An artificial intelligence-based tool for data analysis and prognosis in cancer patients: results from the clarify study
AuthorTorrente, María; Sousa, Pedro A.; Hernández, Roberto; Blanco, Mariola; Calvo de Juan, Virginia; Collazo, Ana; Guerreiro, Gracinda R.; Núñez, Beatriz; Pimentao, Joao; Sánchez, Juan Cristóbal; Campos Yuste, Manuel; Costabello, Luca; Novacek, Vit; Menasalvas, Ernestina; Vidal, María Esther; Provencio Pulla, Mariano
10.3390/cancers14164041Cancers 14.16 (2022): 4041
Funded byThis work was supported by the EU H2020 program, under grant agreement No.875160 (Project CLARIFY) and Centro de Matemática e Aplicações, UID (MAT/00297/2020), Portuguese Foundation of Science and Technology
Subjectsartificial intelligence; cancer patients; data integration; decision support system; patient stratification; precision oncology; Medicina
Rights© 2022 by the authors
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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. 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. 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
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Google Scholar:Torrente, María - Sousa, Pedro A. - Hernández, Roberto - Blanco, Mariola - Calvo de Juan, Virginia - Collazo, Ana - Guerreiro, Gracinda R. - Núñez, Beatriz - Pimentao, Joao - Sánchez, Juan Cristóbal - Campos Yuste, Manuel - Costabello, Luca - Novacek, Vit - Menasalvas, Ernestina - Vidal, María Esther - Provencio Pulla, Mariano
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