Journal articles on the topic 'Breast cancer prediction models'
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Kumar, Mukesh, Saurabh Singhal, Shashi Shekhar, Bhisham Sharma, and Gautam Srivastava. "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning." Sustainability 14, no. 21 (October 27, 2022): 13998. http://dx.doi.org/10.3390/su142113998.
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Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.
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McCarthy, Anne Marie, Zoe Guan, Michaela Welch, Molly E. Griffin, Dorothy A. Sippo, Zhengyi Deng, Suzanne B. Coopey, et al. "Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort." JNCI: Journal of the National Cancer Institute 112, no. 5 (September 26, 2019): 489–97. http://dx.doi.org/10.1093/jnci/djz177.
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Abstract Background Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. Methods We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40–84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. Results Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. Conclusions In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.
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Engel, Christoph, and Christine Fischer. "Breast Cancer Risks and Risk Prediction Models." Breast Care 10, no. 1 (2015): 7–12. http://dx.doi.org/10.1159/000376600.
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BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.
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Sridevi, S. "BREAST CANCER PREDICTION WITH HYBRID ML MODELS." YMER Digital 21, no. 05 (May 31, 2022): 1524–28. http://dx.doi.org/10.37896/ymer21.05/g6.
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In recent times various types of cancer propagation in humans are alarmingly increasing and especially women are prone to and threatened by breast cancer with high morbidity and mortality. The absence of robust prognosis models results in difficulty for physicians to prepare a treatment plan that may extend patient survival chances and time. Hence, the need of the time is to develop the technique which offers minimum error with increased accuracy. Different legacy algorithms like SVM, Regression, are compared with the proposed hybrid prediction model outcome. All experiments are executed within a parallel environment and conducted in anaconda python platform with relevant libraries. This is helpful in domains like. prediction of cancer before diagnosis, prediction of diagnosis and outcome during treatment. The proposed work combining detailed pre-prepressing stages over a deep neural network model with tuned hyper parameters, validated to yield needed accuracy. This can be used to derive and compare the outcome of different techniques and suitable one having max accuracy and stability, can be used depending upon requirement. Different data sets are tried and analysed for prediction with different parameters and results are compared. Keywords — Breast Cancer detection, machine learning, feature selection, classification, hybrid deep learning, image classification, KNN , Random Forest, ROC.
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Antoniou, Antonis C., and Douglas F. Easton. "Risk prediction models for familial breast cancer." Future Oncology 2, no. 2 (April 2006): 257–74. http://dx.doi.org/10.2217/14796694.2.2.257.
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Zheng, Yadi, Jiang Li, Zheng Wu, He Li, Maomao Cao, Ni Li, and Jie He. "Risk prediction models for breast cancer: a systematic review." BMJ Open 12, no. 7 (July 2022): e055398. http://dx.doi.org/10.1136/bmjopen-2021-055398.
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ObjectivesTo systematically review and critically appraise published studies of risk prediction models for breast cancer in the general population without breast cancer, and provide evidence for future research in the field.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesPubMed, the Cochrane Library and Embase were searched from inception to 16 December 2021.Eligibility criteriaWe included studies reporting multivariable models to estimate the individualised risk of developing female breast cancer among different ethnic groups. Search was limited to English language only.Data extraction and synthesisTwo reviewers independently screened, reviewed, extracted and assessed studies with discrepancies resolved through discussion or a third reviewer. Risk of bias was assessed according to the PROBAST framework.Results63 894 studies were screened and 40 studies with 47 risk prediction models were included in the review. Most of the studies used logistic regression to develop breast cancer risk prediction models for Caucasian women by case–control data. The most widely used risk factor was reproductive factors and the highest area under the curve was 0.943 (95% CI 0.919 to 0.967). All the models included in the review had high risk of bias.ConclusionsNo risk prediction models for breast cancer were recommended for different ethnic groups and models incorporating mammographic density or single-nucleotide polymorphisms among Asian women are few and poorly needed. High-quality breast cancer risk prediction models assessed by PROBAST should be developed and validated, especially among Asian women.PROSPERO registration numberCRD42020202570.
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Xiong, Wei, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, and Jiebo Luo. "Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders." BME Frontiers 2022 (April 7, 2022): 1–10. http://dx.doi.org/10.34133/2022/9763284.
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Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal-related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational autoencoder (T-VAE) model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.
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Montazeri, Mitra, Mohadeseh Montazeri, Mahdieh Montazeri, and Amin Beigzadeh. "Machine learning models in breast cancer survival prediction." Technology and Health Care 24, no. 1 (January 27, 2016): 31–42. http://dx.doi.org/10.3233/thc-151071.
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Chaurasia, Vikas, Saurabh Pal, and BB Tiwari. "Prediction of benign and malignant breast cancer using data mining techniques." Journal of Algorithms & Computational Technology 12, no. 2 (February 20, 2018): 119–26. http://dx.doi.org/10.1177/1748301818756225.
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Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.
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Zhao, Melissa, Yushi Tang, Hyunkyung Kim, and Kohei Hasegawa. "Machine Learning With K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients With Breast Cancer." Cancer Informatics 17 (January 2018): 117693511881021. http://dx.doi.org/10.1177/1176935118810215.
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Objective: Despite existing prognostic markers, breast cancer prognosis remains a difficult subject due to the complex relationships between many contributing factors and survival. This study seeks to integrate multiple clinicopathological and genomic factors with dimensional reduction across machine learning algorithms to compare survival predictions. Methods: This is a secondary analysis of the data from a prospective cohort study of female patients with breast cancer enrolled in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We constructed a series of predictive models: ensemble models (Gradient Boosting and Random Forest), support vector machine (SVM), and artificial neural networks (ANN) for 5-year survival based on clinicopathological and gene expression data after K-means clustering with K-nearest-neighbor (KNN) classification. Model performance was evaluated by receiver operating characteristic (ROC) curve, accuracy, and calibration slope (CS). Model stability was assessed over 10 random runs in terms of ROC, accuracy, CS, and variable importance. Results: The analytic cohort is composed of 1874 patients with breast cancer. Overall, the median age was 62 years; the 5-year survival rate was 75%. ROC and accuracy were not significantly different between models (ROC and accuracy around 0.67 and 0.72 across models, respectively). However, ensemble methods resulted in better fit (CS) with stable measures of variable importance across 10 random training/validation splits. K-means clustering of gene expression profiles on training data points along with KNN classification of validation data points was a robust method of dimensional reduction. Furthermore, the gene expression cluster with the highest mortality risk was an influential factor in model prediction. Conclusions: Using machine learning methods to construct predictive models for 5-year survival in patients with breast cancer, we demonstrated discrimination ability across models with new insight into the stability and utility of dimensional reduction on genomic features in breast cancer survival prediction.
