An artificial intelligence-based tool for data analysis and prognosis in cancer patients: results from the clarify study
Author
Torrente, María; Sousa, Pedro A.; Hernández, Roberto; Blanco, Mariola; Calvo de Juan, Virginia


Publisher
MDPIDate
2022-08-22Citation
10.3390/cancers14164041
Cancers 14.16 (2022): 4041
ISSN
2072-6694 (online)DOI
10.3390/cancers14164041Funded by
This 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 TechnologyProject
info:eu-repo/grantAgreement/EC/H2020/875160;Editor's Version
https://doi.org/10.3390/cancers14164041Subjects
artificial intelligence; cancer patients; data integration; decision support system; patient stratification; precision oncology; MedicinaRights
© 2022 by the authorsAbstract
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
Files in this item
Google Scholar:Torrente, María
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Sousa, Pedro A.
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Hernández, Roberto
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Blanco, Mariola
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Calvo de Juan, Virginia
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Collazo, Ana
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Guerreiro, Gracinda R.
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Núñez, Beatriz
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Pimentao, Joao
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Sánchez, Juan Cristóbal
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Campos Yuste, Manuel
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Costabello, Luca
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Novacek, Vit
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Menasalvas, Ernestina
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Vidal, María Esther
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Provencio Pulla, Mariano
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