An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/16/4041 |
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| author | 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 |
| author_facet | 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 |
| author_sort | María Torrente |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:38:23Z |
| format | Article |
| id | doaj.art-b577f73207b442e9aeef8e0c3d946a56 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T04:38:23Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-b577f73207b442e9aeef8e0c3d946a562023-12-03T13:25:43ZengMDPI AGCancers2072-66942022-08-011416404110.3390/cancers14164041An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify StudyMaría Torrente0Pedro A. Sousa1Roberto Hernández2Mariola Blanco3Virginia Calvo4Ana Collazo5Gracinda R. Guerreiro6Beatriz Núñez7Joao Pimentao8Juan Cristóbal Sánchez9Manuel Campos10Luca Costabello11Vit Novacek12Ernestina Menasalvas13María Esther Vidal14Mariano Provencio15Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainDepartment of Electrical Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, PortugalDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainDepartment of Mathematics and CMA, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, PortugalDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainDepartment of Electrical Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, PortugalDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainChronobiology Lab, Department of Physiology, College of Biology, Mare Nostrum Campus, University of Murcia, 30100 Murcia, SpainAccenture Labs, D02 P820 Dublin, IrelandData Science Institute, NUI Galway, H91 A06C Galway, IrelandCentro Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, SpainTIB Leibniz—Information Centre for Science and Technology, 30167 Hannover, GermanyDepartment of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, SpainBackground: 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.https://www.mdpi.com/2072-6694/14/16/4041artificial intelligencedata integrationcancer patientspatient stratificationprecision oncologydecision support system |
| spellingShingle | 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 An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study Cancers artificial intelligence data integration cancer patients patient stratification precision oncology decision support system |
| title | An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study |
| title_full | An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study |
| title_fullStr | An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study |
| title_full_unstemmed | An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study |
| title_short | An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study |
| title_sort | artificial intelligence based tool for data analysis and prognosis in cancer patients results from the clarify study |
| topic | artificial intelligence data integration cancer patients patient stratification precision oncology decision support system |
| url | https://www.mdpi.com/2072-6694/14/16/4041 |
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