Overall survival analyses of female malignancies in Southern Brazil during 2008–2017: A closer look at breast, cervical and ovarian cancer

Schneider, Ione Jayce Ceola; Schmidt, Tauana Prestes; Santos, Ana Maria Martins dos; Corrêa, Vanessa Pereira; Garcia, Leandro Pereira; Oliveira, César de; Franzoi, Maria Alice

doi:10.1016/j.dialog.2022.100010

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Dialogues in Health

2022

DOI: 10.1016/j.dialog.2022.100010

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Overall survival analyses of female malignancies in Southern Brazil during 2008–2017: A closer look at breast, cervical and ovarian cancer

Ione Jayce Ceola Schneider

Tauana Prestes Schmidt

Ana Maria Martins dos Santos

et al.

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2024

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Cited by 2 publications

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Cancer remains one of the most significant global health challenges [1,2]. Among the various types of cancer affecting women worldwide, cervical cancer (CC) is particularly dangerous [3,4]. In 2020, CC ranked as the fourth most common cancer among women, with 604,000 new cases and 342,000 deaths globally [5].

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confidence: 99%

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Chanudom,

Tharavichitkul,

Laosiritaworn

2024

Healthc Inform Res

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Objectives: The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem.Methods: This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient’s death. The intervals were categorized as “<6 months,” “6 months to 3 years,” “3 years to 5 years,” and “>5 years.” The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model’s behavior and decision-making process.Results: The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration.Conclusions: Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.

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confidence: 99%

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Chanudom,

Tharavichitkul,

Laosiritaworn

2024

Healthc Inform Res

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Factors delaying non‐metastatic breast cancer adjuvant therapy and impact on prognosis in a cohort of Brazilian women

Veiga,

Medeiros,

de Aguiar

et al. 2024

Evaluation Clinical Practice

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ObjectiveTo identify factors associated with delays in beginning adjuvant therapy and prognosis impacts on non‐metastatic breast cancer patients.MethodsThis assessment comprised a prospective cohort study concerning breast cancer patients treated at a public oncology centre. A time interval of ≥60 days between surgery and the beginning of the first adjuvant treatment was categorised as a delay. Factors associated with delays were evaluated through logistic regression analysis and the prognosis effects were assessed by a Cox regression analysis.ResultsThe median time interval between surgery and the first adjuvant treatment for the 401 women included in this study was of 57.0 days (37.0–93.0). Independent factors associated with delays comprised not presenting an overexpression of the HER‐2 protein, not having undergone neoadjuvant chemotherapy, and having undergone chemotherapy or other therapeutic modalities other than hormone therapy and chemotherapy as the first adjuvant treatment. Delays did not affect recurrence, distant metastasis, or death risks. Factors associated with recurrence and distant metastasis risks comprised a clinical staging ≥2B, having undergone neoadjuvant chemotherapy, presenting the luminal molecular subtype B and triple‐negative tumours, and having children. Factors associated with death comprised triple‐negative molecular tumours and neoadjuvant chemotherapy.ConclusionDelays in beginning adjuvant treatment did not affect the prognosis of non‐metastatic breast cancer patients. Clinical and treatment‐related factors, on the other hand, were associated with delays, and recurrence, distant metastasis, and death risks.

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Overall survival analyses of female malignancies in Southern Brazil during 2008–2017: A closer look at breast, cervical and ovarian cancer

Cited by 2 publications

References 47 publications

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Factors delaying non‐metastatic breast cancer adjuvant therapy and impact on prognosis in a cohort of Brazilian women

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