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Academic literature on the topic 'Breast cancer prediction models'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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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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Dissertations / Theses on the topic "Breast cancer prediction models"

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Ripley, Ruth Mary. "Neural network models for breast cancer prognosis." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244721.

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Louro, Aldamiz-Echevarría Javier. "Individualized breast cancer risk prediction models applied to population-based screening mammography." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673964.

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Introducció: S'ha demostrat que el cribratge mamogràfic redueix la mortalitat per càncer de mama. Seguint les recomanacions de la Comissió Europea, els països europeus han establert programes poblacionals de cribratge que ofereixen mamografies biennals a dones d'entre 50 i 69 anys d'edat. No obstant això, el cribratge de càncer de mama no està lliure de controvèrsia ja que existeix un debat en relació a l'equilibri entre la reducció de la mortalitat i els efectes adversos. Per a millorar aquest equilibri, l'evidència científica actual dóna suport al cribratge personalitzat. Els estudis de modelització han demostrat que modificar l'interval de cribratge, la prova de cribratge o el rang d'edat de la població objectiu en funció del risc individual de les dones produeix un major benefici que les estratègies convencionals. Per tant, és necessari ampliar la informació actual sobre els factors de risc d'aquesta malaltia i crear models de predicció del risc individual mitjançant l'anàlisi de grans bases de dades poblacionals. Objectiu: L'objectiu general d'aquesta tesi és aprofundir en l'anàlisi del cribratge poblacional del càncer de mama. En concret, aquesta tesi pretén avaluar diferents factors de risc de càncer de mama per a desenvolupar i validar un model de predicció de risc individual d'aquesta malaltia. Es va analitzar com la densitat mamària afecta als diferents indicadors del cribratge en el context de la mamografia digital. A continuació, es van avaluar les diferències en el risc de càncer de mama en funció de si una lesió benigna de mama es va diagnosticar en un cribratge prevalent o un cribratge incident. També es va analitzar la interacció entre la densitat mamària i les lesions benignes en el risc de desenvolupar càncer de mama. Posteriorment, es va realitzar una revisió sistemàtica per a actualitzar l'evidència existent, dur a terme una valoració crítica i una avaluació del risc de biaix i resumir els resultats dels models de risc individualitzats que s'utilitzen per a estimar el risc de càncer de mama en les dones de la població general. Finalment, es va dissenyar un model de predicció individual del risc de càncer de mama i es va validar internament, a partir d'informació fàcilment accessible en un episodi de cribratge. Conclusions: i) Els diferents indicadors de cribratge es veuen afectats negativament per la densitat mamària, disminuint la sensibilitat i el valor predictiu positiu de la prova a mesura que augmenta la densitat mamària. ii) El risc de càncer de mama conferit per una lesió benigna difereix segons la mena de cribratge (prevalent o incident). Fins on sabem, aquest és el primer estudi que analitza l'impacte del tipus de cribratge en el pronòstic de la lesió benigna. iii) El risc de càncer de mama augmenta de manera independent amb la presència d'una lesió benigna i amb una major densitat mamària i es manté elevat durant més de 15 anys. iv) Els models de predicció són eines prometedores per a implementar polítiques de cribratge basades en el risc individualitzat. No obstant això, és un repte recomanar qualsevol d'ells per a la personalització del cribratge ja que necessiten millorar la seva qualitat i capacitat discriminatòria. v) Es va dissenyar i validar internament un model de predicció de risc capaç d'estimar el risc de càncer de mama a curt i llarg termini utilitzant la informació recollida de manera rutinària en el cribratge mamogràfic. El model inclou edat, antecedents familiars de càncer de mama, antecedents de lesió benigna i patrons mamogràfics previs, que van resultar estar relacionats amb un augment del risc de càncer de mama. El model ha de ser validat externament i actualitzat amb noves variables.
Introducción: Se ha demostrado que el cribado mamográfico reduce la mortalidad por cáncer de mama. Siguiendo las recomendaciones de la Comisión Europea, los países europeos han establecido programas poblacionales de cribado que ofrecen mamografías bienales a mujeres de entre 50 y 69 años de edad. Sin embargo, el cribado de cáncer de mama no está libre de controversia ya que existe un debate en cuanto al equilibrio entre la reducción de la mortalidad y los efectos adversos. Para mejorar este equilibrio, la evidencia científica actual apoya el cribado personalizado. Los estudios de modelización han demostrado que modificar el intervalo de cribado, la prueba de cribado o el rango de edad de la población objetivo en función del riesgo individual de las mujeres produce un mayor beneficio que las estrategias convencionales. Por lo tanto, es necesario ampliar la información actual sobre los factores de riesgo de esta enfermedad y crear modelos de predicción del riesgo individual mediante el análisis de grandes bases de datos poblacionales. Objetivo: El objetivo general de esta tesis es profundizar en el análisis del cribado poblacional del cáncer de mama. En concreto, esta tesis pretende evaluar diferentes factores de riesgo de cáncer de mama para desarrollar y validar un modelo de predicción de riesgo individual de esta enfermedad. Se analizó cómo la densidad mamaria afecta a los distintos indicadores de cribado en el contexto de la mamografía digital. A continuación, se evaluaron las diferencias en el riesgo de cáncer de mama en función de si una lesión benigna de mama se diagnosticó en un cribado prevalente o un cribado incidente. También se analizó la interacción entre la densidad mamaria y las lesiones benignas en el riesgo de cáncer de mama. Posteriormente, se realizó una revisión sistemática para actualizar la evidencia existente, llevar a cabo una valoración crítica y una evaluación del riesgo de sesgo y resumir los resultados de los modelos de riesgo individualizados que se utilizan para estimar el riesgo de cáncer de mama en las mujeres de la población general. Por último, se diseñó un modelo de predicción individual del riesgo de cáncer de mama y se validó internamente, basado en información fácilmente accesible en un episodio de cribado. Conclusiones: i) Los distintos indicadores de cribado se ven afectados negativamente por la densidad mamaria, disminuyendo la sensibilidad y el valor predictivo positivo de la prueba a medida que aumenta la densidad mamaria. ii) El riesgo de cáncer de mama conferido por una lesión benigna difiere según el tipo de cribado (prevalente o incidente). Hasta donde sabemos, este es el primer estudio que analiza el impacto del tipo de cribado en el pronóstico de la lesión benigna. iii) El riesgo de cáncer de mama aumenta de forma independiente con la presencia de una lesión benigna y con una mayor densidad mamaria y se mantiene elevado durante más de 15 años. iv) Los modelos de predicción son herramientas prometedoras para implementar políticas de cribado basadas en el riesgo individualizado. Sin embargo, es un reto recomendar cualquiera de ellos para la personalización del cribado ya que necesitan mejorar su calidad y capacidad discriminatoria. v) Diseñamos y validamos internamente un modelo de predicción de riesgo capaz de estimar el riesgo de cáncer de mama a corto y largo plazo utilizando la información recogida de forma rutinaria en el cribado mamográfico. El modelo incluye edad, antecedentes familiares de cáncer de mama, antecedentes de lesión benigna y patrones mamográficos previos, que resultaron estar relacionados con un aumento del riesgo de cáncer de mama. El modelo debe ser validado externamente y actualizado con nuevas variables.
Background: Mammographic screening has been shown to reduce mortality from breast cancer. Following the recommendations of the European Council, European countries have started population-based screening programs that offer biennial mammograms to women aged between 50 and 69 years. The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. To improve this balance, current evidence supports personalized screening. Modeling studies have shown that modifying the screening interval, screening modality, or age range of the target population based on women's individual risk yields a greater benefit than conventional standard strategies. Several risk models have been designed to estimate women's individual breast cancer risk based on their personal characteristics. However, most of these models have not been specifically developed to estimate the risk of women targeted for breast cancer screening. There is therefore a need to broaden current information on risk factors for breast cancer and the estimation of individual risk prediction models through the analysis of large population-based databases. Aims: The general objective of the thesis is to deepen the analysis of population-based breast cancer screening. Specifically, the aim of this thesis is to assess different breast cancer risk factors in order to develop and validate an individualized breast cancer risk prediction model. We evaluated how breast density affects screening performance indicators in a digital mammography context. Then, we assessed differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens. To our knowledge, this is the first time that such an approach has been used. We also evaluated the interaction between breast density and benign breast disease. Subsequently, we performed a systematic review to update the existing evidence, conduct a critical appraisal and risk of bias assessment and summarize the results of the individualized risk models that are used to estimate the risk of breast cancer in women in the general population. Finally, a breast cancer risk prediction model was designed and internally validated, based on information easily accessible at screening. Conclusions: i) Performance screening measures are negatively affected by breast density, with sensitivity and positive predictive value decreasing as breast density increases. ii) The risk of breast cancer conferred by benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyze the impact of screening type on the prognosis of benign breast disease. iii) The risk of breast cancer independently increased with the presence of benign breast disease and with greater breast density and remained elevated for over 15 years. iv) Individualized risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity. v) We designed and internally validated a risk prediction model able to estimate short- and long-term breast cancer risk using information routinely reported at screening participation. The model included age, family history of breast cancer, benign breast disease and previous mammographic findings, which were found to be related to an increase in breast cancer risk. The model should be externally validated and updated with new variables.
Universitat Autònoma de Barcelona. Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública
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Fernandes, Ana Sofia Fachada. "Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/5087.

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Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
There are several clinical prognostic models in the medical field. Prior to clinical use, the outcome models of longitudinal cohort data need to undergo a multi-centre evaluation of their predictive accuracy. This thesis evaluates the possible gain in predictive accuracy in multicentre evaluation of a flexible model with Bayesian regularisation, the (PLANN-ARD), using a reference data set for breast cancer, which comprises 4016 records from patients diagnosed during 1989-93 and reported by the BCCA, Canada, with follow-up of 10 years. The method is compared with the widely used Cox regression model. Both methods were fitted to routinely acquired data from 743 patients diagnosed during 1990-94 at the Christie Hospital, UK, with follow-up of 5 years following surgery. Methodological advances developed to support the external validation of this neural network with clinical data include: imputation of missing data in both the training and validation data sets; and a prognostic index for stratification of patients into risk groups that can be extended to non-linear models. Predictive accuracy was measured empirically with a standard discrimination index, Ctd, and with a calibration measure, using the Hosmer-Lemeshow test statistic. Both Cox regression and the PLANN-ARD model are found to have similar discrimination but the neural network showed marginally better predictive accuracy over the 5-year followup period. In addition, the regularised neural network has the substantial advantage of being suited for making predictions of hazard rates and survival for individual patients. Four different approaches to stratify patients into risk groups are also proposed, each with a different foundation. While it was found that the four methodologies broadly agree, there are important differences between them. Rules sets were extracted and compared for the two stratification methods, the log-rank bootstrap and by direct application of regression trees, and with two rule extraction methodologies, OSRE and CART, respectively. In addition, widely used clinical breast cancer prognostic indexes such as the NPI, TNM and St. Gallen consensus rules, were compared with the proposed prognostic models expressed as regression trees, concluding that the suggested approaches may enhance current practice. Finally, a Web clinical decision support system is proposed for clinical oncologists and for breast cancer patients making prognostic assessments, which is tailored to the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the NPI, Cox regression modelling and PLANN-ARD. For a given patient, all three models yield a generally consistent but not identical set of prognostic indices that can be analysed together in order to obtain a consensus and so achieve a more robust prognostic assessment of the expected patient outcome.
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Renga, Sandra. "An evaluation of two predictive models of adjustment in women with breast cancer : hope versus cognitive adaptation theory." Thesis, Lancaster University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442721.

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Stephen, Jacqueline. "Statistical modelling of biomarkers incorporating non-proportional effects for survival data : with illustration by application to two residual risk models for predicting risk in early breast cancer." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/23390.

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Personalised medicine is replacing the one-drug-fits-all approach with many prognostic models incorporating biomarkers available for risk stratifying patients. Evidence has been emerging that the effects of biomarkers change over time and therefore violate the assumption of proportional hazards when performing Cox regression. Analysis using the Cox model when the assumptions are invalid can result in misleading conclusions. This thesis reviews existing approaches for the analysis of non-proportional effects with respect to survival data. A number of well-developed approaches were identified but to date their uptake in practice has been limited. There is a need for more widespread use of flexible modelling to move away from standard analysis using a Cox model when the assumption of proportional hazards is violated. Two novel approaches were applied to investigate the impact of follow-up duration on two residual risk models, IHC4 and Mammostrat, for predicting risk in early breast cancers using two studies with different lengths of follow up; the Edinburgh Breast Conservation Series (BCS) and the Tamoxifen versus Exemestane Adjuvant Multinational (TEAM) trial. Similar results were observed between the two approaches that were considered, the multivariable fractional polynomial time (MFPT) approach and Royston-Parmer flexible parametric models, with their respective advantages and disadvantages being discussed. The analyses identified a strong time-varying effect of IHC4 score with the prognostic effect of IHC4 score on time-to distant recurrence decreasing with increasing follow-up time. Mammostrat score identified a group of patients with an increased risk of distant recurrence over full follow-up in the TEAM and Edinburgh BCS cohorts. The results suggest a combined IHC4 and Mammostrat risk score could provide information on the risk of recurrence and warrants further study.
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Takada, Masahiro. "Prediction of axillary lymph node metastasis and the pathological response to neoadjuvant chemotherapy in patients with primary breast cancer using a decision tree-based model." Kyoto University, 2012. http://hdl.handle.net/2433/160969.