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Domchek, Susan M., Andrea Eisen, Kathleen Calzone, Jill Stopfer, Anne Blackwood, and Barbara L. Weber. "Application of Breast Cancer Risk Prediction Models in Clinical Practice." Journal of Clinical Oncology 21, no. 4 (February 15, 2003): 593–601. http://dx.doi.org/10.1200/jco.2003.07.007.
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Breast cancer risk assessment provides an estimation of disease risk that can be used to guide management for women at all levels of risk. In addition, the likelihood that breast cancer risk is due to specific genetic susceptibility (such as BRCA1 or BRCA2 mutations) can be determined. Recent developments have reinforced the clinical importance of breast cancer risk assessment. Tamoxifen chemoprevention as well as prevention studies such as the Study of Tamoxifen and Raloxifene are available to women at increased risk of developing breast cancer. In addition, specific management strategies are now defined for BRCA1 and BRCA2 mutation carriers. Risk may be assessed as the likelihood of developing breast cancer (using risk assessment models) or as the likelihood of detecting a BRCA1 or BRCA2 mutation (using prior probability models). Each of the models has advantages and disadvantages, and all need to be interpreted in context. We review available risk assessment tools and discuss their application. As illustrated by clinical examples, optimal counseling may require the use of several models, as well as clinical judgment, to provide the most accurate and useful information to women and their families.
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Dutta, Shawni, Jyotsna Kumar Mandal, Tai Hoon Kim, and Samir Kumar Bandyopadhyay. "Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN." Applied Computer Systems 25, no. 2 (December 1, 2020): 163–71. http://dx.doi.org/10.2478/acss-2020-0018.
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Abstract Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed model is compared against other baseline classifiers such as stacked simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.
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Jiang, Shu (Joy), and Graham A. Colditz. "Abstract B014: Improving prediction of second events after DCIS using whole breast mammogram images." Cancer Prevention Research 15, no. 12_Supplement_1 (December 1, 2022): B014. http://dx.doi.org/10.1158/1940-6215.dcis22-b014.
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Abstract Purpose: We aim to improve prediction of subsequent events after DCIS. Current models show relatively poor performance predicting subsequent events and often are limited to ipsilateral events. National population-based SEER data including over160,000 cases of DCIS and followed an average of 90 months indicate that second events (including risk of invasive breast cancer – IBC) are equality likely in the ipsilateral and contralateral breast. Thus, additional information on risk may be obtained by more complete modeling of risk estimating recurrence. Mammographic images have been largely ignored despite evidence that even breast density improves prediction of breast cancer in the general population. Furthermore, we have shown whole breast mammogram images (WBI) included in Cox models significantly improves prediction of breast cancer risk over using mammographic breast density alone and breast cancer risk factors. Population: We retrieved digital mammograms for women diagnosed with DCIS at Washington University School of Medicine from 2008 onwards and followed for subsequent diagnosis of pathology confirmed breast lesions. Using more than 100 cases and controls matched on year of mammogram and age we fit Cox models to evaluate risk of ipsilateral invasive breast cancer. To compare and accommodate laterality of subsequent breast lesions, we fit competing risk models using baseline covariates that are available on the women-level as well as WBI that are side-specific. Results: Preliminary analysis shows WBI predicts subsequent invasive breast cancer. Further, we will demonstrate appropriate models adjusting for competing risk gives unbiased estimates of invasive breast cancer. Conclusion: WBI and competing risk analysis improves our understanding of associations for side of occurrence of subsequent breast lesions after an initial diagnosis of DCIS. Citation Format: Shu (Joy) Jiang, Graham A. Colditz. Improving prediction of second events after DCIS using whole breast mammogram images [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr B014.
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Cheng, Skye H., Cheng-Fang Horng, Mike West, Erich Huang, Jennifer Pittman, Mei-Hua Tsou, Holly Dressman, et al. "Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer." Journal of Clinical Oncology 24, no. 28 (October 1, 2006): 4594–602. http://dx.doi.org/10.1200/jco.2005.02.5676.
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Purpose This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. Patients and Methods A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. Results Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. Conclusion Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.
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Derouane, Françoise, Cédric van Marcke, Martine Berlière, Amandine Gerday, Latifa Fellah, Isabelle Leconte, Mieke R. Van Bockstal, Christine Galant, Cyril Corbet, and Francois P. Duhoux. "Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine." Cancers 14, no. 16 (August 11, 2022): 3876. http://dx.doi.org/10.3390/cancers14163876.
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Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.
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Minnier, Jessica, Nallakkandi Rajeevan, Lina Gao, Byung Park, Saiju Pyarajan, Paul Spellman, Sally G. Haskell, Cynthia A. Brandt, and Shiuh-Wen Luoh. "Polygenic Breast Cancer Risk for Women Veterans in the Million Veteran Program." JCO Precision Oncology, no. 5 (July 2021): 1178–91. http://dx.doi.org/10.1200/po.20.00541.
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PURPOSE Accurate breast cancer (BC) risk assessment allows personalized screening and prevention. Prospective validation of prediction models is required before clinical application. Here, we evaluate clinical- and genetic-based BC prediction models in a prospective cohort of women from the Million Veteran Program. MATERIALS AND METHODS Clinical BC risk prediction models were validated in combination with a genetic polygenic risk score of 313 (PRS313) single-nucleotide polymorphisms in genetic females without prior BC diagnosis (n = 35,130, mean age 49 years) with 30% non-Hispanic African ancestry (AA). Clinical risk models tested were Breast and Prostate Cancer Cohort Consortium, literature review, and Breast Cancer Risk Assessment Tool, and implemented with or without PRS313. Prediction accuracy and association with incident breast cancer was evaluated with area under the receiver operating characteristic curve (AUC), hazard ratios, and proportion with high absolute lifetime risk. RESULTS Three hundred thirty-eight participants developed incident breast cancers with a median follow-up of 3.9 years (2.5 cases/1,000 person-years), with 196 incident cases in women of European ancestry and 112 incident cases in AA women. Individualized Coherent Absolute Risk Estimator-literature review in combination with PRS313 had an AUC of 0.708 (95% CI, 0.659 to 0.758) in women with European or non-African ancestries and 0.625 (0.539 to 0.711) in AA women. Breast Cancer Risk Assessment Tool with PRS313 had an AUC of 0.695 (0.62 to 0.729) in European or non-AA and 0.675 (0.626 to 0.723) in AA women. Incorporation of PRS313 with clinical models improved prediction in European but not in AA women. Models estimated up to 9% of European and 18% of AA women with absolute lifetime risk > 20%. CONCLUSION Clinical and genetic BC risk models predict incident BC in a large prospective multiracial cohort; however, more work is needed to improve genetic risk estimation in AA women.
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Sohrabei, Solmaz, and Alireza Atashi. "Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data." Frontiers in Health Informatics 10, no. 1 (July 25, 2021): 83. http://dx.doi.org/10.30699/fhi.v10i1.296.
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Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8.Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.
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Kim, Geunwon, and Manisha Bahl. "Assessing Risk of Breast Cancer: A Review of Risk Prediction Models." Journal of Breast Imaging 3, no. 2 (February 19, 2021): 144–55. http://dx.doi.org/10.1093/jbi/wbab001.
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Abstract Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman’s breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.
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LAITMAN, YAEL, MONICA SIMEONOV, LITAL KEINAN-BOKER, IRENA LIPHSHITZ, and EITAN FRIEDMAN. "Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models." Genetics Research 95, no. 6 (December 2013): 174–77. http://dx.doi.org/10.1017/s0016672313000232.
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SummarySeveral breast cancer risk prediction models have been validated in ethnically diverse populations, but none in Israeli high-risk women. To validate the accuracy of the IBIS and BOADICEA risk prediction models in Israeli high-risk women, the 10-year and lifetime risk for developing breast cancer were calculated using both BOADICEA and IBIS models for high-risk, cancer-free women, counselled at the Sheba Medical Center from 1 June 1996–31 May 2000. Women diagnosed with breast cancer by 31 May 2011 were identified from the Israeli National Cancer Registry. The observed to expected breast cancer ratios were calculated to evaluate the predictive value of both algorithms. Overall, 358 mostly (N = 205, 57·2%) Ashkenazi women, were eligible, age range at counselling was 20–75 years (mean 46·76 ± 9·8 years). Over 13·6 ± 1·45 years (range 11–16 years), 15 women (4·19%) were diagnosed with breast cancer, at a mean age of 57 ± 8·6 years. The 10-year risks assigned by BOADICEA and IBIS ranged from 0·2 to 12·6% and 0·89 to 21·7%, respectively. The observed:expected breast cancer ratio was 15/18·6 (0·8–95% CI 0·48–1·33) and 15/28·6 (0·52–95% CI 0·32–0·87), using both models, respectively. In Jewish Israeli high-risk women the BOADICEA model has a better predictive value and accuracy in determining 10-year breast cancer risk than the IBIS model.
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Zain, Zuhaira Muhammad, Mona Alshenaifi, Abeer Aljaloud, Tamadhur Albednah, Reham Alghanim, Alanoud Alqifari, and Amal Alqahtani. "Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis." International Journal of Advances in Intelligent Informatics 6, no. 3 (November 6, 2020): 313. http://dx.doi.org/10.26555/ijain.v6i3.462.
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Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.
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Mathew, Dr Tina Elizabeth. "An Improvised Random Forest Model for Breast Cancer Classification." NeuroQuantology 20, no. 5 (May 18, 2022): 713–22. http://dx.doi.org/10.14704/nq.2022.20.5.nq22227.
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Breast Cancer is considered as the most common cancer in females with high incidence rate. The evolution of modern facilities has helped in reducing the mortality rate, yet the incidence is still the highest among all cancers affecting women. Early diagnosis is a predominant factor for survival. Hence techniques to assist the current modalities are essential. Machine learning techniques have been used so as to produce better prediction and classification models which will aid in better and earlier disease diagnosis and classification. Random Forest is a supervised machine learning classifier that helps in better classification. Random Forests are applied to the Wisconsin breast cancer dataset and the performance of the classifier is evaluated for breast cancer classification. Here in this study an improvised random forest model which uses a cost sensitive learning approach for classification is proposed and it is found to have a better performance than the traditional random forest approach. The model gave an accuracy of 97.51%.
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Adnan, Nahim, Tanzira Najnin, and Jianhua Ruan. "A Robust Personalized Classification Method for Breast Cancer Metastasis Prediction." Cancers 14, no. 21 (October 29, 2022): 5327. http://dx.doi.org/10.3390/cancers14215327.
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Accurate prediction of breast cancer metastasis in the early stages of cancer diagnosis is crucial to reduce cancer-related deaths. With the availability of gene expression datasets, many machine-learning models have been proposed to predict breast cancer metastasis using thousands of genes simultaneously. However, the prediction accuracy of the models using gene expression often suffers from the diverse molecular characteristics across different datasets. Additionally, breast cancer is known to have many subtypes, which hinders the performance of the models aimed at all subtypes. To overcome the heterogeneous nature of breast cancer, we propose a method to obtain personalized classifiers that are trained on subsets of patients selected using the similarities between training and testing patients. Results on multiple independent datasets showed that our proposed approach significantly improved prediction accuracy compared to the models trained on the complete training dataset and models trained on specific cancer subtypes. Our results also showed that personalized classifiers trained on positively and negatively correlated patients outperformed classifiers trained only on positively correlated patients, highlighting the importance of selecting proper patient subsets for constructing personalized classifiers. Additionally, our proposed approach obtained more robust features than the other models and identified different features for different patients, making it a promising tool for designing personalized medicine for cancer patients.
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He, Li, Yuelong Wang, Yongning Yang, Liqiu Huang, and Zhining Wen. "Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/424509.
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For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs) generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.
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Kadhim, Rania R., and Mohammed Y. Kamil. "Comparison of breast cancer classification models on Wisconsin dataset." International Journal of Reconfigurable and Embedded Systems (IJRES) 11, no. 2 (July 1, 2022): 166. http://dx.doi.org/10.11591/ijres.v11.i2.pp166-174.
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Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
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Fan, Run, Yufan Chen, Sarah Nechuta, Hui Cai, Kai Gu, Liang Shi, Pingping Bao, Yu Shyr, Xiao‐Ou Shu, and Fei Ye. "Prediction models for breast cancer prognosis among Asian women." Cancer 127, no. 11 (March 11, 2021): 1758–69. http://dx.doi.org/10.1002/cncr.33425.
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Miller, Eric A., Paul F. Pinsky, Brandy M. Heckman-Stoddard, and Lori M. Minasian. "Breast cancer risk prediction models and subsequent tumor characteristics." Breast Cancer 27, no. 4 (February 13, 2020): 662–69. http://dx.doi.org/10.1007/s12282-020-01060-9.
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Chlebowski, R. T., G. L. Anderson, D. S. Lane, A. Aragaki, T. Rohan, S. Yasmeen, G. Sato, C. A. Rosenberg, and F. A. Hubbell. "Predicting risk of estrogen receptor positive breast cancers in postmenopausal women." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 1507. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.1507.
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1507 Background: Chemoprevention strategies for estrogen receptor positive (ER+) breast cancers are emerging, especially for postmenopausal women, but require methods of targeting appropriate populations. Our objective was to improve the Breast Cancer Risk Assessment Tool [Gail Model (GM)] for estimating ER+ breast cancer risk. Methods: A prospective cohort involving 161,809 postmenopausal women aged 50–79 years, (93,676 in the observational study (OS) and 68,132 in clinical trials (CT)) at Women’s Health Initiative (WHI) Clinical Centers had comprehensive assessment of lifestyle, medication use and breast cancer risk factors. Breast cancer risk from the GM and other models incorporating additional or fewer risk factors and five year incidence of ER + and ER negative (ER-) invasive breast cancers were determined. Main outcome measures were concordance statistics for models predicting breast cancer risk. Results: Of 148,266 women meeting eligibility criteria, (no prior breast cancer and/or mastectomy), 3,236 developed breast cancer. Chronological age and age at menopause, both GM components, were significantly associated with only ER+ but not ER- breast cancer risk (p<0.05 for heterogeneity test). The GM predicted population-based ER+ cancer risk with reasonable accuracy (concordance statistic 0.60, 95% confidence interval (CI) 0.58 to 0.62) but for ER- cancers, the results were equivalent to chance allocation (concordance statistic 0.49, 95% CI 0.45 to 0.54). For ER+ cancers, no additional risk factors improved the GM prediction. However, a simpler model, developed in the OS and tested in the CT population, including only age, family history, and benign breast biopsy was comparable to GM in ER+ breast cancer prediction (concordance statistics 0.58, 95% CI 0.56 to 0.60). Using this model, all women ≥ 55 years old (or ≥ 60 year old if African American) with either a prior breast biopsy or first degree breast cancer family history had five year breast cancer risk of ≥ 1.8%. Conclusions: In postmenopausal women with comprehensive mammography use, the GM identifies populations at increased risk for ER+ breast cancer but not for ER- cancer. A model with fewer variables provides a simpler alternative for identifying populations appropriate for breast cancer chemoprevention interventions. No significant financial relationships to disclose.
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Zhang, Zirui, and Zixuan Li. "Evaluation Methods for Breast Cancer Prediction in Machine Learning Field." SHS Web of Conferences 144 (2022): 03010. http://dx.doi.org/10.1051/shsconf/202214403010.
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Breast cancer is the most common malignant tumor found in women, and there is no cure for advanced breast cancer. Early detection and treatment can effectively improve patient survival. This paper uses five machine learning classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbors Algorithm (KNN). The training data for the five models are provided by the Wisconsin Breast Cancer Dataset (WBCD). By evaluating and comparing the performance of the five models in accuracy, F1Score, ROC curve, and PR curve, the study finds that LR has the best performance.
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Mondal, Shudipti Rani, Aafreen ., and Rakesh Pal. "PREDICTION OF BREAST CANCER USING MACHINE LEARNING." International Journal of Innovative Research in Advanced Engineering 8, no. 3 (March 30, 2021): 28–33. http://dx.doi.org/10.26562/ijirae.2021.v0803.001.
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In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approaches to data mining using classification to create deep predictions that can be applied to Breast Cancer data. In addition, by testing datasets on different classifiers, this analysis predicts the best model that delivers high efficiency. In this paper, the UCI machine learning repository has 699 instances with 11 attributes collected from the Breast cancer dataset. First, the data set is pre-processed, visualized and fed to different classifiers such as Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. 10-fold cross validation is implemented and testing is carried out in order to create and validate new models. Effective analysis shows that Logistic Regression generates the deep predictions of all classifiers and obtains the best model delivering strong and precise outcomes, followed by other methods: Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. Most models were less reliable compared to the approach of logistic regression.
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Mahmoud, Mattia A., Anne Marie McCarthy, Despina Kontos, Emily Conant, Jinbo Chen, Sarah Ehsan, Lauren Pantalone, and Walter Mankowski. "Abstract P022: Quantitative measures of breast density and breast cancer risk prediction among black women in a screening population." Cancer Prevention Research 16, no. 1_Supplement (January 1, 2023): P022. http://dx.doi.org/10.1158/1940-6215.precprev22-p022.
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Abstract Background: Although mammographic density (MD) is a strong predictor of invasive breast cancer, it has been shown to increase the discriminatory ability of existing risk prediction models only slightly. Breast density is assessed visually by radiologists according to the Breast Imaging Reporting and Data System (BI-RADS) criteria, which has been shown to lack reproducibility. Additionally, the racial diversity of women included in the previous studies was limited. Quantitative measures of breast density have been developed that automatically measure density directly from images. Our prior work found that Black women had lower BI-RADS breast density, despite having a greater quantity of dense breast tissue on average compared with white women when quantitative measures were used. The study purpose was to determine if adding quantitative breast density measures improved breast cancer risk prediction for both white and Black women compared to the Breast Cancer Risk Assessment Tool (BCRAT). Methods: A total of 16,942 women (N=6881 white, N=10061 Black) screened with full-field digital mammography (FFDM) or with a combination of FFDM and digital breast tomosynthesis (DBT) at the Hospital of the University of Pennsylvania (HUP) between September 1, 2010 to December 31, 2014 were included. Area breast density measurements including dense area and area percent density were obtained using a fully automated, validated LIBRA software. All patients were followed from the date of first screening mammogram visit until breast cancer diagnosis or end of follow up on December 31, 2019. We used the BCRA R package (v2.1) for the BCRAT (https://dceg.cancer.gov/tools/risk-assessment/bcra) to estimate the expected 5 year absolute risk for breast cancer. We evaluated the area under the curve (AUC) and calibration (observed to expected ratio, O/E) of the following models: BCRAT alone, BCRAT + BI-RADS density, BCRAT + quantitative density measures, and BCRAT + BI-RADS density + quantitative density measures. Results: There were 123 breast cancers among white and 123 breast cancers among Black women. Adding dense area and area percent density to the BCRAT alone or BCRAT plus BI-RADS density did not improve predictive accuracy for white or Black women. AUC remained close to 0.59 for white women and 0.61 for Black women in all models, with no statistically significant differences in AUCs (DeLong Test p value = 0.09). Underprediction was worse in white women than in Black women. Under-prediction of the BCRAT was reduced when adding percent density from [O/E 1.24 vs. O/E 1.17] in white women. Calibration stayed relatively the same (O/E=1.10) for Black women even when adding both quantitative MD measures. Conclusion: Our results suggest that adding quantitative area mammographic density measurements to the BCRAT does not improves breast cancer risk prediction among Black or white women. Given the increasing use of digital breast tomosynthesis (DBT), future studies should examine whether volumetric breast density measures have superior predictive value among Black women. Citation Format: Mattia A. Mahmoud, Anne Marie McCarthy, Despina Kontos, Emily Conant, Jinbo Chen, Sarah Ehsan, Lauren Pantalone, Walter Mankowski. Quantitative measures of breast density and breast cancer risk prediction among black women in a screening population. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P022.
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Monirujjaman Khan, Mohammad, Somayea Islam, Srobani Sarkar, Foyazel Iben Ayaz, Morsaleen Kabeer Ananda, Tahia Tazin, Amani Abdulrahman Albraikan, and Faris A. Almalki. "Machine Learning Based Comparative Analysis for Breast Cancer Prediction." Journal of Healthcare Engineering 2022 (April 11, 2022): 1–15. http://dx.doi.org/10.1155/2022/4365855.
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One of the most prevalent and leading causes of cancer in women is breast cancer. It has now become a frequent health problem, and its prevalence has recently increased. The easiest approach to dealing with breast cancer findings is to recognize them early on. Early detection of breast cancer is facilitated by computer-aided detection and diagnosis (CAD) technologies, which can help people live longer lives. The major goal of this work is to take advantage of recent developments in CAD systems and related methodologies. In 2011, the United States reported that one out of every eight women was diagnosed with cancer. Breast cancer originates as a result of aberrant cell division in the breast, which leads to either benign or malignant cancer formation. As a result, early detection of breast cancer is critical, and with effective treatment, many lives can be saved. This research covers the findings and analyses of multiple machine learning models for identifying breast cancer. The Wisconsin Breast Cancer Diagnostic (WBCD) dataset was used to develop the method. Despite its small size, the dataset provides some interesting data. The information was analyzed and put to use in a number of machine learning models. For prediction, random forest, logistic regression, decision tree, and K-nearest neighbor were utilized. When the results are compared, the logistic regression model is found to offer the best results. Logistic regression achieves 98% accuracy, which is better than the previous method reported.
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Chen, Hongling, Mingyan Gao, Ying Zhang, Wenbin Liang, and Xianchun Zou. "Attention-Based Multi-NMF Deep Neural Network with Multimodality Data for Breast Cancer Prognosis Model." BioMed Research International 2019 (May 13, 2019): 1–11. http://dx.doi.org/10.1155/2019/9523719.
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Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients, which can not only effectively avoid overtreatment and medical resources waste, but also provide scientific basis to help medical staff and patients family members to make right medical decisions. As well known, cancer is a partly inherited disease with various important biological markers, especially the gene expression profile data and clinical data. Therefore, the accuracy of prediction model can be improved by integrating gene expression profile data and clinical data. In this paper, we proposed an end-to-end model, Attention-based Multi-NMF DNN (AMND), which combines clinical data and gene expression data extracted by Multiple Nonnegative Matrix Factorization algorithms (Multi-NMF) for the prognostic prediction of breast cancer. The innovation of this method is highlighted through using clinical data and combining multiple feature selection methods with the help of Attention mechanism. The results of comprehensive performance evaluation show that the proposed model reports better predictive performances than either models only using data of single modality, e.g., gene or clinical, or models based on any single NMF improved methods which only use one of the NMF algorithms to extract features. The performance of our model is competitive or even better than other previously reported models. Meanwhile, AMND can be extended to the survival prediction of other cancer diseases, providing a new strategy for breast cancer prognostic prediction.
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Li, Huayao, Chundi Gao, Jing Zhuang, Lijuan Liu, Jing Yang, Cun Liu, Chao Zhou, Fubin Feng, Ruijuan Liu, and Changgang Sun. "An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database." Cancer Biomarkers 30, no. 4 (April 9, 2021): 417–28. http://dx.doi.org/10.3233/cbm-201684.
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BACKGROUND: Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer. OBJECTIVE: To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformatics METHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses. RESULTS: We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer. CONCLUSIONS: The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.
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Krishnamurthi, Rajalakshmi, Niyati Aggrawal, Lokendra Sharma, Diva Srivastava, and Shivangi Sharma. "Importance of Feature Selection and Data Visualization Towards Prediction of Breast Cancer." Recent Patents on Computer Science 12, no. 4 (August 19, 2019): 317–28. http://dx.doi.org/10.2174/2213275912666190101121058.
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Background: Breast cancer is one of the most common forms of cancers among women and the leading cause of death among them. Countries like United States, England and Canada have reported a high number of breast cancer patients every year and this number is continuously increasing due to detection at later stages. Hence, it is very important to create awareness among women and develop such algorithms which help to detect malignant cancer. Several research studies have been conducted to analyze the breast cancer data. Objective: This paper presents an effective method in predicting breast cancer and its stage and will also analyze the performance of different supervised learning algorithms such as Random Classifier, Chi2 Square test used in order to predict. The paper focuses on the three important aspects such as the feature selection, the corresponding data visualisation and finally making a prediction call on different machine learning models. Methods: The dataset used for this work is breast cancer Wisconsin data taken from UCI library. The dataset has been used to show the different 32 features which are all important and how it can be achieved using data visualisation. Secondly, after the feature selection, different machine learning models have been applied. Conclusion: The machine learning models involved are namely Support Vector Machine (SVM), KNearest Neighbour (KNN), Random Forest, Principal Component Analysis (PCA), Neural Network using Perceptron (NNP). This has been done to check which type of model is better under what conditions. At different stages several charts have been plotted and eliminated based on relative comparison. Results have shown that Random Tree classifier along with Chi2 Square proves to be an efficient one.
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Silva Araújo, Vinícius, Augusto Guimarães, Paulo de Campos Souza, Thiago Silva Rezende, and Vanessa Souza Araújo. "Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer." Machine Learning and Knowledge Extraction 1, no. 1 (February 14, 2019): 466–82. http://dx.doi.org/10.3390/make1010028.
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Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals..
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Jupe, E. R., D. A. Ralph, C. E. Aston, S. Manjeshwar, T. D. Pugh, B. A. Gramling, D. C. Defreese, and C. D. Shimasaki. "Genetic models for estimating age-specific risk of sporadic breast cancer." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 10038. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.10038.
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10038 Introduction: Accurately assessing individual risk of developing breast cancer is essential for early detection and prevention. The possibility exists to improve upon the widely used Gail model in predicting an individual’s risk of developing breast cancer, by incorporating both genetic and lifestyle information. Methods: We have developed a model with data from ∼7,000 Caucasian women (2,300 cases and 4,700 controls) enrolled in four geographically distinct regions of the US. Questionnaire-based information on clinical and lifestyle variables, including the Gail model questions, were collected. All samples were genotyped for 120 common, functional polymorphisms in candidate genes likely to influence breast carcinogenesis. The data were randomly divided into training (75%) and validation (25%) sets. Because of age-dependent genetic penetrance observed in the study, separate models were built for three age intervals (30–44, 45–54 and ≥ 55). Following assessment of Hardy-Weinberg equilibrium and linkage-disequilibrium, univariate and multivariate statistical analyses were performed to assess genetic main effects and epistatic interactions and define model terms. Models were evaluated by Receiver Operator Curve (ROC) analysis. Results: The riskprediction models developed for all three age intervals were informative with Area Under the Curve (AUC) that was consistently greater than expected by chance and exceeded the predictive power of the Gail model alone. No strong associations were found between scores derived from our models and the Gail model. The results suggest that the genetic markers examined contribute to breast cancer prediction independent of the Gail scores, and due to the improvement in the AUC, probably complement the Gail scores. The strongest genetic contributors to risk were identified in the youngest group where the AUC was 0.71 in the training set and 0.66 in the validation set. Estimated AUC values appear to be quite stable across 1,000 bootstrap samples of training and validation sets. Conclusions: The predictive power of these models integrating both genetic and epidemiological data provides an improved estimate of an individual’s breast cancer risk. [Table: see text]
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Chen, Yong-Zi, Youngchul Kim, Hatem H. Soliman, GuoGuang Ying, and Jae K. Lee. "Single drug biomarker prediction for ER− breast cancer outcome from chemotherapy." Endocrine-Related Cancer 25, no. 6 (June 2018): 595–605. http://dx.doi.org/10.1530/erc-17-0495.
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ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER− patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER− breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER− breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER− patients in the Hatzis cohort (AUC = 0.637, P = 0.002) and 69 ER− patients in the Hess cohort (AUC = 0.635, P = 0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC = 0.60, 0.72, P = 0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER− and TN subgroups (log-rank test P-value = 0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER− breast cancer.
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Chuang, Li-Yeh, Guang-Yu Chen, Sin-Hua Moi, Fu Ou-Yang, Ming-Feng Hou, and Cheng-Hong Yang. "Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm." BioMed Research International 2019 (April 1, 2019): 1–12. http://dx.doi.org/10.1155/2019/2304128.
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Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.
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Alsayadi, Hamzah A., Abdelaziz A. Abdelhamid, El-Sayed M. El El-Kenawy, Abdelhameed Ibrahim, and Marwa M. Eid. "Ensemble of Machine Learning Fusion Models for Breast Cancer Detection Based on the Regression Model." Fusion: Practice and Applications 9, no. 2 (2022): 19–26. http://dx.doi.org/10.54216/fpa.090202.
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Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. The various machine learning (ML) algorithms applied include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 real-valued feature information. Among all five machine learning methods, K-Neighbors regressor had the best performance and ensemble using K-Neighbors regressor gave the best accuracy. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.
