Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors.

OBJECTIVES: To establish and validate a radiomics nomogram model for preoperative prediction of KIT exon 9 mutation status in patients with gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS: Eighty-seven patients with pathologically confirmed GISTs were retrospectively enrolled in this study. Imaging and clinicopathological data were collected and randomly assigned to the training set (n = 60) and test set (n = 27) at a ratio of 7:3. Based on contrast-enhanced CT (CE-CT) arterial and venous phase images, the region of interest (ROI) of the tumors were manually drawn layer by layer, and the radiomics features were extracted. The intra-class correlation coefficient (ICC) was used to test the consistency between observers. Least absolute shrinkage and selection operator regression (LASSO) were used to further screen the features. The nomogram of integrated radiomics score (Rad-Score) and clinical risk factors (extra-gastric location and distant metastasis) was drawn on the basis of multivariate logistic regression. The area under the receiver operating characteristic (AUC) curve and decision curve analysis were used to evaluate the predictive efficiency of the nomogram, and the clinical benefits that the decision curve evaluation model may bring to patients. RESULTS: The selected radiomics features (arterial phase and venous phase features) were significantly correlated with the KIT exon 9 mutation status of GISTs. The AUC, sensitivity, specificity, and accuracy in the radiomics model were 0.863, 85.7%, 80.4%, and 85.0% for the training group (95% confidence interval [CI]: 0.750-0.938), and 0.883, 88.9%, 83.3%, and 81.5% for the test group (95% CI: 0.701-0.974), respectively. The AUC, sensitivity, specificity, and accuracy in the nomogram model were 0.902 (95% confidence interval [CI]: 0.798-0.964), 85.7%, 86.9%, and 91.7% for the training group, and 0.907 (95% CI: 0.732-0.984), 77.8%, 94.4%, and 88.9% for the test group, respectively. The decision curve showed the clinical application value of the radiomic nomogram. CONCLUSION: The radiomics nomogram model based on CE-CT can effectively predict the KIT exon 9 mutation status of GISTs and may be used for selective gene analysis in the future, which is of great significance for the accurate treatment of GISTs.

PI3K δ inhibitor PI-3065 induces apoptosis in hepatocellular carcinoma cells by targeting survivin.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and its clinical treatment remains challenging. The development of new treatment regimens is important for effective HCC treatment. Phosphoinositide 3-kinase (PI3K) is a lipid kinase that plays an important role in cell growth and metabolism and is overexpressed in nearly 50% of patients with HCC. Studies have shown that PI-3065, a small-molecule inhibitor of phosphatidylinositol 3-kinase delta, significantly inhibits solid breast cancer. However, its antitumor effects against HCC and the underlying mechanisms remain unclear. In the present study, we found that PI-3065 dose- and time-dependently reduced HCC cell viability and induced apoptosis while posing no obvious apoptotic toxicity in normal liver cells. Further mechanistic analysis showed that PI-3065 induced apoptosis mainly by inhibiting survivin protein expression, decreasing mitochondrial membrane potential, and promoting cytochrome C release. Simultaneously, PI-3065 markedly suppressed the colony formation, migration, and epithelial-mesenchymal transition abilities of HCC cells. Furthermore, transplantation of nude mice with HCC tumors showed that PI-3065 inhibits HCC tumor growth in vivo by targeting survivin. In summary, PI-3065 specifically inhibited survivin expression and exerted anti-HCC activity in vivo and in vitro, suggesting that it may serve as an effective antitumor drug for HCC treatment, which warrants further study.