Investigation of Prognostic Factors of Survival in Breast Cancer Using a Frailty Model: A Multicenter Study

Yazdani, Akram; Yaseri, Mehdi; Haghighat, Setareh; Kaviani, Ahmad; Zeraati, Hojjat

doi:10.1177/1178223419879112

Cited by 11 publications

(12 citation statements)

References 42 publications

(52 reference statements)

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“…Breast cancer is one of the most common diseases among Iranian women. Oncological studies show that the prevalence of breast cancer among Iranian women is increasing, and the age of onset of this disease is decreasing with more disease intensity [ 8 , 16 ]. On the other hand, different factors influence the deterioration of the disease and its overall survival.…”

Section: Introductionmentioning

confidence: 99%

“…In Iran, due to the population growth, the increase of life expectancy, the relative increase of old population, the annual increase of cancer and the acceleration of change among influential factors in the escalation of cancer prevalence such as the prevalence and the development of the cancer risk factors, the rate of cancer development is predicted to rise and it will be doubled during the next two decades [ 8 , 16 ]. Therefore, this disease and programs to control are of utmost importance.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

et al. 2020

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Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

et al. 2020

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“…Our results revealed no significant relationship between diagnosis age and breast cancer patients' survival rate or cure probability. However, some studies have reported significant association [ 26 – 28 ].…”

Section: Discussionmentioning

confidence: 99%

Long-term outcomes of non-metastatic breast cancer patients by molecular subtypes

et al. 2022

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Background Today, with the progress of medical sciences, increasing the cure probability and survival time is an important goal of cancer treatment. This study compared long-term disease-free survival (DFS) of non-metastatic breast cancer patients based on different molecular subtypes. Methods This retrospective cohort study consisted of 1287 patients diagnosed with breast cancer and treated at Motamed Cancer Institute from 2000 to 2016 and followed up until 2018. Kaplan–Meier curve was fitted to data based on molecular subtypes. Then the semi-parametric mixture cure model was applied to determine the survival and cure probability of molecular subtypes by adjusting clinical and demographic factors. Results Among 1287 breast cancer patients, 200 (15.5%) cases died. The mean age of patients was 47.00 ± 10.72 years. Women with the HR+/HER2-subtype had the best 5-year survival rate (84.2%), whereas other subtypes had a lower rate as follows: HR+/HER2+ (77.3%), triple-negative (76.5%), and HR−/HER2+ (62.3%). Kaplan–Meier curve calculated a cure rate of about 60% and patients who survived more than 150 months were intuitively considered cured. After adjustment for clinical and demographic variables, the cure probability of HR−/Her2+ patients was substantially lower than HR+/HER2– patients (OR = 0.22), though there were no significant variations in short-term DFS based on molecular subtypes (HR = 0.91). Conclusions Our results confirm that the most prevalent breast cancer was HR+/HER2− tumor type which had the best prognosis. It is also concluded that HR−/HER2+ patients had the worst outcomes, with the highest rates of recurrence and metastasis and the lowest overall and disease-free survival rates.

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“…The Cox method models the hazard ratio rather than the survival time directly, and the complexity of the hazard ratio estimate interpretation is considered as a major problem. 4 , 11 The censored quantile regression (CQR) model seeks to model the survival time separately for each given quantile directly, and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risks and is a flexible model for controlling the heterogeneity of covariate effects.…”

Section: Introductionmentioning

confidence: 99%

“…That is at least 10 years lower than the world. 4 A major factor in reducing the survival of breast cancer patients is recurrence. 5 Cancer may come back in the same place as original cancer (local recurrence), or it may spread to other areas of your body (distant metastases).…”

Section: Introductionmentioning

confidence: 99%

Determining Prognostic Factors of Disease-Free Survival in Breast Cancer Using Censored Quantile Regression

Yazdani

Haghighat

2022

Breast Cancer�(Auckl)

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Background: The analysis of disease-free survival and related factors leads to a better understanding of the patient’s condition and recurrence-related characteristics and provides a basis for more appropriate treatment guidance. In this study, we aimed to investigate the role of prognostic factors on disease-free survival in breast cancer with a quantile regression model. Methods: This retrospective study was conducted by reviewing data obtained from 2056 breast cancer patients. Age at diagnosis and education status, tumor size, lymph node ratio, tumor grade, estrogen receptor and progesterone receptor, type of surgery, use of radiotherapy, chemotherapy, and hormone therapy were the prognosis factors considered in this study. A quantile regression model was used to investigate prognostic factors of disease-free survival in breast cancer. Results: Disease recurrence was verified in 251 (13.9%) women, and 39 (0.02%) women died before experience recurrence. The 10th percentile of disease-free survival for patients with the hormone therapy was 23.85 months greater than patients who did not receive this treatment ( P value < .001). In the examination of the tumor size, the 10th and 20th percentiles of disease-free survival for patients with tumor size > 5 cm were 31.06 and 27 months less than patients with the tumor size < 2 cm, respectively ( P value = .006 and .021, respectively). Compared with grade 1 tumors, the 10th and 20th percentiles of disease-free survival for patients with grade 3 tumors decreased 30.11 and 38.32 months, respectively ( P value < .001 and .038, respectively). The 10th and 20th percentiles of disease-free survival decreased 28.16 and 45.32 months with a 1 unit increase in lymph node ratio, respectively ( P value = .032 and .032, respectively). Conclusions: Among the prognostic factors, tumor size, grade, and lymph node ratio showed a close relationship with disease-free survival in breast cancer. The findings indicated that developing public screening and educational programs through the health care system with more emphasis on low-educated women is needed among Iranian women.

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Investigation of Prognostic Factors of Survival in Breast Cancer Using a Frailty Model: A Multicenter Study

Cited by 11 publications

References 42 publications

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

Long-term outcomes of non-metastatic breast cancer patients by molecular subtypes

Determining Prognostic Factors of Disease-Free Survival in Breast Cancer Using Censored Quantile Regression

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