Efectos del tratamiento con quimioterapia y radioterapia concomitante sobre los volúmenes pulmonares en mujeres con cáncer de mama en Talca, Chile

Valverde-Ampai, Washington; Palma-Rozas, Gonzalo; Conei, Daniel; Marzuca‐Nassr, Gabriel Nasri; Medina-González, Paul; Escobar-Cabello, Máximo; Sol, Mariano del; Muñoz-Cofré, Rodrigo

doi:10.15446/revfacmed.v68n2.73402

Cited by 2 publications

(2 citation statements)

References 26 publications

(24 reference statements)

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“…The results obtained show a similar decline in lung capacities, specifically the IC of patients with DM which was not significant; however this variable was significantly smaller than the CG when the condition of BC was added (Figure 1). The evidence indicates that the IC is equivalent to between 50 and 60% of the TLC and that both are sensitive to restrictive patterns; this is important if we consider that Sreeram (2016), in a study of 90 subjects with DM2, showed the existence of a restrictive pattern in 55% of the cases [35].…”

Section: Discussionmentioning

confidence: 91%

Effects of Breast Cancer and Type 2 Diabetes Mellitus on Pulmonary Volumes and Pressures in Adult Women

Cofré¹,

Sol²,

Rozas³

et al. 2019

Preprint

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Background and Objective: Type 2 diabetes mellitus (DM2) and breast cancer (BC) are diseases of high prevalence worldwide. Both alter lung function separately. So suffering both tables would increase this decrease in lung function. The objetive was to determine the effects of DM2 and BC on ventilation volumes and pressures in adult women. Material and Methods: Forty-two women patients were recruited, of whom 40 were accepted under the exclusion criteria. They were divided into four groups: control group (CG), DM2, BC and DM2+BC. Body plethysmography was used to measure forced vital capacity, lung volumes, airway resistance and muscle pressures. Finally the normality of the data was determined using Student's t test or the Mann-Whitney U test; the threshold of significance was p<0.05. Results: No significant differences were observed in the anthropometric variables between the control group and the other groups. The ventilation flows showed no significant differences, while the lung volumes presented significant differences in the inspiratory capacity (IC) variables (p<0,002). Maximum inspiratory and expiratory pressures (MIP-MEP) also presented significant diminution (p<0,001; p<0,041, respectively). Conclusions: From the results obtained we can conclude that the combination of type 2 diabetes mellitus with breast cancer caused a diminution in ventilation volumes and pressures, specifically in IC, MIP and MEP.

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Section: Discussionmentioning

confidence: 91%

Effects of Breast Cancer and Type 2 Diabetes Mellitus on Pulmonary Volumes and Pressures in Adult Women

Cofré¹,

Sol²,

Rozas³

et al. 2019

Preprint

Self Cite

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“…Other diverse strategies have been studied to manage breast cancer, such as machine learning (see, e.g., Nindrea et al [34], Magna et al [35], Acevedo et al [36], Yu et al [37]), Delphi techniques (Iunes et al [38]), spatial autocorrelation (Durán and Monsalves [39]), and medical techniques such as genetic epidemiology, chemotherapy, radiotherapy, telerehabilitation (see, e.g., Ramírez-Parada et al [40], Zavala et al [41], Mella-Abarca et al [42], Valverde-Ampai et al [43]), among others.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Silva-Aravena

Delafuente

Gutiérrez-Bahamondes

et al. 2023

Cancers

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Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

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Efectos del tratamiento con quimioterapia y radioterapia concomitante sobre los volúmenes pulmonares en mujeres con cáncer de mama en Talca, Chile

Cited by 2 publications

References 26 publications

Effects of Breast Cancer and Type 2 Diabetes Mellitus on Pulmonary Volumes and Pressures in Adult Women

Effects of Breast Cancer and Type 2 Diabetes Mellitus on Pulmonary Volumes and Pressures in Adult Women

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

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