2026-05-27T11:57:03Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/312442026-02-03T11:05:57Zcom_10630_2254col_10630_37953

Ribelles, Nuria Jerez-Aragonés, José Manuel Rodríguez-Brazzarola, Pablo Jiménez-Rodríguez, Begoña Díaz-Redondo, Tamara Mesa, Héctor Márquez, Antonia Sánchez-Muñoz, Alfonso Pajares, Bella Carabantes, Francisco Bermejo-Pérez, María José Villar, Ester Domínguez-Recio, María Emilia Saez-Lara, Enrique Gálvez Carvajal, Laura Godoy-Ortiz, Ana Franco, Leónardo Ruiz-Medina, Sofía López, Irene Alba-Conejo, Emilio 2024-05-10T08:13:21Z 2024-05-10T08:13:21Z 2021 Ribelles N, Jerez JM, Rodriguez-Brazzarola P, Jimenez B, Diaz-Redondo T, Mesa H, Marquez A, Sanchez-Muñoz A, Pajares B, Carabantes F, Bermejo MJ, Villar E, Dominguez-Recio ME, Saez E, Galvez L, Godoy A, Franco L, Ruiz-Medina S, Lopez I, Alba E. 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. Eur J Cancer. 2021 Feb;144:224-231. doi: 10.1016/j.ejca.2020.11.030. Epub 2020 Dec 26. PMID: 33373867. https://hdl.handle.net/10630/31244 10.1016/j.ejca.2020.11.030 Background: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HRþ/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. Materials and methods: Data derived from the electronic health records (EHRs) of real-world patients with HRþ/HER2-negative advanced breast cancer were used to develop predictive models for early and late progression to first-line treatment. Two machine learning approaches were used: a classic approach using a data set of manually extracted features from reviewed (EHR) patients, and a second approach using natural language processing (NLP) of freetext clinical notes recorded during medical visits. Results: Of the 610 patients included, there were 473 (77.5%) progressions to first-line treatment, of which 126 (20.6%) occurred within the first 6 months. There were 152 patients (24.9%) who showed no disease progression before 28 months from the onset of first-line treatment. The best predictive model for early progression using the manually extracted dataset achieved an area under the curve (AUC) of 0.734 (95% CI 0.687e0.782). Using the NLP free-text processing approach, the best model obtained an AUC of 0.758 (95% CI 0.714 e0.800). The best model to predict long responders using manually extracted data obtained an AUC of 0.669 (95% CI 0.608e0.730). With NLP free-text processing, the best model attained an AUC of 0.752 (95% CI 0.705e0.799). Conclusions: Using machine learning methods, we developed predictive models for early and late progression to first-line treatment of HRþ/HER2-negative metastatic breast cancer, also finding that NLP-based machine learning models are slightly better than predictive models based on manually obtained data. eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Mamas - Cáncer Proceso en lenguaje natural (Informática) Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HRþ)/HER2-negative advanced breast cancer patients. journal article