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Pawluczyk, Olga. "Volumetric estimation of breast density for breast cancer risk prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58694.pdf.

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Dartois, Laureen. "Facteurs comportementaux et non-comportementaux associés au risque de cancer et de mortalité à partir des données de la cohorte de femmes françaises E3N." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA11T081/document.

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Contexte : Le cancer est la seconde cause de mortalité chez la femme en France, et la première chez les femmes âgées de 35 à 84 ans. Le cancer du sein est le cancer le plus fréquemment diagnostiqué, représentant 35 % des cas chez les femmes en France en 2012. De multiples facteurs, comportementaux et non-Comportementaux, augmentant le risque de cancer, tant en incidence qu’en mortalité, ont été identifiés dans la littérature, tandis que leur influence conjointe est très peu évaluée. Dans le cas du cancer du sein, certains facteurs diffèrent selon le statut ménopausique des femmes, suggérant une étiologie différente entre les cancers du sein diagnostiqués avant et après la ménopause. Objectif : Les données de la cohorte prospective française E3N ont été utilisées pour évaluer l’influence des facteurs comportementaux et non-Comportementaux sur le risque de cancer et de mortalité chez les femmes avant et après la ménopause. Nous avons également cherché à estimer leur impact relatif sur la population et à identifier les facteurs à forts pouvoirs prédictifs.Résultats : Nos résultats suggèrent que le mode de vie a une influence modeste sur le risque de cancer et de mortalité lors de l’adhésion à une seule recommandation de santé publique. En revanche, elle est conséquente lors d’une adhésion conjointe à plusieurs recommandations. Les facteurs comportementaux jouent ainsi un rôle non négligeable dans la survenue de cancer et sur le risque de décès prématuré. Dans le cas du cancer du sein, ces facteurs influencent particulièrement le risque après la ménopause, tandis qu’avant la ménopause leur impact est plus faible que les facteurs qui ne relèvent pas du mode de vie ou de choix personnels. Ces observations sont retrouvées lorsque l’on cherche à prédire le risque de cancer du sein avant et après la ménopause. En effet, la prédiction du risque de cancer du sein en préménopause s’établit principalement à partir de facteurs non-Comportementaux, alors que la prédiction du risque en postménopause est également déterminée par des facteurs comportementaux.Conclusion : Nous avons montré que l’étiologie du cancer du sein diffère selon la nature de la tumeur, et en particulier selon le statut ménopausique des femmes. À tout âge, le mode de vie a une influence sur le risque de cancer et de mortalité prématurée, particulièrement après la ménopause lorsque leur impact est supérieur à celui des facteurs non-Comportementaux. Ces résultats demandent, cependant, à être reproduits dans des études prospectives portant sur des femmes plus jeunes
Background: Cancer is the second leading cause of mortality among women in France, and the leading cause of mortality among women aged between 35 and 84. Breast cancer is the most frequently diagnosed cancer, with 35% of cases among women in France in 2012. Multiple behavioural and non-Behavioural factors have been associated with increases in cancer incidence and mortality. However, the literature about their combined impact is scarce. Regarding breast cancer, some risk factors differed according to the menopausal status, suggesting a different etiology between premenopausal and postmenopausal breast cancers.Objectives: Data from the E3N prospective cohort of French women were used to evaluate the influence of behavioural and non-Behavioural factors on cancer risk before and after the menopause and on mortality. In addition, we aimed at estimating their relative impact on the population and identifying factors with the highest predictive power.Results: Our results suggest a modest influence of the lifestyle on cancer risk and mortality when adhering to only one public health recommendation. However, the influence is substantial with a combined adherence to several recommendations. Behavioural factors play a key role in the occurrence of cancer and mortality risk. Regarding breast cancer, these factors influence particularly the risk after the menopause, while before, their impact is lower than non-Behavioural factors. These observations were retrieved when aiming at predicting breast cancer risk according to menopausal status. Prediction was established by non-Behavioural factors in premenopause, while the prediction in postmenopause was driven by behavioural factors.Conclusion: We have shown that the etiology of breast cancer differs according to the nature of the tumour, and particularly according to the menopausal status of women. Whatever the age, lifestyle influence the risk of cancer and mortality, especially after the menopause when their impact is higher than the non-Behavioural factors’ one. New results from prospective study on younger women are warranted to confirm the results
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Chen, Hsiu-Hsi. "Mathematical models for progression of breast cancer and evaluation of breast cancer screening." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388263.

