Objective:To construct and validate a nomogram model based on clinical factors and PET/CT metabolic parameters of 18F-FDG for predicting epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. Methods:From January 2014 to January 2019, 114 patients (59 males, 55 females, age (60.0±10.8) years) with lung adenocarcinoma in the First Affiliated Hospital of Harbin Medical University were retrospectively enrolled. Clinical data (smoking status, tumor location, clinical stage and carcinoembryonic antigen (CEA) level), 18F-FDG PET/CT metabolic parameters (SUV max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and EGFR mutation status were analyzed. Patients were divided into training group (80 cases) and validation group (34 cases). In the training group, univariate analyses (independent-sample t test, Wilcoxon rank sum test, χ2 test or Fisher′s exact probability method) were used for categorical variables. Variables that showed significant differences between EGFR mutation group and wild type group were selected. Variance inflation factors (VIF) were calculated and the collinearity variables were deleted, and a nomogram model of optimal logistic model was constructed based on Akaike information criterion (AIC). The effect of the model was evaluated by the concordance index (C-index), sensitivity, specificity, accuracy, calibration and decision curve analysis (DCA) in the training group and the validation group. Results:Among 114 patients, 56 were with EGFR mutations and 58 were with EGFR wild type. In the training group, there were significant differences in gender (male/female: 14/26 vs 25/15; χ2=6.05, P=0.014), smoking status (with/without smoking history: 4/36 vs 22/18; χ2=18.46, P<0.001) and SUV max (5.72(3.90, 8.32) vs 8.09(4.56, 12.55); W=1 045.50, P=0.018) between EGFR mutation group and wild type group. However, there were no significant differences in other factors ( t=-0.54, χ2 values: 0.20 and 0.20, W values: 921.50 and 983.00, all P>0.05). The VIF of gender, smoking status and SUV max were all less than 10, and the nomogram model with three factors showed the minimum AIC (90.06). In the training group, C-index value of the model was 0.798 (95% CI: 0.699-0.897), with the sensitivity of 85.0%(34/40), the specificity of 70.0%(28/40) and the accuracy of 77.5%(62/80). In the validation group, C-index value was 0.854(95% CI: 0.725-0.984), with the sensitivity of 13/16, the specificity of 14/18, and the accuracy of 79.4%(27/34). The calibration curve and the goodness of fit test showed good calibration, and DCA showed that the model could benefit patients clinically within a large risk threshold range (training group: 0-0.59, validation group: 0-0.65). Conclusion:The nomogram model based on gender, smoking status and SUV max can be used to easily predict EGFR mutation status in lung adenocarcinoma.

The neural stimulator is a core component of animal robots. While the control effect of animal robots is influenced by various factors, the performance of the neural stimulator plays a decisive role in regulating animal robots. In order to optimize animal robots, embedded neural stimulators had been developed using flexible printed circuit board technology. This innovation not only enabled the stimulator to generate parameter-adjustable biphasic current pulses through control signals, but also optimized its carrying mode, material, and size, overcoming the disadvantages of traditional backpack or head-inserted stimulators, which have poor concealment and are prone to infection. Static, in vitro, and in vivo performance tests of the stimulator demonstrated that it not only had precise pulse waveform output capability, but also was lightweight and small in size. It had excellent in vivo performance in both laboratory and outdoor environments. Our study has high practical significance for the application of animal robots.
Animals ; Robotics