{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T21:18:41Z","timestamp":1777670321360,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU H2020 program","award":["No.875160 (Project CLARIFY)"],"award-info":[{"award-number":["No.875160 (Project CLARIFY)"]}]},{"name":"EU H2020 program","award":["UID (MAT\/00297\/2020)"],"award-info":[{"award-number":["UID (MAT\/00297\/2020)"]}]},{"name":"Centro de Matem\u00e1tica e Aplica\u00e7\u00f5es","award":["No.875160 (Project CLARIFY)"],"award-info":[{"award-number":["No.875160 (Project CLARIFY)"]}]},{"name":"Centro de Matem\u00e1tica e Aplica\u00e7\u00f5es","award":["UID (MAT\/00297\/2020)"],"award-info":[{"award-number":["UID (MAT\/00297\/2020)"]}]},{"name":"Portuguese Foundation of Science and Technology","award":["No.875160 (Project CLARIFY)"],"award-info":[{"award-number":["No.875160 (Project CLARIFY)"]}]},{"name":"Portuguese Foundation of Science and Technology","award":["UID (MAT\/00297\/2020)"],"award-info":[{"award-number":["UID (MAT\/00297\/2020)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>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\u2019 characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population\u2019s 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.<\/jats:p>","DOI":"10.3390\/cancers14164041","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8791-7660","authenticated-orcid":false,"given":"Mar\u00eda","family":"Torrente","sequence":"first","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"},{"name":"Faculty of Health Sciences, Francisco de Vitoria University, 28223 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3814-6331","authenticated-orcid":false,"given":"Pedro A.","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, Portugal"}]},{"given":"Roberto","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]},{"given":"Mariola","family":"Blanco","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3503-4847","authenticated-orcid":false,"given":"Virginia","family":"Calvo","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]},{"given":"Ana","family":"Collazo","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4805-2638","authenticated-orcid":false,"given":"Gracinda R.","family":"Guerreiro","sequence":"additional","affiliation":[{"name":"Department of Mathematics and CMA, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, Portugal"}]},{"given":"Beatriz","family":"N\u00fa\u00f1ez","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6011-008X","authenticated-orcid":false,"given":"Joao","family":"Pimentao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2825-149 Lisbon, Portugal"}]},{"given":"Juan Crist\u00f3bal","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5233-3769","authenticated-orcid":false,"given":"Manuel","family":"Campos","sequence":"additional","affiliation":[{"name":"Chronobiology Lab, Department of Physiology, College of Biology, Mare Nostrum Campus, University of Murcia, 30100 Murcia, Spain"},{"name":"Biomedical Research Institute of Murcia (IMIB)-Arrixaca, 30120 Murcia, Spain"}]},{"given":"Luca","family":"Costabello","sequence":"additional","affiliation":[{"name":"Accenture Labs, D02 P820 Dublin, Ireland"}]},{"given":"Vit","family":"Novacek","sequence":"additional","affiliation":[{"name":"Data Science Institute, NUI Galway, H91 A06C Galway, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5615-6798","authenticated-orcid":false,"given":"Ernestina","family":"Menasalvas","sequence":"additional","affiliation":[{"name":"Centro Tecnolog\u00eda Biom\u00e9dica, Universidad Polit\u00e9cnica de Madrid, 28223 Madrid, Spain"}]},{"given":"Mar\u00eda Esther","family":"Vidal","sequence":"additional","affiliation":[{"name":"TIB Leibniz\u2014Information Centre for Science and Technology, 30167 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6315-7919","authenticated-orcid":false,"given":"Mariano","family":"Provencio","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1038\/s41416-021-01633-1","article-title":"Artificial intelligence in oncology: Current applications and future perspectives","volume":"126","author":"Luchini","year":"2022","journal-title":"Br. J. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1056\/NEJMra1712502","article-title":"Cancer Survivorship","volume":"379","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hamamoto, R., Suvarna, K., Yamada, M., Kobayashi, K., Shinkai, N., Miyake, M., Takahashi, M., Jinnai, S., Shimoyama, R., and Sakai, A. (2020). Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine. Cancers, 12.","DOI":"10.3390\/cancers12123532"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e594","DOI":"10.1016\/S2589-7500(20)30225-9","article-title":"A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: A multicentre, retrospective study","volume":"2","author":"Lu","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_5","first-page":"83","article-title":"Harnessing Big Data with Machine Learning in Precision Oncology","volume":"18","author":"Singla","year":"2020","journal-title":"Kidney Cancer J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5228","DOI":"10.1038\/s41467-020-19116-x","article-title":"Non-invasive decision support for NSCLC treatment using PET\/CT radiomics","volume":"11","author":"Mu","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology\u2014new tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101860","DOI":"10.1016\/j.artmed.2020.101860","article-title":"Reconstructing the patient\u2019s natural history from electronic health records","volume":"105","author":"Najafabadipour","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/j.ccell.2021.04.015","article-title":"Progress and potential: The Cancer Moonshot","volume":"39","author":"Sharpless","year":"2021","journal-title":"Cancer Cell"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1038\/d41586-021-03691-0","article-title":"Half of top cancer studies fail high-profile reproducibility effort","volume":"600","author":"Mullard","year":"2021","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11912-021-01158-z","article-title":"Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock","volume":"24","author":"Torrente","year":"2022","journal-title":"Curr. Oncol. