Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients

Objective This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging(MRI)for short-term postoperative facial nerve function in patients with acoustic neuroma.Methods A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included.Clinical data and raw features from four MRI sequences(T1-weighted,T2-weighted,T1-weighted contrast enhancement,and T2-weighted-Flair images)were analyzed.Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features.Nomogram,machine learning,and convolutional neural network(CNN)models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery.Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate model performance.A total of 1050 radiomic parameters were extracted,from which 13 radiomic and 3 clinical features were selected.Results The CNN model performed best among all prediction models in the test set with an area under the curve(AUC)of 0.89(95%CI,0.84–0.91).Conclusion CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma.As such,CNN modeling may serve as a potential decision-making tool for neurosurgery.

Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China:a multicenter observational study

Background:Blood glucose control is closely related to type 2 diabetes mellitus(T2DM)prognosis.This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network(EN)with a machine-learning algorithm to predict glycemic control.Methods:Basic information,biochemical indices,and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017.An EN regression was used to address variable collinearity.Then,three common machine learning algorithms(random forest[RF],support vector machine[SVM],and back propagation artificial neural network[BP-ANN])were used to simulate and predict blood glucose status.Additionally,a stepwise logistic regression was performed to compare the machine learning models.Results:The well-controlled blood glucose rate was 45.82%in North China.The multivariable analysis found that hypertension history,atherosclerotic cardiovascular disease history,exercise,and total cholesterol were protective factors in glycosylated hemoglobin(HbAlc)control,while central adiposity,family history,T2DM duration,complications,insulin dose,blood pressure,and hypertension were risk factors for elevated HbAlc.Before the dimensional reduction in the EN,the areas under the curve of RF,SVM,and BP were 0.73,0.61,and 0.70,respectively,while these figures increased to 0.75,0.72,and 0.72,respectively,after dimensional reduction.Moreover,the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models(the sensitivity and accuracy of logistic were 0.52 and 0.56;RF:0.79,0.70;SVM:0.84,0.73;BP-ANN:0.78,0.73,respectively).Conclusions:More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications.The EN and machine learning algorithms are alternative choices,in addition to the traditional logistic model,for building predictive models of blood glucose control in patients with T2DM.

Truxene-based Conjugated Microporous Polymers via Different Synthetic Methods

Four truxene-based conjugated microporous polymers(Tr-CMPs)were prepared via different synthetic methods and their structure-property relationships were studied.The polymer networks have high Brunauer-Emmett-Teller(BET)specific surface areas ranging from 554 m^2·g^–1to 1024 m^2·g^–1.Pore sizes of the CMPs with different linkers are mainly located between 0.60 and 1.96 nm.Among all the Tr-CMPs,TrCMP4 has the highest BET surface area of 1024 m^2·g^–1and exhibits the highest H2 uptake of 0.88 wt%.Tr-CMP2 prepared by Suzuki-Miyaura coupling reaction has the highest photoluminescence quantum yields(PLQYs)of 13.06% and CO2 uptake of 6.25 wt%.