Preoperative prediction of microvascular invasion classification in hepatocellular carcinoma based on clinical features and MRI parameters.

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical pathological factor and the degree of MVI influences treatment decisions and patient prognosis. The present study aimed to predict the MVI classification based on preoperative MRI features and clinical parameters. The present retrospective cohort study included 150 patients (training cohort, n=108; validation cohort, n=42) with pathologically confirmed HCC. Clinical and imaging characteristics data were collected from Shengli Oilfield Central Hospital (Dongying, China). Univariate and multivariate logistic regression analyses were conducted to assess the association of clinical variables and MRI parameters with MVI (grade M1 and M2) and the M2 classification. Nomograms were developed based on the predictive factors of MVI and the M2 classification. The discrimination capability, calibration and clinical usefulness of the nomograms were evaluated. Multivariate analysis revealed an association between the Lens culinaris agglutinin-reactive fraction of α-fetoprotein, protein induced by vitamin K absence-II and tumor margin and MVI-positive status, while peritumoral enhancement and tumor size were demonstrated to be marginal predictors, but were also included in the nomogram. However, among MVI-positive patients, only peritumoral hypointensity and tumor size were demonstrated to be risk factors for the M2 classification. The nomograms, incorporating these variables, exhibited a strong ability to discriminate between MVI-positive and MVI-negative patients with HCC in both the training and validation cohort [area under the curve (AUC), 0.877 and 0.914, respectively] and good performance in predicting the M2 classification in the training and validation cohorts (AUC, 0.720 and 0.782, respectively). Nomograms incorporating clinical parameters and preoperative MRI features demonstrated promising potential as straightforward and effective tools for predicting MVI and the M2 classification in patients with HCC. Such predictive tools could aid in the judicious selection of optimal clinical treatments.

Role of the Clinical Features and MRI Parameters on Ki-67 Expression in Hepatocellular Carcinoma Patients: Development of a Predictive Nomogram.

PURPOSE: To develop a nomogram using clinical features and the MRI parameters for preoperatively predicting the expression of Ki-67 in patients with hepatocellular carcinoma (HCC). METHODS: One hundred and forty patients (training cohorts: n = 108; validation cohorts: n = 32) with confirmed HCC were investigated. Mann-Whitney U test, independent sample t-test, and chi-squared test were used to analyze the continuous and categorical variables. Univariate and multivariate logistic regression analyses were performed to examine the clinical variables and parameters from MRI associated with Ki-67 expression. As a result, a nomogram was developed based on these associations in patients with HCC. The performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves. RESULTS: In the training set, multivariable logistic regression analysis revealed that lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3) levels, protein induced by vitamin K absence or antagonist-II (PIVKA-II) levels, and tumor shape were independent predictors for Ki-67 expression (p < 0.05). These three variables and the apparent diffusion coefficient (ADC) value were used to establish a nomogram, while the ADC value was found to be a marginal significant predictor. The model demonstrated a strong ability to discriminate Ki-67 expression in both the training and validation cohorts (AUC = 0.862, 0.877). CONCLUSION: A non-invasive preoperative prediction method, which incorporates MRI variables and clinical features was developed, and showed effectiveness in evaluating Ki-67 expression in HCC patients.

Differentiation between pancreatic metastases from clear cell renal cell carcinoma and pancreatic neuroendocrine tumor using double-echo chemical shift imaging.

PURPOSE: The purpose of the study was to retrospectively analyze whether double-echo gradient-echo (GRE) chemical shift imaging (CSI) can differentiate between pancreatic metastases from clear cell renal cell carcinoma (PM-ccRCC) and pancreatic neuroendocrine tumor (pNET). METHODS: Institutional review board approval and informed consent were waived. CSI, T2WI, DWI, and DCE magnetic resonance imaging (MRI) were performed in patients with PM-ccRCC and pNET. Eleven patients with PM-ccRCC and 24 patients with pNET were enrolled into this retrospective study. The signal intensity was measured in the pancreatic tumor and spleen on in-phase and opposed-phase images. The signal intensity index (SII) and tumor-to-spleen ratio (TSR) in PM-ccRCC and pNET were calculated and compared. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of SII and TSR in the differentiation between PM-ccRCC and pNET. RESULTS: The SII between PM-ccRCC and pNET (20.3% ± 16.8% vs. - 3.2% ± 11.4%) was significantly different (P < 0.001), as was the TSR (- 19.2% ± 16.6% vs. 6.0% ± 13.8%) (P < 0.001). The area under the ROC curve was 0.917 for the SII and 0.902 for the TSR. Additionally, an SII threshold value of 8.1% permitted the differentiation of PM-ccRCC from pNET with a sensitivity of 90.9%, a specificity of 91.7%, a positive predictive value of 90.1%, a negative predictive value of 91.7%, and an accuracy of 91.4%. A TSR cut-off value of - 4.7% enabled the differentiation of the two groups with a sensitivity of 79.2%, a specificity of 90.9%, a positive predictive value of 90.9%, a negative predictive value of 79.2% and an accuracy of 82.9%. CONCLUSION: Double-echo GRE chemical shift MR imaging can accurately differentiate between PM-ccRCC and pNET.