Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Glaucoma Using Machine Learning.

PRCIS: Machine learning (ML) based on the optical coherence tomography angiography vessel density features with different thresholds using a support vector machine (SVM) model provides excellent performance for glaucoma detection. BACKGROUND: To assess the classification performance of ML based on the 4 vessel density features of peripapillary optical coherence tomography angiography for glaucoma detection. METHODS: Images from 119 eyes of 119 glaucoma patients and 76 eyes of 76 healthy individuals were included. Four vessel density features of optical coherence tomography angiography images were developed using a threshold-based segmentation method and were integrated into 3 models of machine learning classifiers. Images were divided into 70% training set and 30% test set. Classification performances of SVM, random forest, and Gaussian Naive Bayes models were evaluated with the area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: Glaucoma eyes had lower vessel densities at different thresholds. For differentiating glaucoma eyes, the best results were achieved with 70% and 100% thresholds, in which SVM classifier discriminated glaucoma from healthy eyes with an AUC of 1 and accuracy of 1. After SVM, the random forest classifier with 100% thresholds showed an AUC of 0.993 and an accuracy of 0.994. Furthermore, the AUC of our ML performance (SVM) was 0.96 in a subgroup analysis of mild and moderate glaucoma eyes. CONCLUSIONS: ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for glaucoma detection.

Log Odds of Positive Lymph Nodes (LODDS) as an Independent Predictor of Overall Survival Following Radical Cystectomy in Urothelial Bladder Cancer: Time to Rethink Conventional Node Staging.

BACKGROUND: Radical cystectomy (RC) with lymph node dissection is the mainstay of treatment for patients with muscle-invasive bladder cancer (MIBC) and high risk non-MIBC. The American Joint Committee on Cancer's (AJCC) node staging and lymph node ratio (LNR) systems are used in estimating prognosis; however, they do not directly factor in negative dissected nodes. In this study, we evaluated the log odds of positive lymph nodes (LODDS), a novel measure of nodal involvement, as a predictor of survival. PATIENTS AND METHODS: Eighty-three patients who underwent RC were retrospectively included and their demographic and clinical data were collected. Kaplan-Meier curve and Cox regression were used for survival analyses. RESULTS: Median number of dissected lymph nodes was 13 (range 3-45). ROC curve analysis indicated -0.92 as the optimal LODDS cutoff. LODDS > -0.92 was associated with higher T stage, lymphovascular invasion, and significantly worse overall survival (OS) (mean OS 18.6 vs. 45.1 months, P-value < .001). Furthermore, we evaluated AJCC node staging, LNR, and LODDS in three separate multivariable Cox regression models. Among 3 different measures of nodal disease burden, only LODDS was an independent predictor of OS (HR 2.71, 95% CI 1.28-5.73, P = .009). CONCLUSIONS: Our results show that LODDS is an independent predictor of OS and outperforms AJCC node staging and LNR in forecasting prognosis among patients with urothelial bladder cancer who undergo RC.