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Cariati, Massimiliano. "Breast cancer stem cells and xenograft models." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612710.

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Books on the topic "Breast cancer prediction models"

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Pawluczyk, Olga. Volumetric estimation of breast density for breast cancer risk prediction. Ottawa: National Library of Canada, 2001.

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Ueno, Naoto T. Inflammatory Breast Cancer: An Update. 2nd ed. Dordrecht: Springer Netherlands, 2012.

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A more difficult exercise. London: Bloomsbury, 1989.

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O'Connell, Fiona Claire. Morphology and gene expression in the postnatal mouse mammary gland. Dublin: University College Dublin, 1997.

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Focus on Breast Cancer Research. Nova Science Publishers, 2004.

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James, Paul, and Alison H. Trainer. Constitutional Genomic Variation in Disease Prediction and Prevention: Breast Cancer. Academic Press, 2022.

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James, Paul, and Alison H. Trainer. Constitutional Genomic Variation in Disease Prediction and Prevention: Breast Cancer. Elsevier Science & Technology Books, 2022.

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Ahlgren, Johan. Studies on Prediction of Axillary Lymph Node Status in Invasive Breast Cancer. Uppsala Universitet, 2002.

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Ng, Raymond Hin Wai. Plasminogen activators in cancer (experimental tumor models and human breast). 1986.

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Cheung, Alison Min Yan. Characterization of the biological functions of breast cancer gene BRCA2 using conditionally-inactivated mouse models. 2003.

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Book chapters on the topic "Breast cancer prediction models"

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Nanda, Aparajita, Manju, and Sarishty Gupta. "Breast Cancer Prediction Models: A Comparative Study and Analysis." In Advances in Intelligent Systems and Computing, 415–22. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4538-9_41.

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Al-Quraishi, Tahsien, Jemal H. Abawajy, Morshed U. Chowdhury, Sutharshan Rajasegarar, and Ahmad Shaker Abdalrada. "Breast Cancer Recurrence Prediction Using Random Forest Model." In Advances in Intelligent Systems and Computing, 318–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72550-5_31.

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Dawngliani, M. S., N. Chandrasekaran, R. Lalmawipuii, and H. Thangkhanhau. "Breast Cancer Recurrence Prediction Model Using Voting Technique." In International Conference on Mobile Computing and Sustainable Informatics, 17–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49795-8_2.

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Taghipour, Sharareh, Dragan Banjevic, Anthony Miller, and Bart Harvey. "Competing Risks Models and Breast Cancer: A Brief Review." In Risk Assessment and Evaluation of Predictions, 301–13. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8981-8_14.

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AL Binali, Shiekhah, Souham Meshoul, and Hadil Shaiba. "Breast Cancer Subtypes Prediction Using Omics Data and Machine Learning Models." In Artificial Intelligence and Sustainable Computing, 591–602. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1653-3_45.

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Jena, Lambodar, Lara Ammoun, and Bichitrananda Patra. "Machine Learning Model for Breast Cancer Tumor Risk Prediction." In Smart Innovation, Systems and Technologies, 517–31. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_47.

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Di, Junwen, and Zhiguo Shi. "Prediction Model of Breast Cancer Based on mRMR Feature Selection." In Communications in Computer and Information Science, 32–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63820-7_4.

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Sinha, Nishita, Puneet Sharma, and Deepak Arora. "Prediction Model for Breast Cancer Detection Using Machine Learning Algorithms." In Computational Methods and Data Engineering, 431–40. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6876-3_33.

