Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States

Abstract

Introduction: The incidence of kidney cancer is increasing; it could be counteracted with new ways to predict and detect it. We aimed to implement an artificial neural network in order to predict new cases of renal-cell carcinoma (RCC) in the population using population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States.

Patients and methods: Statistics were collected on US population numbers, life expectancy, obesity, smoking, and hypertension. We used MATLAB R2018 (MathWorks) software to implement an artificial neural network. Data were repeatedly and randomly divided into training (70%) and validation (30%) subsets.

Results: The number of new RCC cases will grow from 44,400 (2020) to 55,400 (2050), an increase of +24.7%. Our data show that preventing hypertension would have the greatest impact on reduction of the incidence, estimated at -775 and -575 cases per year in 2020 and in 2030, respectively. The prevention of obesity and smoking would have a more limited impact, estimated at -64 and -180 cases per year in 2020 and in 2030, respectively, for obesity, and -173 and -21 cases per year in 2020 and in 2030, respectively, for smoking.

Conclusions: Our predictions underline the need for accurate studies on RCC-related risk factors to reduce the incidence.

Keywords: Atezolizumab; Avelumab; Nivolumab; Pembrolizumab; Renal-cell carcinoma.