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1001\/jamaoncol.2022.1572","article-title":"Maximizing Cancer Data\u2014The Future of Cancer Is Now","volume":"8","year":"2022","journal-title":"JAMA Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1001\/jama.2022.3580","article-title":"Preparing Clinicians for a Clinical World Influenced by Artificial Intelligence","volume":"327","author":"James","year":"2022","journal-title":"JAMA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4138","DOI":"10.1002\/cam4.3935","article-title":"Artificial intelligence in oncology: Path to implementation","volume":"10","author":"Chua","year":"2021","journal-title":"Cancer Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1038\/s41598-020-59115-y","article-title":"Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection","volume":"10","author":"Xu","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Eminaga, O., Shkolyar, E., Breil, B., Semjonow, A., Boegemann, M., Xing, L., Tinay, I., and Liao, J.C. (2022). Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study. Cancers, 14.","DOI":"10.3390\/cancers14133135"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Scurti, M., Ruiz, E.M., Vidal, M.E., Torrente, M., Vogiatzis, D., Paliouras, G., Provencio, M., and Rodr\u00edguez Gonz\u00e1lez, A. (2020, January 28\u201330). A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records. Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA.","DOI":"10.1109\/CBMS49503.2020.00044"},{"key":"ref_18","first-page":"853","article-title":"On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer","volume":"21","author":"Mohamed","year":"2022","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e20546","DOI":"10.1200\/JCO.2018.36.15_suppl.e20546","article-title":"Big data for supporting precision medicine in lung cancer patients","volume":"36","author":"Torrente","year":"2018","journal-title":"J. Clin. Oncol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1001\/jamaoncol.2019.1800","article-title":"Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports","volume":"5","author":"Kehl","year":"2019","journal-title":"JAMA Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e180926","DOI":"10.1001\/jamanetworkopen.2018.0926","article-title":"Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy","volume":"1","author":"Elfiky","year":"2018","journal-title":"JAMA Netw. Open"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1200\/JCO.2019.37.27_suppl.271","article-title":"Improving quality through A.I.: Applying machine learning to predict unplanned hospitalizations after radiation","volume":"37","author":"Christopherson","year":"2019","journal-title":"J. Clin. Oncol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e1915997","DOI":"10.1001\/jamanetworkopen.2019.15997","article-title":"Machine learning approaches to predict 6-month mortality among patients with cancer","volume":"2","author":"Parikh","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e913","DOI":"10.7717\/peerj-cs.913","article-title":"Negation and uncertainty detection in clinical texts written in Spanish: A deep learning-based approach","volume":"8","author":"Montenegro","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-030-16391-4_11","article-title":"Artificial intelligence and personalized medicine","volume":"178","author":"Schork","year":"2019","journal-title":"Cancer Treat. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.ccell.2021.04.002","article-title":"Artificial intelligence for clinical oncology","volume":"39","author":"Kann","year":"2021","journal-title":"Cancer Cell"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1038\/s41571-020-0417-8","article-title":"Artificial intelligence in radiation oncology","volume":"17","author":"Huynh","year":"2020","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1002\/cpt.1951","article-title":"Artificial intelligence and mechanistic modeling for clinical decision making in oncology","volume":"108","author":"Benzekry","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_29","first-page":"842","article-title":"Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations","volume":"42","author":"Jacob","year":"2022","journal-title":"Am. Soc. Clin. Oncol. Educ. Book"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/16\/4041\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:13:21Z","timestamp":1760141601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/16\/4041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,22]]},"references-count":29,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["cancers14164041"],"URL":"https:\/\/doi.org\/10.3390\/cancers14164041","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,22]]}}}