Objective To evaluate the association between diabetic retinopathy (DR) and mean ocular perfusion pressure (MOPP) in patients with type 2 diabetes mellitus (T2DM).Methods Patients from the Fushun Diabetic Retinopathy Cohort Study (FS-DIRECT), a communitybased prospective cohort study conducted in northeast China, were included in this study. The presence and severity of DR were determined by grading fundus photographs according to the Early Treatment Diabetic Retinopathy Study (ETDRS) retinopathy scale. Systolic and diastolic blood pressure (SBP and DBP) were recorded using an electronic sphygmomanometer. Intraocular pressure (IOP) was measured using an iCare rebound tonometer. MOPP was calculated using the formula MOPP = 2/3 [DBP + 1/3(SBP ? DBP)] ? IOP.Results In total, 1,857 patients who had gradable fundus photography and MOPP data were enrolled in this study. Male patients had a higher MOPP than female patients (52.25 ± 8.75 vs. 50.96 ± 8.74mmHg, P = 0.002). Overall, both male and female patients with any type of DR, non-proliferative DR (NPDR), or non-sight-threatening DR (non-STDR) had significantly higher MOPP relative to patients without DR. Increased MOPP (per 1 mmHg) was in turn associated with the presence of any type of DR [odds ratio (OR) = 1.03, 95％ confidence interval (CI) : 1.02–1.04], NPDR (OR = 1.0395％ CI: 1.02–1.04),and non-STDR (OR = 1.03, 95％ CI: 1.01–1.04) after adjusting for confounders. Increased MOPP (per 1 mmHg) was also associated with an increased likelihood of macular edema (OR = 1.02, 95％ CI:1.01–1.04).Conclusions The results suggest that increased MOPP was associated with DR and macular edema in northeastern Chinese patients with T2DM.

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.
Artificial Intelligence ; Female ; Humans ; Magnetic Resonance Imaging ; Ovarian Neoplasms/diagnostic imaging* ; Prognosis ; Tomography, X-Ray Computed

Objective:To investigate the epidemiological characteristics and current status of surgical management for esophageal cancer in China.Methods:A national database was setup through a network platform. The clinical data of esophageal cancer treated by surgery was collected from 70 major hospitals in China between January 2009 and December 2014.Results:Complete data of 8 181 cases of esophageal cancer patients who underwent surgery were recorded in the database and recruited in the analysis. Among them, 6 052 cases were male and 2 129 were female, the average age was 60.5 years.The epidemiological investigation results showed that 148 cases (1.8%) had history of psychological trauma, 7 527 cases (92.0%) were lower social economic status, 5 072 cases (62.0%) were short of fresh vegetables and fruits, 6 544 cases (80.0%) ate rough food frequently, 3 722 cases (45.5%) drank untreated water directly from lake or river or shallow well, 3 436 cases (42.0%) had a unhealthy eating habit, including habits of eating food fast (507 cases, 6.2%), eating hot food or drinking hot tea/soup (998 cases, 12.2%), eating fried food (1 939 cases, 23.7%), 4 410 cases (53.9%) had the habits of smoking cigarettes and 2 822 cases (34.5%) drank white wine frequently.The pathological results showed that 7 813 cases (95.5%) were squamous cell carcinoma, 267 cases were adenocarcinoma (3.3%), 25 cases were adenosquamous cell carcinoma (0.3%) and 50 cases were small cell carcinoma (0.6%). A total of 1 800 cases (22.0%) received preoperative neoadjuvant therapy due to locally advanced disease or difficulty of resection. The esophagectomies were performed through left thoracotomy approach in 5 870 cases (71.8%), through right chest approach in 2 215 cases (27.1%), and the remain 96 cases (1.2%) received surgery though other approaches.A total of 8 001 cases (97.8%) underwent radical resection, the other 180 cases (2.2%) received palliative resection. The 30-day postoperative mortality rate was 0.5%, the overall ≥ grade Ⅱ postoperative complication rate was 11.6% (951 cases). The 1-yr, 3-yr, and 5-yr overall actual survival rates were 82.6%, 61.6%, and 52.9%, respectively.Conclusions:The data analysis of the national database for esophageal cancer shows that bad eating habits or eating rough food without enough nutrients, lower social and economic status, drinking white wine and smoking cigarettes frequently may be correlated with tumorigenesis of esophageal cancer. However, strong evidences produced by prospective observation studies are needed. Overall, the long-term survival of esophageal cancer patients has been improved gradually due to the application of advanced surgical techniques and reasonable multimodality treatment.

