Details
| Original language | English |
|---|---|
| Article number | 4041 |
| Journal | Cancers |
| Volume | 14 |
| Issue number | 16 |
| Publication status | Published - 22 Aug 2022 |
| Externally published | Yes |
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.
Keywords
- artificial intelligence, cancer patients, data integration, decision support system, patient stratification, precision oncology
ASJC Scopus subject areas
- Medicine(all)
- Oncology
- Biochemistry, Genetics and Molecular Biology(all)
- Cancer Research
Sustainable Development Goals
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In: Cancers, Vol. 14, No. 16, 4041, 22.08.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients
T2 - Results from the Clarify Study
AU - Torrente, María
AU - Sousa, Pedro A.
AU - Hernández, Roberto
AU - Blanco, Mariola
AU - Calvo, Virginia
AU - Collazo, Ana
AU - Guerreiro, Gracinda R.
AU - Núñez, Beatriz
AU - Pimentao, Joao
AU - Sánchez, Juan Cristóbal
AU - Campos, Manuel
AU - Costabello, Luca
AU - Novacek, Vit
AU - Menasalvas, Ernestina
AU - Vidal, María Esther
AU - Provencio, Mariano
N1 - Funding information: 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 Technology.
PY - 2022/8/22
Y1 - 2022/8/22
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.
AB - 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.
KW - artificial intelligence
KW - cancer patients
KW - data integration
KW - decision support system
KW - patient stratification
KW - precision oncology
UR - http://www.scopus.com/inward/record.url?scp=85137601681&partnerID=8YFLogxK
U2 - 10.3390/cancers14164041
DO - 10.3390/cancers14164041
M3 - Article
AN - SCOPUS:85137601681
VL - 14
JO - Cancers
JF - Cancers
SN - 2072-6694
IS - 16
M1 - 4041
ER -