Matteo Santoni1, Francesco Piva2, Camillo Porta3, Sergio Bracarda4, Daniel Y Heng5, Marc R Matrana6, Enrique Grande7, Veronica Mollica8, Gaetano Aurilio9, Mimma Rizzo10, Matteo Giulietti2, Rodolfo Montironi11, Francesco Massari12. 1. Oncology Unit, Macerata Hospital, Macerata, Italy. 2. Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Ancona, Italy. 3. Department of Biomedical Sciences and Human Oncology, University of Bari "A. Moro," Bari, Italy. 4. Struttura Complessa di Oncologia Medica e Traslazionale, Azienda Ospedaliera Santa Maria di Terni, Terni, Italy. 5. Division of Medical Oncology, Department of Oncology, Tom Baker Cancer Centre, University of Calgary, Calgary, Canada. 6. Department of Internal Medicine, Hematology/Oncology, Ochsner Medical Center, New Orleans, LA. 7. Medical Oncology Department, MD Anderson Cancer Center, Madrid, Spain. 8. Oncologia Medica, Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy. 9. Medical Oncology Division of Urogenital and Head and Neck Tumours, IEO, European Institute of Oncology IRCCS, Milan, Italy. 10. Department of Internal Medicine and Therapeutics, University of Pavia and Division of Translational Oncology, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy. 11. Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy. 12. Oncologia Medica, Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy. Electronic address: fmassari79@gmail.com.
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.
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.