Treatment of Metastatic Renal Cell Carcinoma with Checkpoint Inhibitors in Clinical Practice in the Volga-Ural Region of the Eurasian Continent.

INTRODUCTION: Renal cancer ranks 10th in the mortality structure of the Russian Federation. The introduction of checkpoint inhibitors has changed the paradigm of treatment of patients with malignant neoplasms. METHOD: Data from clinical trials have shown good progression-free median and median overall survival. Each cancer center has been accumulating its own experience in treating patients with renal cell cancer by applying modern target drugs and immunotherapy. RESULT: In routine clinical practice, oncologists do not get the results that have been demonstrated in clinical trials when evaluating the effectiveness of the therapy. CONCLUSION: In this single-center clinical study, we discuss the results of using nivolumab as mono-therapy and the combination of nivolumab with ipilimumab in metastatic renal parenchyma cancer patients.

Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods.

The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indices and classifications for quantifying the severity of keratoconus have been developed. Today, many of them involve the use of the latest methods of computer processing and data analysis. The main purpose of this work was to develop a machine-learning-based algorithm to precisely determine the stage of keratoconus, allowing optimal management of patients with this disease. A multicentre retrospective study was carried out to obtain a database of patients with keratoconus and to use machine-learning techniques such as principal component analysis and clustering. The created program allows for us to distinguish between a normal state; preclinical keratoconus; and stages 1, 2, 3 and 4 of the disease, with an accuracy in terms of the AUC of 0.95 to 1.00 based on keratotopographer readings, relative to the adapted Amsler-Krumeich algorithm. The predicted stage and additional diagnostic criteria were then used to create a standardised keratoconus management algorithm. We also developed a web-based interface for the algorithm, providing us the opportunity to use the software in a clinical environment.