ABSTRACT
Objective • To explore the layer segmentation method of optical coherence tomography (OCT) images of retinal vascular diseases using an unsupervised learning method, and compare it with the built-in layering method of OCT machine. Methods • Standardized image acquisition was performed on OCT images from 50 patients with myopic choroidal neovascularization (mCNV) and 20 patients with diabetic macular edema (DME). Standards were established by manual marking of hierarchical information by professional physicians. A retinal multi-layer segmentation method based on the minimization of interlayer energy was proposed, and the results were compared with those obtained by the built-in layering method of OCT machine. The layering accuracy was verified by the unmarked boundary position error. Results • This segmentation method divided the retina of each patient into five layers: internal limiting membrane, lower layer of nerve fiber layer, upper layer of outer nuclear layer, upper layer of ellipsoid zone and Bruch's membrane. The average segmentation error in the overall data set was (4.831±7.015) μm. The error of mCNV group and DME group were (4.839±16.819) μm and (5.048±9.986) μm, respectively, both of which were lower than the automatic measurement results of OCT machine [(13.638±58.024) μm and (14.796±45.342) μm, respectively]. The accuracy of this method at each layer was higher than that of the automatic measurement. Conclusion • This multi-layer segmentation method can be used for segmentation of different types of retinal vascular diseases, and the results are significantly better than those obtained by the built-in method in OCT machine. It can be extended for layer segmentation of other retinal vascular diseases.
ABSTRACT
Lung cancer is the leading cause of cancer-related deaths worldwide. Acquired drug resistance and metastasis are the main causes of treatment failure in lung cancer. Tumor microenvironment is a complex network for the survival and progression of tumor cells, in which inflammatory factors play a critical role in drug resistance and metastasis. Interleukin (IL)-8 is one of critical pro-inflammatory cytokines responsible for cell proliferation, invasion and metastasis, drug resistance and early recurrence in lung cancer. A serum based approach is advantageous for providing a real-time detection and evaluation of disease status in patients. In this review, we summarize the recent advances of IL-8 in predicting prognosis and radiation-induced lung toxicity (RILT), as well as increasing resistance and stem-like characteristics of lung cancer.