Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to...Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer’s favourable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model’s overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, F1 score and finally the AUC & ROC curve. In this study of hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.展开更多
Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of br...Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.展开更多
Breast cancer is one of the most common cancers among women in the world, with more than two million new cases of breast cancer every year. This disease is associated with numerous clinical and genetic characteristics...Breast cancer is one of the most common cancers among women in the world, with more than two million new cases of breast cancer every year. This disease is associated with numerous clinical and genetic characteristics. In recent years, machine learning technology has been increasingly applied to the medical field, including predicting the risk of malignant tumors such as breast cancer. Based on clinical and targeted sequencing data of 1980 primary breast cancer samples, this article aimed to analyze these data and predict living conditions after breast cancer. After data engineering, feature selection, and comparison of machine learning methods, the light gradient boosting machine model was found the best with hyperparameter tuning (precision = 0.818, recall = 0.816, f1 score = 0.817, roc-auc = 0.867). And the top 5 determinants were clinical features age at diagnosis, Nottingham Prognostic Index, cohort and genetic features rheb, nr3c1. The study shed light on rational allocation of medical resources and provided insights to early prevention, diagnosis and treatment of breast cancer with the identified risk clinical and genetic factors.展开更多
Objective： To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy （NET） in postmenopausal breast cancer. Methods： Consecutive 160 postmenopausal women with T 1-3 N 0-1 M 0...Objective： To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy （NET） in postmenopausal breast cancer. Methods： Consecutive 160 postmenopausal women with T 1-3 N 0-1 M 0 hormone receptor （HR）-positive invasive breast cancer were treated with anastrozole for 16 weeks before surgery. New slides of tumor specimens taken before and after treatment were conducted centrally for biomarker analysis and classified using the Applied Imaging Ariol MB-8 system. The pathological response was evaluated using the Miller ＆ Payne classification. The cell cycle response was classified according to the change in the Ki67 index after treatment. Multivariable logistic regression analysis was used to calculate the combined index of the biomarkers. Receiver operating characteristic （ROC） curves were used to determine whether parameters may predict response. Results： The correlation between the pathological and cell cycle responses was low （Spearman correlation coefficient =0.241, P〈0.001; Kappa value =0.119, P=0.032）. The cell cycle response was significantly associated with pre-treatment estrogen receptor （ER） status （P=0.001）, progesterone receptor （PgR） status （P〈0.001）, human epidermal growth factor receptor 2 （Her-2） status （P=0.050） and the Ki67 index （P〈0.001）, but the pathological response was not correlated with these factors. Pre-treatment ER levels [area under the curve （AUC） =0.634, 95% confidence interval （95% CI）, 0.534-0.735, P=0.008] and combined index of pre-treatment ER and PgR levels （AUC =0.684, 95% CI, 0.591-0.776, P〈0.001） could not predict the cell cycle response, but combined index including per-treatment ER/PR/Her-2/Ki67 expression levels could （AUC =0.830, 95% CI, 0.759-0.902, P〈0.001）. Conclusions： The combined use of pre-treatment ER/PgR/Her-2/Ki67 expression levels, instead of HR expression levels, may predict the cell cycle response to NET.展开更多
文摘Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer’s favourable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model’s overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, F1 score and finally the AUC & ROC curve. In this study of hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.
基金supported by the KIAS(Research No.CG076601)in part by Sejong University Faculty Research Fund.
文摘Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.
文摘Breast cancer is one of the most common cancers among women in the world, with more than two million new cases of breast cancer every year. This disease is associated with numerous clinical and genetic characteristics. In recent years, machine learning technology has been increasingly applied to the medical field, including predicting the risk of malignant tumors such as breast cancer. Based on clinical and targeted sequencing data of 1980 primary breast cancer samples, this article aimed to analyze these data and predict living conditions after breast cancer. After data engineering, feature selection, and comparison of machine learning methods, the light gradient boosting machine model was found the best with hyperparameter tuning (precision = 0.818, recall = 0.816, f1 score = 0.817, roc-auc = 0.867). And the top 5 determinants were clinical features age at diagnosis, Nottingham Prognostic Index, cohort and genetic features rheb, nr3c1. The study shed light on rational allocation of medical resources and provided insights to early prevention, diagnosis and treatment of breast cancer with the identified risk clinical and genetic factors.
文摘Objective： To determine the predictive ability of biomarkers for responses to neoadjuvant endocrine therapy （NET） in postmenopausal breast cancer. Methods： Consecutive 160 postmenopausal women with T 1-3 N 0-1 M 0 hormone receptor （HR）-positive invasive breast cancer were treated with anastrozole for 16 weeks before surgery. New slides of tumor specimens taken before and after treatment were conducted centrally for biomarker analysis and classified using the Applied Imaging Ariol MB-8 system. The pathological response was evaluated using the Miller ＆ Payne classification. The cell cycle response was classified according to the change in the Ki67 index after treatment. Multivariable logistic regression analysis was used to calculate the combined index of the biomarkers. Receiver operating characteristic （ROC） curves were used to determine whether parameters may predict response. Results： The correlation between the pathological and cell cycle responses was low （Spearman correlation coefficient =0.241, P〈0.001; Kappa value =0.119, P=0.032）. The cell cycle response was significantly associated with pre-treatment estrogen receptor （ER） status （P=0.001）, progesterone receptor （PgR） status （P〈0.001）, human epidermal growth factor receptor 2 （Her-2） status （P=0.050） and the Ki67 index （P〈0.001）, but the pathological response was not correlated with these factors. Pre-treatment ER levels [area under the curve （AUC） =0.634, 95% confidence interval （95% CI）, 0.534-0.735, P=0.008] and combined index of pre-treatment ER and PgR levels （AUC =0.684, 95% CI, 0.591-0.776, P〈0.001） could not predict the cell cycle response, but combined index including per-treatment ER/PR/Her-2/Ki67 expression levels could （AUC =0.830, 95% CI, 0.759-0.902, P〈0.001）. Conclusions： The combined use of pre-treatment ER/PgR/Her-2/Ki67 expression levels, instead of HR expression levels, may predict the cell cycle response to NET.
