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Showing content from https://pubmed.ncbi.nlm.nih.gov/30449493/ below:

A scoring system to predict recurrence in breast cancer patients

. 2018 Dec;27(4):681-687. doi: 10.1016/j.suronc.2018.09.005. Epub 2018 Sep 18. A scoring system to predict recurrence in breast cancer patients

Affiliations

¹ General Surgery Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, 03600, Alicante, Spain.
² Department of Clinical Medicine, Miguel Hernández University, Ctra Valencia-Alicante S/N, San Juan de Alicante, Alicante, 03550, Spain. Electronic address: apalazon@umh.es.
³ Department of Clinical Medicine, Miguel Hernández University, Ctra Valencia-Alicante S/N, San Juan de Alicante, Alicante, 03550, Spain.
⁴ General Surgery Service, Marina Baixa Hospital, Avda Alcalde En Jaume Botella Mayor 7, Villajoyosa, 03570, Alicante, Spain.
⁵ Oncology Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, Alicante, 03600, Spain.
⁶ Pathological Anatomy Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, Alicante, 03600, Spain.

PMID: 30449493
DOI: 10.1016/j.suronc.2018.09.005

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A scoring system to predict recurrence in breast cancer patients

Esther Paredes-Aracil et al. Surg Oncol. 2018 Dec.

. 2018 Dec;27(4):681-687. doi: 10.1016/j.suronc.2018.09.005. Epub 2018 Sep 18. Affiliations

¹ General Surgery Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, 03600, Alicante, Spain.
² Department of Clinical Medicine, Miguel Hernández University, Ctra Valencia-Alicante S/N, San Juan de Alicante, Alicante, 03550, Spain. Electronic address: apalazon@umh.es.
³ Department of Clinical Medicine, Miguel Hernández University, Ctra Valencia-Alicante S/N, San Juan de Alicante, Alicante, 03550, Spain.
⁴ General Surgery Service, Marina Baixa Hospital, Avda Alcalde En Jaume Botella Mayor 7, Villajoyosa, 03570, Alicante, Spain.
⁵ Oncology Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, Alicante, 03600, Spain.
⁶ Pathological Anatomy Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, Alicante, 03600, Spain.

PMID: 30449493
DOI: 10.1016/j.suronc.2018.09.005

Item in Clipboard

Abstract

Objective: Current breast cancer recurrence prediction models have limitations for clinical practice (statistical methodology, simplicity and specific populations). We therefore developed a new model that overcomes these limitations.

Methods: This cohort study comprised 272 patients with breast cancer followed between 2003 and 2016. The main variable was time-to-recurrence (locoregional and/or metastasis) and secondary variables were its risk factors: age, postmenopause, grade, oestrogen receptor, progesterone receptor, c-erbB2 status, stage, multicentricity, diagnosis and treatment. A Cox model to predict recurrence was estimated with the secondary variables, and this was adapted to a points system to predict risk at 5 and 10 years from diagnosis. The model was validated internally by bootstrapping, calculating the C statistic and smooth calibration (splines). The system was integrated into a mobile application for Android.

Results: Of the 272 patients with breast cancer, 47 (17.3%) developed recurrence in a mean time of 8.6 ± 3.5 years. The system variables were: age, grade, multicentricity and stage. Validation by bootstrapping showed good discrimination and calibration.

Conclusions: A points system has been developed to predict breast cancer recurrence at 5 and 10 years.

Keywords: Breast neoplasms; Mobile applications; Models; Recurrence; Statistical.

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