Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

Journal: European journal of cancer (Oxford, England : 1990)

PMID: 33373867

Abstract

BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient response. In the era of personalised oncology, new approaches for developing predictive models of response are needed.

Authors

Nuria Ribelles

Virgen de la Victoria University Hospital, Malaga, Spain.
José M Jerez

Department of Computer Languages and Computer Science, University of Málaga, 29071 Málaga, Spain.
Pablo Rodriguez-Brazzarola

University of Málaga, Department of Languages and Computer Science, E.T.S.I. Computing, Málaga, Spain.
Begoña Jimenez

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Tamara Diaz-Redondo

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Hector Mesa

University of Málaga, Department of Languages and Computer Science, E.T.S.I. Computing, Málaga, Spain.
Antonia Marquez

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Alfonso Sanchez-Muñoz

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Bella Pajares

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Francisco Carabantes

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Maria J Bermejo

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Ester Villar

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Maria E Dominguez-Recio

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Enrique Saez

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Laura Galvez

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Ana Godoy

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Leo Franco

University of Málaga, Department of Languages and Computer Science, E.T.S.I. Computing, Málaga, Spain.
Sofia Ruiz-Medina

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Irene Lopez

Medical Oncology Intercenter Unit, Regional and Virgen de la Victoria University Hospitals, IBIMA, Málaga, Spain.
Emilio Alba

Virgen de la Victoria University Hospital, Malaga, Spain.

Keywords

Adult Aged Aged, 80 and over Antineoplastic Combined Chemotherapy Protocols Breast Neoplasms Disease Progression Electronic Health Records Female Follow-Up Studies Humans Machine Learning Middle Aged Natural Language Processing Prognosis Receptor, ErbB-2 Receptors, Estrogen Receptors, Progesterone Retrospective Studies Survival Rate Young Adult

External Resources

View on PubMed Access via DOI PubMed (33373867)

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

Abstract

Authors

Keywords

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