Background. Numerous studies have shown that hepatocellular carcinoma (HCC) without microvascular invasion (MVI) may have better outcomes. This study established a preoperative MVI risk nomogram mainly incorporating three related risk factors of MVI in BCLC 0/A HCC after surgery. Methods. Independent predictors for the risk of MVI were investigated, and an MVI risk nomogram was established based on 60 patients in the training group who underwent curative hepatectomy for BCLC 0/A HCC and validated using a dataset in the validation group. Results. Univariate analysis in the training group showed that hepatitis viral B (HBV) DNA (P=0.034), tumor size (P<0.001), CT value in the venous phase (P=0.039), CT value in the delayed phase (P=0.017), peritumoral enhancement (P=0.013), visible small blood vessels in the arterial phase (P=0.002), and distance from the tumor to the inferior vena cava (IVC) (DTI, P=0.004) were risk factors significantly associated with the presence of MVI. According to multivariate analysis, the independent predictive factors of MVI, including tumor size (P=0.002), CT value in the delayed phase (P=0.018), and peritumoral enhancement (P=0.057), were incorporated in the corresponding nomogram. The nomogram displayed an unadjusted C-index of 0.851 and a bootstrap-corrected C-index of 0.832. Calibration curves also showed good agreement on the presence of MVI. ROC curve analyses showed that the nomogram had a large AUC (0.851). Conclusions. The proposed nomogram consisting of tumor size, CT value in the delayed phase, and peritumoral enhancement was associated with MVI risk in BCLC 0/A HCC following curative hepatectomy.

Background. To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better. Materials and Methods. We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDHwt, 16cases); IDH mutant with the intact 1p or 19q group (IDHmut/1p19q+, 18cases); and IDH mutant with the 1p/19q codeleted group (IDHmut/1p19q−, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent t test or Mann–Whitney U test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden’s index. Area under the curve (AUC) results were compared using the method of Delong et al. Results. The inhomogenity from the two different ROI methods for distinguishing IDHwt gliomas from IDHmut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDHmut/1p19q− tumors and IDHmut/1p19q+ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods. Conclusion. ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas.