Leukemia Risk after Adjuvant Treatment of Early Breast Cancer

Balduzzi, Alessandra; Castiglione‐Gertsch, Monica

doi:10.1517/17455057.1.1.073

Cited by 2 publications

(2 citation statements)

References 54 publications

(85 reference statements)

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“…To our knowledge, this large population-based study is the first to evaluate racial/ethnic differences in risk of second primary cancers among invasive BC survivors between 2001 and 2010. Consistent with existing literature [16][17][18][19][20], our analysis demonstrates that BC survivors across all racial/ethnic backgrounds were frequently at increased risks of second primary BC and AML, with the greatest risk in more advanced stage index BC. Our study also suggests that Black and Asian/Pacific Islander women have increased relative risk and excess risk of AML that remains elevated even among early-stage (Stage I) invasive BC cases.…”

Section: Discussionsupporting

confidence: 89%

Racial and ethnic differences in risk of second primary cancers among breast cancer survivors.

Calip

Law

Winer

et al. 2015

JCO

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Purpose-Disparities exist in breast cancer (BC) outcomes between racial/ethnic groups in the United States. Reasons for these disparities are multifactorial including differences in genetics, stage at presentation, access to care and socioeconomic factors. Less is documented on racial/ ethnic differences in subsequent risk of second primary cancers (SPC). The purpose of this study is to evaluate the risk of SPC among different racial/ethnic groups of women with BC. Methods-Retrospective cohort of 134,868 Non-Hispanic White (NHW), 17,484 Black, 18,034 Hispanic and 19,802 Asian/Pacific Islander (API) women with stages I-III BC in twelve Surveillance, Epidemiology and End Results Program registries between 2001-2010. Standardized incidence ratios (SIR), 95% confidence intervals (CI) and absolute excess risks were calculated by comparing incidence of SPC in the cohort to incidence in the general population for specific cancer sites by race/ethnicity and stratified by index BC characteristics. Results-All women were at increased risks of second primary BC and acute myeloid leukemia (AML), with higher risk among more advanced stage index BC. Black and API women had higher SIRs for AML [4.86 (95% CI 3.05-7.36) and 5.00 (95% CI 3.26-7.32) respectively] which remained elevated among early-stage (I) BC cases. Conclusions-Women with a history of invasive BC have increased risk of SPC, most notable for second primary BC and AML. These risks for secondary cancers differ by race/ethnicity. Studies evaluating possible genetic and biobehavioral mechanisms underlying these differences

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Section: Discussionsupporting

confidence: 89%

Racial and ethnic differences in risk of second primary cancers among breast cancer survivors.

Calip

Law

Winer

et al. 2015

JCO

View full text Add to dashboard Cite

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“…Mehrdad et al [7] reported a case study of a young female with breast cancer that developed acute myelogenous leukemia (AML) malignancy within a limited time of treatment. Balduzzi et al [8] revealed that most of the patients who showed secondary Leukemia after chemotherapy for their primary breast cancer had been treated with the combined use of alkylating agents and radiation therapy.…”

Section: Introductionmentioning

confidence: 99%

Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

Deif¹,

Hammam²,

Solyman³

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease.

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Leukemia Risk after Adjuvant Treatment of Early Breast Cancer

Cited by 2 publications

References 54 publications

Racial and ethnic differences in risk of second primary cancers among breast cancer survivors.

Racial and ethnic differences in risk of second primary cancers among breast cancer survivors.

Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

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