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@article{2241660,
author = {Torrente, María and Sousa, Pedro A and Hernández, Roberto and Blanco, Mariola and Collazo, Virginia Calvo Ana and Guerreiro, Gracinda R and Núñez, Beatriz and Pimentao, Joao and Sánchez, Juan Cristóbal and Campos, Manuel and Costabello, Luca and Nováček, Vít and Menasalvas, Ernestina and Vidal, María Esther and Provencio, Mariano and Calvo, Virginia},
article_location = {SWITZERLAND},
article_number = {16},
doi = {http://dx.doi.org/10.3390/cancers14164041},
keywords = {artificial intelligence; data integration; cancer patients; patient stratification; precision oncology; decision support system},
language = {eng},
issn = {2072-6694},
journal = {CANCERS},
title = {An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study},
url = {https://www.mdpi.com/2072-6694/14/16/4041},
volume = {14},
year = {2022}
}
TY - JOUR
ID - 2241660
AU - Torrente, María - Sousa, Pedro A - Hernández, Roberto - Blanco, Mariola - Collazo, Virginia Calvo Ana - Guerreiro, Gracinda R - Núñez, Beatriz - Pimentao, Joao - Sánchez, Juan Cristóbal - Campos, Manuel - Costabello, Luca - Nováček, Vít - Menasalvas, Ernestina - Vidal, María Esther - Provencio, Mariano - Calvo, Virginia
PY - 2022
TI - An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study
JF - CANCERS
VL - 14
IS - 16
SP - 1-10
EP - 1-10
PB - MDPI
SN - 20726694
KW - artificial intelligence
KW - data integration
KW - cancer patients
KW - patient stratification
KW - precision oncology
KW - decision support system
UR - https://www.mdpi.com/2072-6694/14/16/4041
N2 - 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.
ER -
TORRENTE, María, Pedro A SOUSA, Roberto HERNÁNDEZ, Mariola BLANCO, Virginia Calvo Ana COLLAZO, Gracinda R GUERREIRO, Beatriz NÚÑEZ, Joao PIMENTAO, Juan Cristóbal SÁNCHEZ, Manuel CAMPOS, Luca COSTABELLO, Vít NOVÁČEK, Ernestina MENASALVAS, María Esther VIDAL, Mariano PROVENCIO and Virginia CALVO. An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study. \textit{CANCERS}. SWITZERLAND: MDPI, 2022, vol.~14, No~16, p.~1-10. ISSN~2072-6694. Available from: https://dx.doi.org/10.3390/cancers14164041.
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