A scoring system to predict recurrence in breast cancer patients
| Autor: | José Vicente Coloma-Lidón, Vicente Francisco Gil-Guillén, José Ramón Ots-Gutiérrez, David Manuel Folgado-de la Rosa, Cristina Llorca-Ferrándiz, Sonia Alonso-Hernández, Antonio Palazón-Bru, Esther Paredes-Aracil |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Oncology
medicine.medical_specialty Breast Neoplasms Metastasis Cohort Studies 03 medical and health sciences 0302 clinical medicine Breast cancer Mobile applications Recurrence Models Internal medicine medicine Humans 030212 general & internal medicine Stage (cooking) Statistic Bootstrapping (statistics) Models Statistical Proportional hazards model business.industry Carcinoma Ductal Breast Middle Aged Statistical Prognosis medicine.disease Combined Modality Therapy Carcinoma Lobular Lymphatic Metastasis 030220 oncology & carcinogenesis Female Surgery Neoplasm Recurrence Local Breast neoplasms business Predictive modelling Follow-Up Studies Cohort study |
| Zdroj: | SURGICAL ONCOLOGY-OXFORD r-FISABIO. Repositorio Institucional de Producción Científica instname r-FISABIO: Repositorio Institucional de Producción Científica Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO) r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante |
| ISSN: | 0960-7404 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect