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An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel: Development and usability study.
Lin, Cheng-Yao; Chien, Tsair-Wei; Chen, Yen-Hsun; Lee, Yen-Ling; Su, Shih-Bin.
Afiliación
  • Lin CY; Division of Hematology-Oncology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan.
  • Chien TW; Department of Senior Welfare and Services, Southern Taiwan University of Science and Technology, Tainan, Taiwan.
  • Chen YH; Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan.
  • Lee YL; Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan.
  • Su SB; Division of Hematology-Oncology, Department of Internal Medicine, Chi Mei Center, Liouying, Tainan, Taiwan.
Medicine (Baltimore) ; 101(4): e28697, 2022 Jan 28.
Article en En | MEDLINE | ID: mdl-35089226
ABSTRACT

BACKGROUND:

Breast cancer (BC) is the most common malignant cancer in women. A predictive model is required to predict the 5-year survival in patients with BC (5YSPBC) and improve the treatment quality by increasing their survival rate. However, no reports in literature about apps developed and designed in medical practice to classify the 5YSPBC. This study aimed to build a model to develop an app for an automatically accurate classification of the 5YSPBC.

METHODS:

A total of 1810 patients with BC were recruited in a hospital in Taiwan from the secondary data with codes on 53 characteristic variables that were endorsed by professional staff clerks as of December 31, 2019. Five models (i.e., revolution neural network [CNN], artificial neural network, Naïve Bayes, K-nearest Neighbors Algorithm, and Logistic regression) and 3 tasks (i.e., extraction of feature variables, model comparison in accuracy [ACC] and stability, and app development) were performed to achieve the goal of developing an app to predict the 5YSPBC. The sensitivity, specificity, and receiver operating characteristic curve (area under ROC curve) on models across 2 scenarios of training (70%) and testing (30%) sets were compared. An app predicting the 5YSPBC was developed involving the model estimated parameters for a website assessment.

RESULTS:

We observed that the 15-variable CNN model yields higher ACC rates (0.87 and 0.86) with area under ROC curves of 0.80 and 0.78 (95% confidence interval 0.78-82 and 0.74-81) based on 1357 training and 540 testing cases an available app for patients predicting the 5YSPBC was successfully developed and demonstrated in this study.

CONCLUSION:

The 15-variable CNN model with 38 parameters estimated using CNN for improving the ACC of the 5YSPBC has been particularly demonstrated in Microsoft Excel. An app developed for helping clinicians assess the 5YSPBC in clinical settings is required for application in the future.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Redes Neurales de la Computación / Aplicaciones Móviles Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Aged / Aged80 / Female / Humans / Middle aged Idioma: En Revista: Medicine (Baltimore) Año: 2022 Tipo del documento: Article País de afiliación: Taiwán

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Redes Neurales de la Computación / Aplicaciones Móviles Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Aged / Aged80 / Female / Humans / Middle aged Idioma: En Revista: Medicine (Baltimore) Año: 2022 Tipo del documento: Article País de afiliación: Taiwán
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