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Iversen, Edwin, Giovanni Parmigiani, and Donald Berry. "Validating Bayesian Prediction Models: a Case Study in Genetic Susceptibility to Breast Cancer." In Case Studies in Bayesian Statistics, 321–38. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1502-8_8.

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Mohanty, Hemangini, and Santilata Champati. "Comparative Study of Eight Classification Models for Diagnosis and Prediction of Breast Cancer." In Advances in Mathematical Modelling, Applied Analysis and Computation, 193–205. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0179-9_11.

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Conference papers on the topic "Breast cancer prediction models"

1

Nafa, Fatema, Enoc Gonzalez, and Gurpreet Kaur. "An Approach using Machine Learning Model for Breast Cancer Prediction." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121815.

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Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an efficient process for prediction. The best overall accuracy for breast cancer detection is achieved equal to 94%. using Gaussian Naive Bayes.
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Priya, N., and G. Shobana. "Potential Breast Cancer Drug Prediction using Machine Learning Models." In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE, 2020. http://dx.doi.org/10.1109/ic-etite47903.2020.288.

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Ali, Amna, Ali Tufail, Umer Khan, and Minkoo Kim. "A survey of prediction models for breast cancer survivability." In the 2nd International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1655925.1656155.

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Naveen, R. K. Sharma, and Anil Ramachandran Nair. "Efficient Breast Cancer Prediction Using Ensemble Machine Learning Models." In 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT). IEEE, 2019. http://dx.doi.org/10.1109/rteict46194.2019.9016968.

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Chen, Yifan, Erry Gunawan, Yongmin Kim, Kay Soon Low, Cheong Boon Soh, and Lin Lin Thi. "UWB Microwave Breast Cancer Detection: Generalized Models and Performance Prediction." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260757.

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Chen, Yifan, Erry Gunawan, Yongmin Kim, Kay Soon Low, Cheong Boon Soh, and Lin Lin Thi. "UWB Microwave Breast Cancer Detection: Generalized Models and Performance Prediction." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4397986.

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Ghani, Muhammad Usman, Talha Mahboob Alam, and Fawwad Hassan Jaskani. "Comparison of Classification Models for Early Prediction of Breast Cancer." In 2019 International Conference on Innovative Computing (ICIC). IEEE, 2019. http://dx.doi.org/10.1109/icic48496.2019.8966691.

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Chun, J., F. Schnabel, and O. Ogunyemi. "Assessment of breast cancer risk prediction models in a high-risk population." In CTRC-AACR San Antonio Breast Cancer Symposium: 2008 Abstracts. American Association for Cancer Research, 2009. http://dx.doi.org/10.1158/0008-5472.sabcs-4074.

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El Rahman, Sahar A., Amjad Al-montasheri, Batool Al-hazmi, Haya Al-dkaan, and Maram Al-shehri. "Machine Learning Model for Breast Cancer Prediction." In 2019 International Conference on Fourth Industrial Revolution (ICFIR). IEEE, 2019. http://dx.doi.org/10.1109/icfir.2019.8894777.

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Hong, Gongyin. "Prediction of breast cancer using regression model." In International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), edited by Ahmed El-Hashash. SPIE, 2022. http://dx.doi.org/10.1117/12.2660386.

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Reports on the topic "Breast cancer prediction models"

1

Hartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada442889.

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Hartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada427975.

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Hartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada418667.

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Hartmann, Lynn C. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada552165.

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Griep, Anne E. Transgenic Rat Models for Breast Cancer Research. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/adb235877.

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Rohan, Thomas E. Proteomic Prediction of Breast Cancer Risk: A Cohort Study. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada506647.

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Euhus, David M., Sara Milchgrub, and Raheela Ashfraq. Prediction of Breast Cancer Risk by Aberrant Methylation in Mammary Duct Lavage. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada428491.

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Gregerson, Karen A. Human-Compatible Animal Models for Preclinical Research on Hormones in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574629.

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Olefsky, Jerrold. Role of Inflammation and Insulin Resistance in Mouse Models of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada580520.

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Sun, Leon, Judd W. Moul, Hongyu Wu, Fatiha Bensouda, and Holly Wu. Development of Internet-Accessible Prediction Models for Prostate Cancer Diagnosis, Treatment, and Follow-Up. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada413542.

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