Objectives To analyse the radiological features of dynamic MRI and diffusion weighted imaging for atypical small hepatic hemangiomas (≤2 cm).Methods The MR images of 14 patients with 14 pathologically-confirmed small atypical hepatic hemangiomas were retrospectively analyzed.All these patients underwent MR imaging including plain T1 weighted imaging,T2 weighted imaging,dynamic contrast enhanced scanning,and diffusion weighted imaging.The signal-to-noise ratios of hemangiomas,the portal vein and the aorta,lesion-to-liver contrast-to-noise ratios,ADCs of hemangiomas and the liver,lesion-to-liver signal ratios in DWI were assessed to generalize the MRI features and the key points in differential diagnosis of this type of hepatic hemangioma.Results In dynamic contrast enhanced scanning,the atypical hemangiomas were barely enhanced and they were hypointense most of the time.The lesions might show a faint enhancement in the delayed phase.There were significant differences in the changes in signal-to-noise ratio between hemangiomas and aorta as well as portal vein in all the three phases (P＜0.05).In DWI,the signal intensities and ADCs of the hemangiomas were higher than the liver parenchyma (P＜0.01).Conclusions MRI dynamic contrast enhanced scanning,diffusion weighted imaging and evaluation of the ADCs were important in the diagnosis and differential diagnosis of small atypical hepatic hemangiomas.
Key words:
Liver; Hemangioma; Magnetic resonance imaging
Purpose: Liver imaging reporting and data system (LI-RADS) classification, especially the identification of LR-3 to 5 lesions with hepatocellular carcinoma (HCC) probability, is of great significance to treatment strategy determination. We aimed to develop a semi-automatic LI-RADS grading system on multiphase gadoxetic acid-enhanced MRI using deep convolutional neural networks (CNN). Patients and Methods: An internal data set of 439 patients and external data set of 71 patients with suspected HCC were included and underwent gadoxetic acid-enhanced MRI. The expert-guided LI-RADS grading system consisted of four deep 3D CNN models including a tumor segmentation model for automatic diameter estimation and three classification models of LI-RADS major features including arterial phase hyper-enhancement (APHE), washout and enhancing capsule. An end-to-end learning system comprising single deep CNN model that directly classified the LI-RADS grade was developed for comparison. Results: On internal testing set, the segmentation model reached a mean dice of 0.84, with the accuracy of mapped diameter intervals as 82.7% (95% CI: 74.4%, 91.7%). The area under the curves (AUCs) were 0.941 (95% CI: 0.914, 0.961), 0.859 (95% CI: 0.823, 0.890) and 0.712 (95% CI: 0.668, 0.754) for APHE, washout and capsule, respectively. The expert-guided system significantly outperformed the end-to-end system with a LI-RADS grading accuracy of 68.3% (95% CI: 60.8%, 76.5%) vs 55.6% (95% CI: 48.8%, 63.0%) ( P < 0.0001). On external testing set, the accuracy of mapped diameter intervals was 91.5% (95% CI: 81.9%, 100.0%). The AUCs were 0.792 (95% CI: 0.745, 0.833), 0.654 (95% CI: 0.602, 0.703) and 0.658 (95% CI: 0.606, 0.707) for APHE, washout and capsule, respectively. The expert-guided system achieved an overall grading accuracy of 66.2% (95% CI: 58.0%, 75.2%), significantly higher than the end-to-end system of 50.1% (95% CI: 43.1%, 58.1%) ( P < 0.0001). Conclusion: We developed a semi-automatic step-by-step expert-guided LI-RADS grading system (LR-3 to 5), superior to the conventional end-to-end learning system. This deep learning-based system may improve workflow efficiency for HCC diagnosis in clinical practice. Keywords: liver imaging reporting and data system, LI-RADS, hepatocellular carcinoma, HCC, magnetic resonance imaging, MRI, deep learning
Das fibrolamelläre Karzinom (FLC) ist eine seltene Form des hepatozellulären Karzinoms, das insbesondere junge lebergesunde Patienten betrifft. Aufgrund der Seltenheit und des im extrazellulären Kontrastmittel verstärkten MRT mit der fokalen nodulären Hyperplasie überlappenden Erscheinungsbilds sind verzögerte Diagnosen häufig. Ziel dieser Studie ist, die Bildeigenschaften des FLCs im Gadoxetsäure-verstärkten MRT der Leber unter besonderer Berücksichtigung der hepatobiliären Phase (HBP) zu analysieren.
