Contract grant sponsorNational Natural Science Foundation of China; Contract grant number: 81260214.BackgroundRecent studies have highlighted the diagnostic value of Gadolinium‐ethoxybenzyl‐diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced MRI in small hepatocellular carcinoma (HCC). Ki67 and CD34 are histologic markers that reflect the proliferation of tumor cells and the microvascular density (MVD).PurposeTo explore the diagnostic value of Gd‐EOB‐DTPA‐enhanced MRI for Ki67 expression and MVD in HCC.Study TypeRetrospective.SubjectsIn all, 180 patients with HCC.Field Strength/Sequence3.0T, Gd‐EOB‐DTPA‐enhanced T1WI volumetric interpolated breath‐hold examination (VIBE) axial fat suppression plain, and enhanced scanning.AssessmentThe T1 relaxation time (T1rt) and signal intensity (SI) of the lesion were measured. The Ki67 expressions and MVD were evaluated by immunohistochemistry.Statistical TestReceiver operating characteristic (ROC) curves were used to analyze the diagnostic efficacy of T1rt for high Ki67 expression (≥50%) and high MVD (≥100).ResultsThe T1rt‐20min, rrT1rt‐20min, and SI‐hepatobiliary phase (SI‐HBP) were strongly correlated with Ki67, the r values were 0.846, –0.765, and –0.760 (P < 0.05), respectively. There were moderate correlations with CD34, with r values –0.444, 0.336, and –0.463 (P < 0.05), respectively. The T1rt‐Pre, T1rt‐20min, SI‐Pre, and SI‐HBP were significantly different both between the high and low ki67 expression groups (P < 0.05) and between the high MVD and low MVD groups (P < 0.05). In the two groups the T1rt‐20min and SI‐HBP was 800.06 ± 128.91 vs. 530.06 ± 139.29 (P < 0.05) and 122.29 ± 39.39 vs. 173.49 ± 46.15 (P < 0.05); T1rt‐20min was found to have high diagnostic efficiency for high ki67 expression (area under the curve [AUC], 0.937; P < 0.05) T1rt‐20min had moderate diagnostic value for high MVD (AUC, 0.716; P < 0.05).Data ConclusionThe T1rt and SI of Gd‐EOB‐DTPA‐enhanced MRI were correlated with Ki67 expression and MVD. T1rt‐20min has a high diagnostic value for high ki67 expression and high MVD in HCC tissues.Level of Evidence: 3Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;51:1755–1763.

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Background Recent studies have highlighted the correlation between diabetes and pancreatic fat infiltration. Notably, pancreatic fat content (PFC) is a potential biomarker in diabetic patients, and magnetic resonance imaging (MRI) provides an effective method for noninvasive assessment of pancreatic fat infiltration. However, most reports of quantitative measurement of pancreatic fat have lacked comparisons of pathology results. The primary objective of this study was to determine the feasibility and accuracy of pancreatic MRI by using pancreatic fat fraction (PFF) measurements with the IDEAL-IQ sequence; the secondary objective was to explore changes in PFC between pigs with and without diabetes. Methods In this prospective study, 13 Bama Mini-pigs (7 females, 6 males; median age, 2 weeks) were randomly assigned to diabetes ( n = 7) or control ( n = 6) groups. Pigs in the diabetes group received high fat/high sugar feed, combined with streptozotocin injections. At the end of 15 months, biochemical changes were evaluated. All pigs underwent axial MRI with the IDEAL-IQ sequence to measure PFF; PFC of fresh pancreatic parenchyma was measured by the Soxhlet extraction method; and pancreatic fat distribution was observed by histopathology. Results of all analyses were compared between the diabetes and control groups by using the Mann-Whitney U-test. Correlations of PFF and PFC, fasting blood glucose (GLU), and serum insulin (INS) were calculated by using the Spearman correlation coefficient. Single-measure intraclass correlation coefficient (ICC) was used to assess interreader agreement. Results There were significant differences between diabetes and control groups: GLU (mmol/L) was 18.06 ± 6.03 and 5.06 ± 1.41 ( P < 0.001); INS (mU/L) was 21.59 ± 2.93 and 29.32 ± 3.27 ( P = 0.003); PFC (%) was 34.60 ± 3.52 and 28.63 ± 3.25 ( P = 0.027); and PFF (%) was 36.51 ± 4.07 and 27.75 ± 3.73 ( P = 0.003). There was a strongly positive correlation between PFF and PFC (r = 0.934, P < 0.001); there were moderate correlations between PFF and GLU (r = 0.736, P = 0.004; positive correlation), and between PFF and INS (r = − 0.747, P = 0.003; negative correlation). Excellent interreader agreement was observed for PFF measurements (ICC, 0.954). Conclusions Pancreatic fat infiltration shows a clear association with diabetes. MRI with the IDEAL-IQ sequence can be used to accurately and reproducibly quantify PFC.

