An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients

Results from the Clarify Study

verfasst von

María Torrente, 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, Vit Novacek, Ernestina Menasalvas, María Esther Vidal, Mariano Provencio

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.

Externe Organisation(en)

Universidad Autónoma de Madrid (UAM)
Universidad Francisco de Vitoria (UFV)
Universidade Nova de Lisboa
Universidad de Murcia
Biomedical Research Institute of Murcia (IMIB)
Accenture Plc
University of Galway
Universidad Politécnica de Madrid (UPM)
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Artikel

Journal

Cancers

Band

14

ISSN

2072-6694

Publikationsdatum

22.08.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Onkologie, Krebsforschung

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.3390/cancers14164041 (Zugang: Offen)