Background Microvascular invasion (MVI) is a well‐established poor prognostic factor for hepatocellular carcinoma (HCC). Preoperative prediction of MVI is important for both therapeutic and prognostic purposes, but noninvasive methods are lacking. Purpose To develop an MR elastography (MRE)‐based nomogram for the preoperative prediction of MVI in HCC. Study Type Prospective. Subjects A total of 111 patients with surgically resected single HCC (52 MVI‐positive and 59 MVI‐negative), randomly allocated to training and validation cohorts (7:3 ratio). Field Strength/Sequence 2D‐MRE and conventional sequences (T1‐weighted in‐phase and opposed phase gradient echo, T2‐weighted fast spin echo, diffusion‐weighted single‐shot spin echo echo‐planar, and dynamic contrast‐enhanced T1‐weighted gradient echo) at 3.0 T. Assessment MRE‐stiffness and conventional qualitative and quantitative MRI features were evaluated and compared between MVI‐positive and MVI‐negative HCCs. Statistical Tests Univariable and multivariable logistic regression analyses were applied to identify potential predictors for MVI, and a nomogram was constructed according to the predictive model. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. Harrell's C‐index evaluated the discrimination performance of the nomogram, calibration curves analyzed its diagnostic performance and decision curve analysis determined its clinical usefulness. A P value <0.05 was considered statistically significant. Results Tumor stiffness >6.284 kPa (odds ratio [OR] = 24.38) and the presence of arterial peritumoral enhancement (OR = 6.36) were independent variables associated with MVI. The areas under the ROC curves for tumor stiffness were 0.81 (95% confidence interval [CI]: 0.70, 0.89) and 0.77 (95% CI: 0.60, 0.90) in the training and validation cohorts, respectively. When both predictive variables were integrated, the best nomogram performance was achieved with C‐indices of 0.88 (95% CI: 0.78, 0.94) and 0.87 (95% CI: 0.71, 0.96) in the two cohorts, fitting well in calibration curves. The decision curve exhibited optimal net benefit with a wide range of threshold probabilities for the nomogram. Data Conclusion An MRE‐based nomogram may be a potential noninvasive imaging biomarker for predicting MVI of HCC preoperatively. Evidence Level 2. Technical Efficacy Stage 2.
Objective: To investigate the diagnostic value of diffusion kurtosis imaging (DKI) histogram analysis in hepatic fibrosis staging.Materials and Methods: Thirty-six rats were divided into carbon tetrachloride-induced fibrosis groups (6 rats per group for 2, 4, 6, and 8 weeks) and a control group (n = 12).MRI was performed using a 3T scanner.Histograms of DKI were obtained for corrected apparent diffusion (D), kurtosis (K) and apparent diffusion coefficient (ADC).Mean, median, skewness, kurtosis and 25th and 75th percentiles were generated and compared according to the fibrosis stage and inflammatory activity.Results: A total of 35 rats were included, and 12, 5, 5, 6, and 7 rats were diagnosed as F0-F4.The mean, median, 25th and 75th percentiles, kurtosis of D map, median, 25th percentile, skewness of K map, and 75th percentile of ADC map demonstrated significant correlation with fibrosis stage (r = -0.767 to 0.339, p < 0.001 to p = 0.039).The fibrosis score was the independent variable associated with histogram parameters compared with inflammatory activity grade (p < 0.001 to p = 0.041), except the median of K map (p = 0.185).Areas under the receiver operating characteristic curve of D were larger than K and ADC maps in fibrosis staging, although no significant differences existed in pairwise comparisons (p = 0.0512 to p = 0.847). Conclusion:Corrected apparent diffusion of DKI histogram analysis provides added value and better diagnostic performance to detect various liver fibrosis stages compared with ADC.
Abstract Background and objectives To investigate the different features between metastatic lymph node and nonmetastatic lymph node on magnetic resonance imaging (MRI) and the relationship between the rectal lesion and lymph node metastasis (LNM). Methods Eighty‐two patients with retrospectively consecutive pT1‐2 stage rectal cancer in 2016 were divided into lymph node metastasis (LNM+) and lymph node nonmetastasis (LNM−) group based on their histopathologic examinations. We evaluated the following features of lymph nodes: number, shape, signal heterogeneity, border, and diameter of the largest lymph node on T2‐weight images. We also calculated tumor apparent diffusion coefficient ratio and tumor percent enhancement. Fisher’s exact test was applied for inspecting lymph node numbers on MRI and logistic regression analysis in examining risk factors for LNM. Results The MR‐LN number was significantly different between the LNM+ and LNM− group (median: 4 vs 1, P = 0.001). Multivariate logistic regression analysis exhibited that the diameter of the largest lymph node and the tumor percent enhancement of the arterial phase were independent risk factors of LNM ( P = 0.005 vs 0.021, respectively). Conclusions The largest lymph node’s diameter and the tumor percent enhancement of arterial phase on MRI were helpful in determining LNM in pT1‐2 rectal cancer.
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