CT-based non-invasive identification of the most common gene mutation status in patients with non-small cell lung cancer.

BACKGROUND: Epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) are mutually exclusive, and they are two important genes that are most prone to mutation in patients with non-small cell lung cancer. PURPOSE: This retrospective study investigated the ability of radiomics to predict the mutation status of EGFR and KRAS in patients with non-small cell lung cancer (NSCLC) and guide precision medicine. METHODS: Computed tomography images of 1045 NSCLC patients from five different institutions were collected, and 1204 imaging features were extracted. In the training set (EGFR: 678, KRAS: 246), Max-Relevance and Min-Redundancy and least absolute shrinkage and selection operator logistic regression were used to screen radiomics features. The combination of selected radiomics features and clinical factors was used to establish the combined models in identifying EGFR and KRAS mutation status, respectively, through stepwise logistic regression. Then, on two independent external validation sets (EGFR: 203/164, KRAS: 123/95), the performance of each model was evaluated separately, and then the overall performance of predicting the two mutation states was calculated. RESULTS: In the EGFR and KRAS groups, radiomics signatures comprised 14 and 10 radiomics features, respectively. They were mutually exclusive between the tumors with positive EGFR mutation and those with positive KRAS mutation in imaging phenotype. For the EGFR group, the area under the curve (AUC) of the combined model in the two validation sets was 0.871 (95% CI: 0.821-0.926) and 0.861 (95% CI: 0.802-0.911), respectively, whereas the AUC of the combined model in the two validation sets was 0.798 (95% CI: 0.739-0.850) and 0.778 (95% CI: 0.735-0.821), respectively, for the KRAS group. Considering both EGFR and KRAS, the overall precision, recall, and F1-score of the combined model in the two validation sets were 0.704, 0.844, and 0.768, as well as 0.754, 0.693, and 0.722, respectively. CONCLUSIONS: Our study demonstrates the potential of radiomics in the non-invasive identification of EGFR and KRAS mutation status, which may guide patients with non-small cell lung cancer to choose the most appropriate personalized treatment. This method can be used when biopsy will bring unacceptable risk to patients with NSCLC.

Genistein Alleviates High-Fat Diet-Induced Obesity by Inhibiting the Process of Gluconeogenesis in Mice.

Genistein is an isoflavone phytoestrogen that has been shown to improve obesity; however, the underlying molecular mechanisms involved therein have not been clearly elucidated. In this study, we administered genistein to high-fat diet-induced obese mice to investigate its effect on hepatic gluconeogenesis. The results showed that genistein treatment significantly inhibited body weight gain, hyperglycemia, and adipose and hepatic lipid deposition in high-fat diet-induced obese mice. Glucose tolerance test (GTT), insulin tolerance test (ITT) and pyruvate tolerance test (PTT) showed that genistein treatment significantly inhibited gluconeogenesis and improved insulin resistance in obese mice. In addition, this study also found that genistein could promote the expression of miR-451 in vitro and in vivo, and the dual-luciferase reporter system showed that G6pc (glucose-6-phosphatase) may be a target gene of miR-451. Both genistein treatment and in vivo injection of miR-451 agomir significantly inhibited gluconeogenesis and inhibited the expression of G6pc and Gk (glycerol kinase, a known target gene of miR-451). In conclusion, genistein may inhibit gluconeogenesis in obese mice by regulating the expression of Gk and G6pc through miR-451. These results may provide insights into the functions of miR-451 and food-derived phytoestrogens in ameliorating and preventing gluconeogenesis-related diseases.

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma. METHODS: A total of 665 patients with lung adenocarcinoma (528/137) were recruited from two different institutions. In the training set, an 18-layer convolutional neural network (CNN) and fivefold cross-validation strategy were used to establish a CNN model. Subsequently, an independent external validation cohort from the other institution was used to evaluate the predictive efficacy of the CNN model. Grad-weighted class activation mapping (Grad-CAM) technology was used for the visual interpretation of the CNN model. In addition, this study also compared the prediction abilities of the radiomics and CNN models. Receiver operating characteristic (ROC) curves, accuracy and precision values, and recall and F1-score were used to evaluate the effectiveness of the CNN model and compare its performance with that of the radiomics model. RESULTS: In the validation set, the micro- and macroaverage values of the area under the ROC curve of the CNN model to identify the three EGFR subtypes were 0.78 and 0.79, respectively. All evaluation indicators of the CNN model were better than those of the radiomics model. CONCLUSIONS: Our study confirmed the potential of DL for predicting the EGFR mutation status in lung adenocarcinoma. The imaging phenotypes of the three mutation subtypes were found to be different, which can provide a basis for choosing more accurate and personalized treatment in patients with lung adenocarcinoma.