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An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study.
Torrente, María; Sousa, Pedro A; Hernández, Roberto; Blanco, Mariola; Calvo, Virginia; Collazo, Ana; Guerreiro, Gracinda R; Núñez, Beatriz; Pimentao, Joao; Sánchez, Juan Cristóbal; Campos, Manuel; Costabello, Luca; Novacek, Vit; Menasalvas, Ernestina; Vidal, María Esther; Provencio, Mariano.
Afiliação
  • Torrente M; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Sousa PA; Faculty of Health Sciences, Francisco de Vitoria University, 28223 Madrid, Spain.
  • Hernández R; Department of Electrical Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, Portugal.
  • Blanco M; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Calvo V; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Collazo A; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Guerreiro GR; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Núñez B; Department of Mathematics and CMA, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, Portugal.
  • Pimentao J; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Sánchez JC; Department of Electrical Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, Portugal.
  • Campos M; Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain.
  • Costabello L; Chronobiology Lab, Department of Physiology, College of Biology, Mare Nostrum Campus, University of Murcia, 30100 Murcia, Spain.
  • Novacek V; Biomedical Research Institute of Murcia (IMIB)-Arrixaca, 30120 Murcia, Spain.
  • Menasalvas E; Accenture Labs, D02 P820 Dublin, Ireland.
  • Vidal ME; Data Science Institute, NUI Galway, H91 A06C Galway, Ireland.
  • Provencio M; Centro Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain.
Cancers (Basel) ; 14(16)2022 Aug 22.
Article em En | MEDLINE | ID: mdl-36011034
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.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article