Cost Analysis of Breast Cancer Diagnostic Assessment Programs

Honein-AbouHaidar, Gladys; Hoch, Jeffrey S.; Dobrow, Mark; Stuart-McEwan, Terri; McCready, David R.; Gagliardi, Anna R.

doi:10.3747/co.24.3608

Cited by 3 publications

(1 citation statement)

References 17 publications

(21 reference statements)

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“…Considerando el incremento de la demanda de mamografías en EsSalud y la RAR, sumado a tiempos incrementados para diagnóstico y tratamiento sin unidades de mamovigilancia en los establecimientos de salud o redes asistenciales para el seguimiento de las mujeres con sospecha clínica o mamográfica de cáncer, la implementación de una unidad de evaluación de diagnóstico de mama (Breast diagnostic assessment units -DAUs) para todas las mujeres con sospecha de cáncer de mama, debería considerarse a nivel institucional. También el desarrollo de estudios con diseños que permitan controlar a pacientes con y sin UMV en su referencia al tercer nivel en los establecimientos de salud, acompañados de la navegación de las pacientes, sería otra alternativa [26][27][28][29] .…”

Section: Discussionunclassified

Implementación de una unidad de mamovigilancia para el diagnóstico de cáncer de mama en una microred de la seguridad social, Lima-Perú

Figueroa-Montes¹,

Chávez-Altamirano

García-Espinoza

2019

Acta Med Peru

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Objetivo: Medir los tiempos (días) antes y después de la implementación de una unidad de mamovigilancia, para el seguimiento de las pacientes con sospecha mamográfica de cáncer, hasta su diagnóstico anatomopatológico o intervención clínica. Materiales y métodos: Se diseñó un estudio cuasi experimental pre-post, para comparar la mediana de los tiempos (días) en los intervalos de referencia y diagnóstico, sin la implementación de una unidad de mamovigilancia en el 2014-2015 y con su implementación en el 2017, en el Hospital IIISuarez Angamos - EsSalud. Se realizó un análisis no paramétrico con la prueba U de Mann-Whitney. Resultados: Se incluyeron 99 pacientes. El intervalo de referencia sin la unidad de mamovigilancia, tuvo una mediana de 37 días, en comparación a 33 días con la unidad implementada (p=0,003). Sobre el intervalo de diagnóstico, su mediana fue de 189 días sin la unidad y de 56 días con la unidad de mamovigilancia implementada (p<0,001). Conclusión: La implementación de una unidad de mamovigilancia disminuye los tiempos en días en beneficio del diagnóstico oportuno del cáncer en pacientes con sospecha mamográfica en la Red Asistencial Rebagliati-EsSalud.

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Section: Discussionunclassified

Implementación de una unidad de mamovigilancia para el diagnóstico de cáncer de mama en una microred de la seguridad social, Lima-Perú

Figueroa-Montes¹,

Chávez-Altamirano

García-Espinoza

2019

Acta Med Peru

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An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

Zhang

Deng

Liang

et al. 2018

BioMed Research International

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The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes.

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Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model

Chen

Gao

Zhang

et al. 2019

BioMed Research International

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Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction.

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Cost Analysis of Breast Cancer Diagnostic Assessment Programs

Cited by 3 publications

References 17 publications

Implementación de una unidad de mamovigilancia para el diagnóstico de cáncer de mama en una microred de la seguridad social, Lima-Perú

Implementación de una unidad de mamovigilancia para el diagnóstico de cáncer de mama en una microred de la seguridad social, Lima-Perú

An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model

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