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Alfian, Ganjar, Muhammad Syafrudin, Imam Fahrurrozi, Norma Latif Fitriyani, Fransiskus Tatas Dwi Atmaji, Tri Widodo, Nurul Bahiyah, Filip Benes, and Jongtae Rhee. "Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method." Computers 11, no. 9 (September 12, 2022): 136. http://dx.doi.org/10.3390/computers11090136.
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Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.
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Xiao, Jialong, Miao Mo, Zezhou Wang, Changming Zhou, Jie Shen, Jing Yuan, Yulian He, and Ying Zheng. "The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study." JMIR Medical Informatics 10, no. 2 (February 18, 2022): e33440. http://dx.doi.org/10.2196/33440.
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Background Over the recent years, machine learning methods have been increasingly explored in cancer prognosis because of the appearance of improved machine learning algorithms. These algorithms can use censored data for modeling, such as support vector machines for survival analysis and random survival forest (RSF). However, it is still debated whether traditional (Cox proportional hazard regression) or machine learning-based prognostic models have better predictive performance. Objective This study aimed to compare the performance of breast cancer prognostic prediction models based on machine learning and Cox regression. Methods This retrospective cohort study included all patients diagnosed with breast cancer and subsequently hospitalized in Fudan University Shanghai Cancer Center between January 1, 2008, and December 31, 2016. After all exclusions, a total of 22,176 cases with 21 features were eligible for model development. The data set was randomly split into a training set (15,523 cases, 70%) and a test set (6653 cases, 30%) for developing 4 models and predicting the overall survival of patients diagnosed with breast cancer. The discriminative ability of models was evaluated by the concordance index (C-index), the time-dependent area under the curve, and D-index; the calibration ability of models was evaluated by the Brier score. Results The RSF model revealed the best discriminative performance among the 4 models with 3-year, 5-year, and 10-year time-dependent area under the curve of 0.857, 0.838, and 0.781, a D-index of 7.643 (95% CI 6.542, 8.930) and a C-index of 0.827 (95% CI 0.809, 0.845). The statistical difference of the C-index was tested, and the RSF model significantly outperformed the Cox-EN (elastic net) model (C-index 0.816, 95% CI 0.796, 0.836; P=.01), the Cox model (C-index 0.814, 95% CI 0.794, 0.835; P=.003), and the support vector machine model (C-index 0.812, 95% CI 0.793, 0.832; P<.001). The 4 models’ 3-year, 5-year, and 10-year Brier scores were very close, ranging from 0.027 to 0.094 and less than 0.1, which meant all models had good calibration. In the context of feature importance, elastic net and RSF both indicated that TNM staging, neoadjuvant therapy, number of lymph node metastases, age, and tumor diameter were the top 5 important features for predicting the prognosis of breast cancer. A final online tool was developed to predict the overall survival of patients with breast cancer. Conclusions The RSF model slightly outperformed the other models on discriminative ability, revealing the potential of the RSF method as an effective approach to building prognostic prediction models in the context of survival analysis.
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Wu, Shuai, and Wenjia Xiong. "Comparison of Different Machine Learning Models in Breast Cancer." Highlights in Science, Engineering and Technology 8 (August 17, 2022): 624–29. http://dx.doi.org/10.54097/hset.v8i.1238.
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Breast Cancer is mainly found in women and is the main cause of increased mortality among women. Breast cancer diagnosis is time-consuming, and due to the low availability of the system, it is necessary to develop a system that can automatically diagnose breast cancer at an early stage. Various machine learning and Deep Learning Algorithms have been used to classify benign and malignant tumors. This paper focuses on the implementation of various models, such as Logistic regression, random forest and naive Bayes. Each algorithm has measured and compared the accuracy and obtained accuracy. This paper aims to compare the advantages and disadvantages of different regression models in breast cancer prediction. The method proposed in this paper can promote the integration of machine learning and medicine, and improve clinical diagnostic accuracy.
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Pyatchanina, T. V., and A. N. Ohorodnyk. "Risk models for breast cancer." Proceedings of the National Academy of Sciences of Belarus, Medical series 15, no. 4 (January 14, 2019): 503–10. http://dx.doi.org/10.29235/1814-6023-2018-15-4-503-510.
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Scientific evidence indicates the stabilization of indicators of morbidity and mortality from breast cancer in women in Ukraine and the existence of a number of models for predicting the breast cancer risk with the consideration of life style factors, detectable mutations of BRCA1 and BRCA2 genes, family history, as well as predicative and prognostic factors (clinical, molecular-biological) to determine the possible ways of the tumor process and the survival of breast cancer patients.
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Lou, Shi-Jer, Ming-Feng Hou, Hong-Tai Chang, Hao-Hsien Lee, Chong-Chi Chiu, Shu-Chuan Jennifer Yeh, and Hon-Yi Shi. "Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study." Biology 11, no. 1 (December 29, 2021): 47. http://dx.doi.org/10.3390/biology11010047.
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Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174–174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.
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徐, 浦. "Construction of Bioactivity Prediction Models for Breast Cancer Candidate Drugs." Advances in Applied Mathematics 10, no. 12 (2021): 4454–68. http://dx.doi.org/10.12677/aam.2021.1012474.
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Gupta, Puja, and Shruti Garg. "Breast Cancer Prediction using varying Parameters of Machine Learning Models." Procedia Computer Science 171 (2020): 593–601. http://dx.doi.org/10.1016/j.procs.2020.04.064.
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Thongkam, Jaree, Guandong Xu, Yanchun Zhang, and Fuchun Huang. "Toward breast cancer survivability prediction models through improving training space." Expert Systems with Applications 36, no. 10 (December 2009): 12200–12209. http://dx.doi.org/10.1016/j.eswa.2009.04.067.
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López, Nahúm Cueto, María Teresa García-Ordás, Facundo Vitelli-Storelli, Pablo Fernández-Navarro, Camilo Palazuelos, and Rocío Alaiz-Rodríguez. "Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction." International Journal of Environmental Research and Public Health 18, no. 20 (October 12, 2021): 10670. http://dx.doi.org/10.3390/ijerph182010670.
