on the international consensus guidelines for the management of IPMN established in 2017. Morphologically, 42 patients had "worrisome features" and three had "high-risk stigmata." Histologically, 14 lesions were diagnosed as low-grade dysplasia and seven as intermediate-grade dysplasia. The T1ϱ, T2, and ADC values of cyst fluid in each patient's largest cyst were measured on the same slice, avoiding solid components. Spearman's rank correlation test was used to determine the correlation between the morphological malignancy and the T1ϱ, T2, and ADC values. These values were also compared between the low-grade and intermediate-grade groups by Mann-Whitney U-test. RESULTS: There was a significant rank-correlation between the morphological classification and T2 value (p=0.04). The T2 value of the intermediate-grade group was significantly higher than that of the low-grade group (p=0.03). No significant correlations were morphologically or histologically obtained regarding T1ϱ and ADC. CONCLUSION: The T2 value of cyst fluid together with other MR-signs may be useful for predicting the malignant switch in IPMN of the pancreas.

Differentiation of hemangioblastoma from brain metastasis using MR amide proton transfer imaging.

BACKGROUND AND PURPOSE: Differentiation between hemangioblastoma and brain metastasis remains a challenge in neuroradiology using conventional MRI. Amide proton transfer (APT) imaging can provide unique molecular information. This study aimed to evaluate the usefulness of APT imaging in differentiating hemangioblastomas from brain metastases and compare APT imaging with diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging. METHODS: This retrospective study included 11 patients with hemangioblastoma and 20 patients with brain metastases. Region-of-interest analyses were employed to obtain the mean, minimum, and maximum values of APT signal intensity, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV), and these indices were compared between hemangioblastomas and brain metastases using the unpaired t-test and Mann-Whitney U test. Their diagnostic performances were evaluated using receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC). AUCs were compared using DeLong's method. RESULTS: All MRI-derived indices were significantly higher in hemangioblastoma than in brain metastasis. ROC analysis revealed the best performance with APT-related indices (AUC = 1.000), although pairwise comparisons showed no significant difference between the mean ADC and mean rCBV. CONCLUSIONS: APT imaging is a useful and robust imaging tool for differentiating hemangioblastoma from metastasis.

Diagnostic potential of T1ρ and T2 relaxations in assessing the severity of liver fibrosis and necro-inflammation.

PURPOSE: To investigate the utility of T1ρ and T2 relaxations for assessing the severity of liver fibrosis (F stage) and necro-inflammation (A stage) in patients with chronic liver disease (CLD). MATERIALS AND METHODS: We calculated T1ρ and T2 relaxations of the liver parenchyma in 82 patients who underwent liver surgery. F and A stages of enrolled patients were assessed by referring to surgically resected specimens. The relationships between T1ρ or T2 relaxation and F or A stage were assessed using one-way analysis of variance followed by Tukey's multiple comparison test, Spearman's rank correlation test and a receiver operating characteristic analysis. RESULTS: The T1ρ and T2 values of the liver parenchyma were significantly increased as the F and A stages progressed. The T1ρ and T2 values showed significant differences between F0 and F4, between F1 and F4, and between F2 and F4. In addition, T1ρ values showed a significant difference between F0 and F3 as well. The highest diagnostic ability for fibrosis was obtained when differentiating ≥F3 from ≤F2 using T1ρ: the sensitivity was 82.8%, the specificity 79.2% and the area under the curve (AUC) 0.87. The sensitivity and AUC of T1ρ relaxation (46.9% and 0.67) were significantly higher than those of T2 relaxation (29.7% and 0.60) for differentiating ≥A1 from A0. CONCLUSION: T1ρ and T2 relaxations have potential as a biochemical marker for assessing the severity of liver fibrosis and necro-inflammation. T1ρ relaxation may be slightly superior to T2 relaxation in terms of diagnostic ability for liver fibrosis and necro-inflammation.

Comparison of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted MR imaging models in differentiating hepatic hemangiomas from liver metastases.