Background Recent studies have highlighted the correlation between diabetes and pancreatic fat infiltration. However, pancreatic fat content (PFC) is rarely confirmed by pathological results, and a change of PFC during progression of type 2 diabetes (T2DM) is currently controversial. Purpose To evaluate the relationship of MRI‐pancreatic proton density fat fraction to serologic changes and histology in an experimental model of diabetes. Study Type Prospective animal study. Animal Model Thirteen Bama pigs were randomly assigned to diabetes (n = 7) or control (n = 6) groups. Pigs in the diabetic group received high‐fat/high‐sugar feed, combined with three doses of streptozotocin injections. Field Strength/Sequence 3.0T, IDEAL‐IQ sequence. Assessment Starting in the fifth month, biochemical changes were evaluated; all pigs underwent axial MRI with the IDEAL‐IQ sequence to measured pancreatic fat fraction (PFF). PFC was measured by the Soxhlet extraction method. Pancreatic fat distribution and pancreas islet morphology were observed by histopathology. Statistical Tests A Mann–Whitney U‐test, independent‐samples t‐test, Pearson correlation, Spearman correlation, single‐measure intraclass correlation coefficient (ICC) were performed. Results During the development of T2DM, the PFF, weight, fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TCHO), low‐density lipoprotein (LDL), and HOMA‐IR (insulin resistance) of the experimental group showed an upward trend; fasting insulin (INS), high‐density lipoprotein (HDL), and HOMA‐β showed decreasing trends. At the end of the fifteenth month, FBG (mmol/L) was 18.06 ± 6.03 and 5.06 ± 1.41 (P < 0.001), PFF (%) was 36.52 ± 4.07 and 27.75 ± 3.73 (P = 0.002), INS (mU/L) was 21.59 ± 2.93 and 29.32 ± 3.27 (P = 0.001), HOMA‐IR was 16.83 ± 4.22 and 6.70 ± 2.45 (P < 0.001), HOMA‐β was 1.50 ± 0.24 and 2.77 ± 0.45 (P < 0.001), between the experimental and control groups. There were strong and moderate positive correlations between PFF and PFC (r = 0.968, P < 0.001), and FBG (r = 0.657, P = 0.015), and HOMA‐IR (r = 0.608, P = 0.028). Data Conclusion MRI‐proton density fat fraction can measure the fat content of the pancreas with great accuracy and repeatability; PFF is a potential biomarker that can reflect the different stages of diabetes development. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1905–1913.

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BACKGROUND Microvascular invasion (MVI) of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) is an independent prognostic factor for poor progression-free and overall survival. Radiomics can help extract imaging information associated with tumor pathophysiology. AIM To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in sHCC. METHODS In total, 415 patients were diagnosed with sHCC by postoperative pathology. A total of 221 patients were retrospectively included from our hospital. In addition, we recruited 94 and 100 participants as independent external validation sets from two other hospitals. Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging (DWI) were constructed and validated using machine learning. As presented in the radiomics nomogram, a prediction model was developed using multivariable logistic regression analysis, which included radiomics scores, radiologic features, and clinical features, such as the alpha-fetoprotein (AFP) level. The calibration, decision-making curve, and clinical usefulness of the radiomics nomogram were analyzed. The radiomic nomogram was validated using independent external cohort data. The areas under the receiver operating curve (AUC) were used to assess the predictive capability. RESULTS Pathological examination confirmed MVI in 64 (28.9%), 22 (23.4%), and 16 (16.0%) of the 221, 94, and 100 patients, respectively. AFP, tumor size, non-smooth tumor margin, incomplete capsule, and peritumoral hypointensity in hepatobiliary phase (HBP) images had poor diagnostic value for MVI of sHCC. Quantitative radiomic features (1409) of MRI scans) were extracted. The classifier of logistic regression (LR) was the best machine learning method, and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set (hospital A) and validation set (hospital B, C). The AUC of HBP was 0.979, 0.970, and 0.803, respectively, and the AUC of DWI was 0.971, 0.816, and 0.801 ( P < 0.05), respectively. Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts (C-index of HBP and DWI were 0.971, 0.912, 0.808, and 0.970, 0.843, 0.869, respectively). The clinical usefulness of the nomogram was further confirmed using decision curve analysis. CONCLUSION AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC. Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI. The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.

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