A gadoxetic acid-enhanced MRI-based model using LI-RADS v2018 features for preoperatively predicting Ki-67 expression in hepatocellular carcinoma.

PURPOSE: To construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features. METHODS: A total of 121 patients with HCC who underwent EOB-MRI were enrolled in this study. The patients were divided into three groups according to Ki-67 cut-offs: Ki-67 ≥ 20% (n = 86) vs. Ki-67 < 20% (n = 35); Ki-67 ≥ 30% (n = 73) vs. Ki-67 < 30% (n = 48); Ki-67 ≥ 50% (n = 45) vs. Ki-67 < 50% (n = 76). MRI features were analyzed to be associated with high Ki-67 expression using logistic regression to construct multivariable models. The performance characteristic of the models for the prediction of high Ki-67 expression was assessed using receiver operating characteristic curves. RESULTS: The presence of mosaic architecture (p = 0.045), the presence of infiltrative appearance (p = 0.039), and the absence of targetoid hepatobiliary phase (HBP, p = 0.035) were independent differential factors for the prediction of high Ki-67 status (≥ 50% vs. < 50%) in HCC patients, while no features could predict high Ki-67 status with thresholds of 20% (≥ 20% vs. < 20%) and 30% (≥ 30% vs. < 30%) (p > 0.05). Four models were constructed including model A (mosaic architecture and infiltrated appearance), model B (mosaic architecture and targetoid HBP), model C (infiltrated appearance and targetoid HBP), and model D (mosaic architecture, infiltrated appearance and targetoid HBP). The model D yielded better diagnostic performance than the model C (0.776 vs. 0.669, p = 0.002), but a comparable AUC than model A (0.776 vs. 0.781, p = 0.855) and model B (0.776 vs. 0.746, p = 0.076). CONCLUSIONS: Mosaic architecture, infiltrated appearance and targetoid HBP were sensitive imaging features for predicting Ki-67 index ≥ 50% and EOB-MRI model based on LI-RADS v2018 features may be an effective imaging approach for the risk stratification of patients with HCC before surgery.

Prognostic Impact of Quantifying Sarcopenia and Adipopenia by Chest CT in Severe Aplastic Anemia Patients Treated With Allogeneic Hematopoietic Stem Cell Transplantation.

RATIONALE AND OBJECTIVES: To investigate the prognostic role of chest CT-defined sarcopenia and adipopenia in severe aplastic anemia (SAA) patients treated with hematopoietic stem cell transplantation (HSCT). MATERIALS AND METHODS: This was a retrospective study of 123 consecutive SAA patients treated with HSCT. CT imaging was performed to quantify the pectoralis muscle (including major and minor) index (PMI) and the corresponding subcutaneous adipose tissue index (SAI). Sarcopenia and adipopenia were defined as PMI and SAI lower than the respective sex-specific medians. Correlations of the PMI and SAI with anthropometric indexes were calculated. Transplant-related outcomes were compared between the sarcopenia and adipopenia groups. Prognostic factors for overall survival (OS) and fail-free survival (FFS) were identified by Cox regression and were used to create a nomogram. The accuracy of the nomogram was evaluated by ROC curves. RESULTS: PMI showed good correlation with BMI and fat-free mass index (p < 0.001). SAI correlated with BMI and fat mass index (p < 0.001). The sarcopenia group (47.2%) had a significantly worse 3-year OS (90.8% vs. 77.6%, p = 0.045) and 3-year FFS (89.2% vs. 74.1%, p = 0.035) than the nonsarcopenia group. Sarcopenia status and diagnostic category were used to construct the nomogram of OS, as these were independent prognostic factors in the multivariate analysis for OS and FFS (p < 0.05). The area under the curve of the nomogram at one year and three years was 0.801 and 0.721, respectively. CONCLUSION: Sarcopenia indicates a poor prognosis in SAA patients undergoing HSCT. Intensive supportive care is suggested for SAA patients with sarcopenia before transplantation.

A gadoxetic acid-enhanced MRI-based multivariable model using LI-RADS v2018 and other imaging features for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma.

PURPOSE: To identify imaging features of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) using LI-RADS v2018 and other imaging features and to develop a gadoxetic acid-enhanced MRI (EOB-MRI)-based model for pretreatment prediction of MTM-HCC. MATERIALS AND METHODS: A total of 93 patients with pathologically proven HCC (39 MTM-HCC and 54 non-MTM-HCC) were retrospectively evaluated with EOB-MRI at 3 T. Imaging analysis according to LI-RADS v2018 was evaluated by two readers. Univariate and multivariate analyses were performed to determine independent predictors for MTM-HCC. Different logistic regression models were built based on MRI features, including model A (enhancing capsule, blood products in mass and ascites), model B (enhancing capsule and ascites), model C (blood products in mass and ascites), and model D (blood products in mass and enhancing capsule). Diagnostic performance was assessed by receiver operating characteristic (ROC) curves. RESULTS: After multivariate analysis, absence of enhancing capsule (odds ratio = 0.102, p = 0.010), absence of blood products in mass (odds ratio = 0.073, p = 0.030), and with ascites (odds ratio = 55.677, p = 0.028) were identified as independent differential factors for the presence of MTM-HCC. Model A yielded a sensitivity, specificity, and AUC of 35.90% (21.20,52.80), 94.44% (84.60, 98.80), and 0.731 (0.629, 0.818). Model A achieved a comparable AUC than model D (0.731 vs. 0.699, p = 0.333), but a higher AUC than model B (0.731 vs. 0.644, p = 0.048) and model C (0.731 vs. 0.650, p = 0.005). CONCLUSION: The EOB-MRI-based model is promising for noninvasively predicting MTM-HCC and may assist clinicians in pretreatment decisions.