PURPOSE: This study aims to compare the diagnostic values of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) in differentiating hepatic hemangiomas and liver metastases. METHOD: This prospective study was approved by our institutional review board, and written informed consent was obtained from all patients. In this study, 244 patients with known or suspected liver disease underwent magnetic resonance imaging. Among them, 37 patients who had focal hepatic lesions with a maximum diameter of ≥10 mm were evaluated. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for the three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from a stretched exponential model. The parameters were compared between hepatic hemangiomas and liver metastases. RESULTS: In total, 64 focal hepatic lesions were evaluated, of which 22 were identified to be hepatic hemangiomas and 42 were liver metastases. ADC, D, f, and DDC values were significantly lower in liver metastases than in hepatic hemangiomas (P <  0.0001, <  0.0001, 0.015, and <  0.0001, respectively); whereas, the α value was significantly higher in liver metastases than in hepatic hemangiomas (P =  0.028). The areas under the ROC curve (AUCs) for differentiating hepatic hemangiomas and liver metastases in ADC, D, D*, f, DDC, and α were 0.940, 0.908, 0.608, 0.686, 0.952, and 0.667, respectively. The AUC values of ADC and DDC were significantly greater than those of D* (P <  0.0001), f (P =  0.0001), and α values (P =  0.0001). CONCLUSION: ADC and DDC values from the mono-exponential and stretched exponential models could be considered as quantitative imaging biomarkers for differentiating hepatic hemangiomas and liver metastases.

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.

Comparison of the Diagnostic Value of Mono-exponential, Bi-exponential, and Stretched Exponential Signal Models in Diffusion-weighted MR Imaging for Differentiating Benign and Malignant Hepatic Lesions.

PURPOSE: To compare the diagnostic value of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) for differentiating benign and malignant hepatic lesions. METHODS: This prospective study was approved by our Institutional Review Board and the patients provided written informed consent. Magnetic resonance imaging was acquired for 56 patients with suspected liver disease. This identified 90 focal liver lesions with a maximum diameter >10 mm, of which 47 were benign and 43 were malignant. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from a stretched exponential model. The parameters were compared between benign and malignant hepatic lesions. RESULTS: ADC, D, D*, f, and DDC values were significantly lower for malignant hepatic lesions than for benign lesions (P < 0.0001-0.03). Although logistic regression analysis demonstrated that DDC was the only statistically significant parameter for differentiating benign and malignant lesions (P = 0.039), however, the areas under the receiver operating characteristic curve for differentiating benign and malignant lesions were comparable between ADC (0.98) and DDC (0.98) values. CONCLUSION: DDC values obtained from the stretched exponential model could be also used as a quantitative imaging biomarker for differentiating benign and malignant hepatic lesions, however, the diagnostic performance was comparable with ADC values.

Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy.

PURPOSE: Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretable way, in contrast to deep learning (DL) techniques. METHODS: HC was constructed using mp-MR including intravoxel incoherent motion, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI from 40 tumor and normal tissues in peripheral zone (PZ) and 23 tumor and normal tissues in transition zone (TZ). HC model was optimized by assessing the combinations of several dissimilarity and linkage methods. Goodness of HC model was validated by internal methods. RESULTS: Accuracy for differentiating tumor and normal tissue by optimal HC model was 96.3% in PZ and 97.8% in TZ, comparable to current clinical standards. Relationship between input (DWI and permeability parameters) and output (tumor and normal tissue cluster) was shown by heat maps, consistent with literature. CONCLUSION: HC can accurately differentiate PCa and normal tissue, comparable to state-of-the-art diffusion based parameters. Contrary to DL techniques, HC is an operator-independent ML technique producing results that can be interpreted such that the results can be knowledgeably judged.

Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models.

PURPOSE: Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models' ability to differentiate between high-grade and low-grade gliomas. METHODS: This retrospective study included 22 patients with diffuse gliomas (age, 23-74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses. RESULTS: The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. - 0.18 ± 0.63, 0.68 ± 0.74 vs. - 0.08 ± 0.66, 0.63 ± 0.72 vs. - 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96). CONCLUSION: The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas.

Assessment of microvessel perfusion of pituitary adenomas: a feasibility study using turbo spin-echo-based intravoxel incoherent motion imaging.

