An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

SIMPLE SUMMARY: Cancer is associated with significant morbimortality worldwide. Although significant advances have been made in the last few decades in terms of early detection and treatment, providing personalized care remains a challenge. Artificial intelligence (AI) has emerged as a means of improving cancer care with the use of computer science. Identification of risk factors for poor prognosis and patient profiling with AI techniques and tools is feasible and has potential application in clinical settings, including surveillance management. The goal of this study is to present an AI-based solution tool for cancer patients data analysis and improve their management by identifying clinical factors associated with relapse and survival, developing a prognostic model that identifies features associated with poor prognosis, and stratifying patients by risk. ABSTRACT: Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.