1.Evaluation of the effectiveness and safety of TransPRK assisted by smart pulse technology for high myopia
Xiaohao DU ; Jia ZHANG ; Meng SU ; Wenjia CAO ; Shuang ZENG ; Qinmei WANG ; Shihao CHEN
Chinese Journal of Experimental Ophthalmology 2021;39(12):1053-1058
Objective:To evaluate the effectiveness and safety of transepithelial photorefractive keratectomy (TransPRK) assisted by smart pluse technology (SPT) for the correction of high myopia.Methods:An observational case series study was conducted.Sixty high myopic patients (107 eyes) with spherical equivalent (SE)≥-6.0 D who received TransPRK assisted by SPT from January to December 2016 in Eye Hospital of Wenzhou Medical University were enrolled.Uncorrected visual acuity (UCVA) and best corrected visual acuity (BCVA) of the patients were examined and recorded in logarithm of the minimum angle of resolution (LogMAR) units, and refraction was examined with a subjective refractometer.The healing of corneal epithelium and corneal haze was observed with a slit lamp.Intraocular pressure (IOP) was measured with the non-contact tonometer.Safety index (SI) and efficacy index (EI) were analyzed.The follow-up time was 12 months.This study protocol adhered to the Declaration of Helsinki and was approved by an Ethics Committee of Eye Hospital of Wenzhou Medical University (No.2019-197-k-177). Written informed consent was obtained from each patient prior to any medical examination.Results:The mean epithelial healing time was (3.77±1.02) days.There were statistically significant differences in UCVA and BCVA between before and after surgery ( Z=380.812, 267.313; both at P<0.001). And the 7-day, 6-month, and 12-month postoperative BCVA were better than preoperative BCVA, showing statistically significant differences (all at P<0.05). Mean SI was 1.10±0.12, and mean EI was 1.05±0.17 at 12 months after surgery.There was no significant difference between the attempted SE before surgery (-8.02±1.36)D and the achieved SE at 12 months after surgery (-8.04±1.51)D ( P=0.523). SE in the predictive range within ±0.50 D accounted for 79% (85/107) and that within ±1.0 D accounted for 92% (98/107). The IOP was slightly increased in 3 eyes at 7 days and 7 eyes at 1 month after surgery, respectively, which returned to normal after the use of ophthalmic solution for lowing IOP.The incidence of haze severer than grade 1 was less than 1% (1 eye), and haze gradually disappeared after application of drugs. Conclusions:TransPRK assisted by SPT for high myopia shows good safety, effectiveness and predictability.It is an ideal corneal surface surgery to correct high myopia.
2.Prediction of methylation status of MGMT promoter in WHO gradeⅡ,Ⅲ glioma based on MRI deep learning model
Caiqiang XUE ; Xiaohao DU ; Long JIN ; Xiaoai KE ; Bin ZHANG ; Junlin ZHOU
Chinese Journal of Radiology 2021;55(7):734-738
Objective:To explore the value of a deep learning model based on MRI in predicting the methylation status of MGMT in WHO Ⅱ, Ⅲ gliomas.Methods:The clinical and imaging data of 121 patients with WHO grade Ⅱ, Ⅲ glioma confirmed by surgical pathology and molecular pathology in the Second Hospital of Lanzhou University from June 2016 to June 2020 were retrospectively analyzed. Among them, the MGMT promoter was methylated. A total of 78 cases were metabolized and 43 cases were unmethylated. T 2WI and T 1WI enhanced sequence images of 121 cases of WHO Ⅱ, Ⅲ gliomas were collected, and all the images of each patient including the lesion level were selected manually, and were randomly divided into training set and validation set according to 7∶3. The EfficientNet-B3 convolutional neural network was used to build independent prediction models (T 2-net, T 1C-net, TS-net) based on T 2WI, T 1WI enhancement, T 2WI combined with T 1WI enhancement, and the prediction performance of each model was evaluated separately through the ROC curve. Results:The T 2-net model in the validation set presented an accuracy of 72.3%, a sensitivity of 64.7%, a specificity of 73.3%, and an area under the curve (AUC) of 0.72 for predicting the methylation status of the MGMT promoter in WHO Ⅱ, Ⅲ gliomas. The T 1C-net model showed an accuracy of 66.8%, a sensitivity of 68.3%, a specificity of 66.9%, and an AUC of 0.72. The TS-net model showed an accuracy of 81.8%, a sensitivity of 63.1%, a specificity of 85.0%, and AUC of 0.78. Conclusions:The EfficientNet-B3 convolutional neural network based on MRI can predict the methylation status of the MGMT promoter of WHO Ⅱ, Ⅲ gliomas; the TS-net model has the best prediction performance.