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This study evaluates several feature ranking techniques together with some classifiers based on machine learning to identify relevant factors regarding the probability of contracting breast cancer and improve the performance of risk prediction models for breast cancer in a healthy population. The dataset with 919 cases and 946 controls comes from the MCC-Spain study and includes only environmental and genetic features. Breast cancer is a major public health problem. Our aim is to analyze which factors in the cancer risk prediction model are the most important for breast cancer prediction. Likewise, quantifying the stability of feature selection methods becomes essential before trying to gain insight into the data. This paper assesses several feature selection algorithms in terms of performance for a set of predictive models. Furthermore, their robustness is quantified to analyze both the similarity between the feature selection rankings and their own stability. The ranking provided by the SVM-RFE approach leads to the best performance in terms of the area under the ROC curve (AUC) metric. Top-47 ranked features obtained with this approach fed to the Logistic Regression classifier achieve an AUC = 0.616. This means an improvement of 5.8% in comparison with the full feature set. Furthermore, the SVM-RFE ranking technique turned out to be highly stable (as well as Random Forest), whereas relief and the wrapper approaches are quite unstable. This study demonstrates that the stability and performance of the model should be studied together as Random Forest and SVM-RFE turned out to be the most stable algorithms, but in terms of model performance SVM-RFE outperforms Random Forest.
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Massafra, Raffaella, Maria Colomba Comes, Samantha Bove, Vittorio Didonna, Sergio Diotaiuti, Francesco Giotta, Agnese Latorre, et al. "A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification." PLOS ONE 17, no. 9 (September 19, 2022): e0274691. http://dx.doi.org/10.1371/journal.pone.0274691.
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Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.
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Michel, Alissa, Vicky Ro, Julia E. McGuinness, Simukayi Mutasa, Richard Ha, and Katherine D. Crew. "Abstract P2-10-03: Improving breast cancer risk prediction using a convolutional neural network-based mammographic evaluation in combination with clinical risk factors." Cancer Research 82, no. 4_Supplement (February 15, 2022): P2–10–03—P2–10–03. http://dx.doi.org/10.1158/1538-7445.sabcs21-p2-10-03.
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Abstract Background: Accurate assessment of a woman’s individualized breast cancer risk is necessary to inform shared decision-making regarding screening and risk-reducing strategies. Recently, deep learning techniques, including convolutional neural networks (CNN), have shown better predictive potential for breast cancer risk compared to mammographic density (MD). We evaluated whether combining clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model, including MD, with a novel CNN-based mammographic evaluation more accurately predicts breast cancer risk than the BCSC model alone in a cohort of racially/ethnically diverse women. Methods: We conducted a retrospective cohort study of 23,552 women, age 35-74 years, who underwent screening mammography from 2014 to 2018 at Columbia University Irving Medical Center in New York City. We extracted data from the electronic health record (EHR) on breast cancer risk factors (age, race/ethnicity, prior benign breast biopsy, first degree family history of breast cancer, and MD). From this cohort, we identified 206 women who developed breast cancer by linkage to the tumor registry. We calculated 5-year invasive breast cancer risk using the BCSC model. We applied CNN-based breast cancer risk model to full-field craniocaudal mammographic views of both breasts, with an output of a risk score (range, 0-1). We used logistic regression models with breast cancer status as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared the prediction performance of these models via area under the receiver operating characteristics curves (AUCs) based on the DeLong test. We also calculated each model’s AUC for subgroups of age and race/ethnicity. Results: Among 23,552 evaluable women, mean age was 55.9 years (standard deviation [SD], 9.5) with 27% non-Hispanic White, 9% non-Hispanic Black, 36% Hispanic, 5% Asian, and 23% Other/Unknown race/ethnicity. Four percent had a first-degree family history of breast cancer, 10% had a prior benign breast biopsy, 45% had heterogeneously or extremely dense breasts on mammography, and 22% met high-risk criteria based upon a 5-year invasive breast cancer risk 1.67% according to the BCSC model. Mean CNN risk score was higher among breast cancer cases compared to unaffected controls (0.477 vs. 0.466, p=0.077). We found that the hybrid model outperformed the BCSC model (AUC of 0.676 vs. 0.640, respectively; p=0.003). In subgroup analyses, the hybrid model more accurately predicted breast cancer risk compared to the BCSC model among women age<50 (AUC of 0.713 vs. 0.645, respectively; p=0.078) and age>=50 (AUC of 0.663 vs. 0.625, respectively; p=0.026); non-Hispanic Black women (AUC of 0.794 vs. 0.663, respectively; p=0.028) and Hispanic women (AUC of 0.666 vs. 0.621, respectively; p=0.060). Conclusion: Among women undergoing screening mammography, a hybrid model incorporating a CNN-based mammography evaluation with clinical factors from the BCSC model more accurately predicted breast cancer risk relative to the BCSC model alone, particularly among racial and ethnic minorities. Combined with clinical risk factors, our CNN model may be used to efficiently predict breast cancer risk and inform risk-stratified breast cancer screening and prevention strategies. Comparing prediction performance for breast cancer risk of the BCSC model vs. hybrid modelBCSC ModelHybrid ModelP-value*AUC95% CIAUC95% CIAll patients0.6400.602-0.6830.6760.640-0.7110.003Age (years)<500.6450.562-0.7280.7130.648-0.7780.078>=500.6250.579-0.6710.6630.623-0.7030.026Race/ethnicityNon-Hispanic White0.6880.623-0.7540.7040.643-0.7660.230Non-Hispanic Black0.6630.539-0.7880.7940.704-0.8830.028Hispanic0.6210.560-0.6860.6660.597-0.7330.060Asian0.6160.440-0.7910.6510.461-0.8410.694 Citation Format: Alissa Michel, Vicky Ro, Julia E McGuinness, Simukayi Mutasa, Richard Ha, Katherine D Crew. Improving breast cancer risk prediction using a convolutional neural network-based mammographic evaluation in combination with clinical risk factors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-10-03.