Objective:To investigate the epidemiological characteristics and current status of surgical management for esophageal cancer in China.Methods:A national database was setup through a network platform. The clinical data of esophageal cancer treated by surgery was collected from 70 major hospitals in China between January 2009 and December 2014.Results:Complete data of 8 181 cases of esophageal cancer patients who underwent surgery were recorded in the database and recruited in the analysis. Among them, 6 052 cases were male and 2 129 were female, the average age was 60.5 years.The epidemiological investigation results showed that 148 cases (1.8%) had history of psychological trauma, 7 527 cases (92.0%) were lower social economic status, 5 072 cases (62.0%) were short of fresh vegetables and fruits, 6 544 cases (80.0%) ate rough food frequently, 3 722 cases (45.5%) drank untreated water directly from lake or river or shallow well, 3 436 cases (42.0%) had a unhealthy eating habit, including habits of eating food fast (507 cases, 6.2%), eating hot food or drinking hot tea/soup (998 cases, 12.2%), eating fried food (1 939 cases, 23.7%), 4 410 cases (53.9%) had the habits of smoking cigarettes and 2 822 cases (34.5%) drank white wine frequently.The pathological results showed that 7 813 cases (95.5%) were squamous cell carcinoma, 267 cases were adenocarcinoma (3.3%), 25 cases were adenosquamous cell carcinoma (0.3%) and 50 cases were small cell carcinoma (0.6%). A total of 1 800 cases (22.0%) received preoperative neoadjuvant therapy due to locally advanced disease or difficulty of resection. The esophagectomies were performed through left thoracotomy approach in 5 870 cases (71.8%), through right chest approach in 2 215 cases (27.1%), and the remain 96 cases (1.2%) received surgery though other approaches.A total of 8 001 cases (97.8%) underwent radical resection, the other 180 cases (2.2%) received palliative resection. The 30-day postoperative mortality rate was 0.5%, the overall ≥ grade Ⅱ postoperative complication rate was 11.6% (951 cases). The 1-yr, 3-yr, and 5-yr overall actual survival rates were 82.6%, 61.6%, and 52.9%, respectively.Conclusions:The data analysis of the national database for esophageal cancer shows that bad eating habits or eating rough food without enough nutrients, lower social and economic status, drinking white wine and smoking cigarettes frequently may be correlated with tumorigenesis of esophageal cancer. However, strong evidences produced by prospective observation studies are needed. Overall, the long-term survival of esophageal cancer patients has been improved gradually due to the application of advanced surgical techniques and reasonable multimodality treatment.

Objective: To evaluate the feasibility and accuracy of deep learning in CT image segmentation and further lesion-volume assessment of spontaneous intracerebral hemorrhage. Methods: A total of 1 223 cases of spontaneous intracerebral hemorrhage including parenchymal hemorrhage, ventricular hemorrhage, subarachnoid hemorrhage and mixture hemorrhage, from April 2016 to April 2018 in Tianjin Medical University General Hospital, were retrospectively enrolled and analyzed. The patients were randomly divided into training set (905 cases), validation set (156 cases) and test set (162 cases), among each group, the number of parenchymal hemorrhage was 498, 107 and 100, respectively. The bleeding area manually outlined by physician was served as the reference standard to build the segmentation model and to evaluate the performance of the validation set. Patients were divided into 3 groups according to the volume calculated by reference standard. The volume of hematoma in group 1 was less than 5 ml, while group 2 was 5-25 ml, and group 3 was more than 25 ml. Comparison of the hematoma volume calculated by segmentation model and that calculated by ABC/2 formula was conducted in 97 simple intraparenchymal hemorrhage cases. Results: In 162 cases of test set, the Dice coefficients of the segmentation model were 0.87, 0.85, 0.67 and 0.77 in parenchymal hemorrhage, intraventricular hemorrhage, subarachnoid hemorrhage and mixture hemorrhage, respectively. The estimated hematoma volume in the 97 intraparenchymal hemorrhage cases calculated by the segmentation model was (29.55±37.69) ml, and that calculated by the ABC/2 formula was (24.04±31.22) ml. Compared with reference standard, the absolute errors of three segmentation model were (0.52±0.54), (1.53±1.22) and (7.93±8.49) ml in group 1, 2 and 3 respectively. The absolute errors of the ABC/2 formula were (0.68±0.60), (3.16±2.90) and (19.31±17.23) ml in group 1, 2 and 3. Conclusion Deep learning based segmentation model improved detection of intraparenchymal hematoma volume, compared with ABC/2 formula.