Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study.

PURPOSE: This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis. METHODS: Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models. RESULTS: The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897. CONCLUSION: nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index.

BACKGROUND: The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor. PURPOSE: To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index. MATERIAL AND METHODS: The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index. RESULTS: The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade. CONCLUSION: Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Clinico-radiological nomogram for preoperatively predicting post-resection hepatic metastasis in patients with gastric adenocarcinoma.

OBJECTIVE: To establish and validate a model comprising clinical and radiological features to pre-operatively predict post-resection hepatic metastasis (HM) in patients with gastric adenocarcinoma (GAC). METHODS: We retrospectively analyzed 461 patients (HM, 106 patients); and non-metastasis (NM, 355 patients) who were confirmed to have GAC post-surgery. The patients were randomly divided into the training (n = 307) and testing (n = 154) cohorts in a 2:1 ratio. The main clinical risk factors were filtered using the least absolute shrinkage and selection operator algorithm according to their diagnostic value. The selected factors were then used to establish a clinical-radiological model using stepwise logistic regression. The Akaike's information criterion and receiver operating characteristic (ROC) analyses were used to evaluate the prediction performance of the model. RESULTS: Logistic regression analysis showed that the peak enhancement phase, tumor location, alpha-fetoprotein, cancer antigen (CA)-125, CA724 levels, CT-based Tstage and arterial phase CT values were important independent predictors. Based on these predictors, the areas under the ROC curve of the training and testing cohorts were 0.864 and 0.832, respectively, for predicting post-operative HM. CONCLUSION: This study built a synthetical nomogram using the pre-operative clinical and radiological features of patients to predict the likelihood of HM occurring after GAC surgery. It may help guide pre-operative clinical decision-making and benefit patients with GAC in the future. ADVANCES IN KNOWLEDGE: 1. The combination of clinical risk factors and CT imaging features provided useful information for predicting HM in GAC.2. A clinicoradiological nomogram is a tool for the pre-operative prediction of HM in patients with GAC.