<b><i>Background:</i></b> Radiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). <b><i>Methods:</i></b> Two hundred and eight patients with pathologically confirmed HCC (training cohort: <i>n</i> = 146; validation cohort: <i>n</i> = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility. <b><i>Results:</i></b> Higher α-fetoprotein level (<i>p</i> = 0.046), nonsmooth tumor margin (<i>p</i> = 0.003), arterial peritumoral enhancement (<i>p &#x3c;</i> 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (<i>p &#x3c;</i> 0.001) and HBP T1 maps (<i>p &#x3c;</i> 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; <i>p</i> = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; <i>p</i> = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895–0.976) and 0.864 (95% CI 0.761–0.967) in the training and validation cohort, fitting well in calibration curves (<i>p</i> &#x3e; 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. <b><i>Conclusions:</i></b> The nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Purpose To evaluate the potential role of diffusion kurtosis imaging and conventional magnetic resonance (MR) imaging findings including standard monoexponential model of diffusion-weighted imaging and morphologic features for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Materials and Methods Institutional review board approval and written informed consent were obtained. Between September 2015 and November 2016, 84 patients (median age, 54 years; range, 29-79 years) with 92 histopathologically confirmed HCCs (40 MVI-positive lesions and 52 MVI-negative lesions) were analyzed. Preoperative MR imaging examinations including diffusion kurtosis imaging (b values: 0, 200, 500, 1000, 1500, and 2000 sec/mm) were performed and kurtosis, diffusivity, and apparent diffusion coefficient maps were calculated. Morphologic features of conventional MR images were also evaluated. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of these parameters as potential predictors of MVI. Results Features significantly related to MVI of HCC at univariate analysis were increased mean kurtosis value (P < .001), decreased mean diffusivity value (P = .033) and apparent diffusion coefficient value (P = .011), and presence of infiltrative border with irregular shape (P = .005) and irregular circumferential enhancement (P = .026). At multivariate analysis, mean kurtosis value (odds ratio, 6.25; P = .001), as well as irregular circumferential enhancement (odds ratio, 6.92; P = .046), were independent risk factors for MVI of HCC. The mean kurtosis value for MVI of HCC showed an area under the receiver operating characteristic curve of 0.784 (optimal cutoff value was 0.917). Conclusion Higher mean kurtosis values in combination with irregular circumferential enhancement are potential predictive biomarkers for MVI of HCC. RSNA, 2017.

Objectives To develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm. Methods Between March 2012 and September 2019, 356 patients with pathologically confirmed solitary HCC ≤ 5 cm who underwent preoperative gadoxetate disodium–enhanced MRI were retrospectively enrolled. MVI was graded as M0, M1, or M2 according to the number and distribution of invaded vessels. Radiomics features were extracted from DWI, arterial, portal venous, and hepatobiliary phase images in regions of the entire tumor, peritumoral area ≤ 10 mm, and randomly selected liver tissue. Multivariate analysis identified the independent predictors for MVI and RFS, with nomogram visualized the ultimately predictive models. Results Elevated alpha-fetoprotein, total bilirubin and radiomics values, peritumoral enhancement, and incomplete or absent capsule enhancement were independent risk factors for MVI. The AUCs of MVI nomogram reached 0.920 (95% CI: 0.861–0.979) using random forest and 0.879 (95% CI: 0.820–0.938) using logistic regression analysis in validation cohort (n = 106). With the 5-year RFS rate of 68.4%, the median RFS of MVI-positive (M2 and M1) and MVI-negative (M0) patients were 30.5 (11.9 and 40.9) and > 96.9 months (p < 0.001), respectively. Age, histologic MVI, alkaline phosphatase, and alanine aminotransferase independently predicted recurrence, yielding AUC of 0.654 (95% CI: 0.538–0.769, n = 99) in RFS validation cohort. Instead of histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest achieved comparable accuracy in MVI stratification and RFS prediction. Conclusions Preoperative radiomics-based nomogram using random forest is a potential biomarker of MVI and RFS prediction for solitary HCC ≤ 5 cm. Key Points • The radiomics score was the predominant independent predictor of MVI which was the primary independent risk factor for postoperative recurrence. • The radiomics-based nomogram using either random forest or logistic regression analysis has obtained the best preoperative prediction of MVI in HCC patients so far. • As an excellent substitute for the invasive histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest (MVI-RF) achieved comparable accuracy in MVI stratification and outcome, reinforcing the radiologic understanding of HCC angioinvasion and progression.

In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought by the visual context. To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations. To alleviate the visual bias, we further propose to leverage purely text-based entity span detection as an auxiliary module, and design a Unified Multimodal Transformer to guide the final predictions with the entity span predictions. Experiments show that our unified approach achieves the new state-of-the-art performance on two benchmark datasets.

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Diabetes mellitus affects the brain. Both type 1 and type 2 diabetic patients are associated with white matter (WM) damage observable to diffusion tensor imaging (DTI). The underlying histopathological mechanisms, however, are poorly understood. The objectives of this study are 1) to determine whether streptozotocin (STZ)-induced type 1 diabetes is associated with WM damage observable to DTI; and 2) to understand the pathophysiological aspects underlying STZ-induced brain injuries. Male Sprague–Dawley rats received a single intraperitoneal injection of STZ (62 mg/kg). DTI was used to assess brain abnormalities at 4 weeks after induction, combined with histological assessments and ultrastructural analysis. Compared to controls, the STZ-induced rats showed significantly reduced fractional anisotropy (FA) in the motor/somatosensory cortex and striatum. Histologically, the cortex and striatum of the diabetic animals are characterized by demyelination and axonal degradation. In conclusion, STZ-induced diabetes is associated with striatal/cortical injuries observable to DTI. The DTI abnormalities are likely manifestations of demyelination and axonal degradation in the affected brain regions, and can potentially be used as surrogates for evaluating diabetic brain injuries.

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Only a few cases of extranodal Epstein-Barr virus (EBV)-associated B-cell lymphomas arising from patients with angioimmunoblastic T-cell lymphoma (AITL) have been described. We report a case of AITL of which secondary cutaneous EBV-associated diffuse large B-cell lymphoma (DLBCL) developed after the initial diagnosis of AITL. A 65-year-old Chinese male patient was diagnosed as AITL based on typical histological and immunohistochemical characteristics in biopsy of the enlarged right inguinal lymph nodes. The patient initially received 6 cycles of chemotherapy with CHOP regimen (cyclophosphamide, vincristine, adriamycin, prednisone), but his symptoms did not disappear. Nineteen months after initial diagnosis of AITL, the patient was hospitalized again because of multiple plaques and nodules on the skin. The skin biopsy was performed, but this time the tumor was composed of large, polymorphous population of lymphocytes with CD20 and CD79a positive on immunohistochemical staining. The tumor cells were strong positive for EBER by in situ hybridization. The findings of skin biopsy were compatible with EBV-associated DLBCL. CHOP-R chemotherapy (cyclophosphamide, doxorubicin, vincristine, prednisone and rituximab) was then administered, resulting in partial response of the disease with pancytopenia and suppression of cellular immunity. To our knowledge, this is the first case of cutaneous EBV-associated DLBCL originated from AITL in Chinese pepole. We suggest the patients with AITL should perform lymph node and skin biopsies regularly in the course of the disease to detect the progression of secondary lymphomas.Virtual slidesThe virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/1197421158639299