Articles Related to VEGF
Machine Learning Prediction of Response towards Anti-VEGF Injections in Patients with DME: Prediction of Post-Injection CST
Diabetic macular edema (DME) has become one of the most potential complications that results in loss of vision in patients with
diabetic retinopathy. Treatment outcomes that have been predicted directly with advent of machine learning (ML) methods after
the initial anti-vascular endothelial growth factor (anti-VEGF) injection, has become extremely vital in the management of DME.
Purpose: The aim of this study is to analyze the efficiency of the ML regression models which were developed and validated to predict the possible post-injection central subfield thickness (CST) value and distant vision best corrected visual acuity (DV BCVA)
in eyes with DME before the anti-VEGF injection is administered at either treatment initiation or during treatment monitoring.
Methods: This retrospective study was conducted in Medical Research Foundation, Chennai, India from January 2010 to
December 2020. The model development emphasized on an ensemble ML system consisting of four ML models that were
developed and trained independently using the clinical parameters to predict the post injection CST value. The dataset
consisting of 906 patients with total of 1874 samples [Optical coherence tomography (OCT) images and clinical parameters] were divided into trained and test set, and the model was validated on test dataset. The predicted CST values was then
compared against the respective sample’s post injection actual CST value. The comparative results were measured in terms
of Correlation Coefficient and Mean Absolute Relative Error (MARE).
Results: On evaluation, we found that Support Vector Regression (SVR) with linear kernel performed best among the
other models with four different scenarios in term of both CST and DVBCVA prediction with correlation coefficient of
0.65, 0.73, 0.75, 0.85 and 0.83, 0.87, 0.89 and 0.92 respectively.
Molecular Mechanism Linking BRCA1 Dysfunction to High Grade Serous Epithelial Ovarian Cancers with Peritoneal Permeability and Ascites
Ovarian cancer constitutes the second most common gynecological cancer with a five-year survival rate of 40%. Among the various histotypes associated with hereditary ovarian cancer, high-grade serous epithelial ovarian carcinoma (HGSEOC) is the most predominant and women with inherited mutations in BRCA1 have a lifetime risk of 40-60%. HGSEOC is a challenge for clinical oncologists, due to late presentation of patient, diagnosis and high rate of relapse. Ovarian tumors have a wide range of clinical presentations including development of ascites as a result of deregulated endothelial function thereby causing increased vascular permeability of peritoneal vessels.
Do Expression Profiles of Cytokines VEGF, TNF- α, IL-1β, IL-6 and IL-8 Correlate with Gallbladder Cancer?
Gallbladder carcinoma is a multifactorial disease with a complex interplay at molecular levels. Here we look at the expression of specific cytokines (TNF-α, IL-1β, IL-6, IL-8 and VEGF) in GBC patients to develop them as biomarkers of gallbladder cancer.
Phase II Trial of Lower Dose Bevacizumab and Irinotecan in Relapsed High Grade Gliomas
Relapsed high-grade gliomas (HGG) respond poorly to known chemotherapeutic agents with a median survival of 3 to 6 months. Several phase II trials of Bevacizumab for salvage therapy, reported excellent response rates. The optimal dose of Bevacizumab in GBM has not been defined to date. We performed a prospective phase II trial of bevacizumab using 5 mg/kg every 2 weeks.
Editorial Board Members Related to VEGF

Eugene S. Kim
Associate Professor
Department of Surgery
University of Southern California
United States
Department of Surgery
University of Southern California
United States

YOSHIHITO YOKOYAMA
Associate professor
Department of Obstetrics and Gynecology
Hirosaki University Graduate School of Medicine
Japan
Department of Obstetrics and Gynecology
Hirosaki University Graduate School of Medicine
Japan