OBJECTIVES: To evaluate the feasibility of assessment of microvessel perfusion of pituitary adenomas with intravoxel incoherent motion (IVIM) imaging using single-shot turbo spin-echo-based diffusion-weighted imaging (SS-TSE-DWI). METHODS: We examined 51 consecutive patients with pituitary adenomas (35 non-functioning and 16 functioning) and 32 patients with normal pituitary glands using SS-TSE-DWI IVIM. The diffusion coefficient (D), the perfusion fraction (f), and the pseudo-diffusion coefficient (D*) were calculated pixel-by-pixel for each adenoma and normal pituitary gland. We also obtained the pathological microvessel area (MVA) of each adenoma. The IVIM parameters in adenomas were compared with those in normal pituitary glands using the Mann-Whitney U test. The correlation between the MVA and IVIM f of adenomas was analyzed using Spearman's rank correlation coefficient. RESULTS: The mean D (× 10-3 mm2/s) in adenomas was 0.723 ± 0.253, which was significantly lower than that in normal pituitary glands (0.862 ± 0.128; p < 0.0001). The mean f (%) in adenomas was 10.74 ± 4.51, which was significantly lower than that in normal pituitary glands (13.26 ± 4.32, p = 0.0251). No significant difference was found in the mean D*. We found a significant positive correlation between MVA and f in non-functioning adenomas (ρ = 0.634, p < 0.0001) as well as in all adenomas (ρ = 0.451, p = 0.0009). CONCLUSIONS: Assessment of microvessel perfusion of pituitary adenomas based on SS-TSE-DWI IVIM is feasible. Compared to normal pituitary glands, pituitary adenomas were characterized by lower D and f. KEY POINTS: • Assessment of microvessel perfusion of pituitary adenomas based on SS-TSE-IVIM is feasible. • SS-TSE-IVIM helps with evaluation of the vascularity of pituitary lesions. • Pituitary adenomas were characterized by lower D and f than normal pituitary glands.

Histogram analysis of amide proton transfer-weighted imaging: comparison of glioblastoma and solitary brain metastasis in enhancing tumors and peritumoral regions.

OBJECTIVES: Differentiation of glioblastomas (GBMs) and solitary brain metastases (SBMs) is an important clinical problem. The aim of this study was to determine whether amide proton transfer-weighted (APTW) imaging is useful for distinguishing GBMs from SBMs. METHODS: We examined 31 patients with GBM and 17 with SBM. For each tumor, enhancing areas (EAs) and surrounding non-enhancing areas with T2-prolongation (peritumoral high signal intensity areas, PHAs) were manually segmented using fusion images of the post-contrast T1-weighted and T2-weighted images. The mean amide proton transfer signal intensities (APTSIs) were compared among the EAs, PHAs, and contralateral normal appearing white matter (NAWM) within each tumor type. Furthermore, we analyzed APTSI histograms to compare the EAs and PHAs of GBMs and SBMs. RESULTS: In GBMs, the mean APTSI in EAs (2.92 ± 0.74%) was the highest, followed by that in PHAs (1.64 ± 0.83%, p < 0.001) and NAWM (0.43 ± 0.83%, p < 0.001). In SBMs, the mean APTSI in EAs (1.85 ± 0.99%) and PHAs (1.42 ± 0.45%) were significantly higher than that in NAWM (0.42 ± 0.30%, p < 0.001), whereas no significant difference was found between EAs and PHAs. The mean and 10th, 25th, 50th, 75th, and 90th percentiles for APT in EAs of GBMs were significantly higher than those of SBMs. However, no significant difference was found between GBMs and SBMs in any histogram parameters for PHA. CONCLUSIONS: APTSI in EAs, but not PHAs, is useful for differentiation between GBMs and SBMs. KEY POINTS: • Amide proton transfer-weighted imaging and histogram analysis in the enhancing tumor can provide useful information for differentiation between glioblastomas and solitary brain metastasis. • Amide proton transfer signal intensity histogram parameters from peritumoral areas showed no significant difference between glioblastomas and solitary brain metastasis. • Vasogenic edema alone can substantially increase amide proton transfer signal intensity which may mimic tumor invasion.