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Artificial intelligence for identification of patters of toxicity of tyrosine kinase inhibitors in patients with kidney cancer

Grant number: 19/25676-3
Support type:Scholarships in Brazil - Master
Effective date (Start): November 01, 2020
Effective date (End): March 31, 2022
Field of knowledge:Health Sciences - Medicine - Medical Clinics
Principal researcher:Leandro Machado Colli
Grantee:Guilherme Lima Paschoalini
Home Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

Treatment options for metastatic renal cell carcinoma (mRCC) have recently increased with the development of various agents that inhibit vascular endothelial growth factor (VEGF) elements and rapamycin pathways. Sunitinib, a multi-target VEGF receptor tyrosine kinase inhibitor(TKI) and c-kit, showed disease-free survival gain compared to interferon alfa in a phase III study. Although Sunitinib is a widely used treatment in the mRCC scenario, a high proportion of patients treated have significant hematological and metabolic toxicity, which impacts quality of life.The identification of biomarkers to identify these patients with high toxicity may allow the development of specific care to prevent or reduce these toxicities.Based on data from electronic medical records of patients treated at the Clinical Hospital of Ribeirão Preto from 2008-2018 and using Machine Learning(ML) which is a data analysis method that automates the construction of Analytical standard with algorithms, being an important field of Artificial Intelligence (AI), will be the main methodology of this study that will develop biomarkers of prediction of toxicity pattern in mRCC patients who used TKI. (AU)