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Статті в журналах з теми "Breast cancer prediction models"
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.
Повний текст джерелаMcCarthy, 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.
Повний текст джерелаEngel, 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.
Повний текст джерелаSridevi, 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.
Повний текст джерелаAntoniou, 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.
Повний текст джерелаZheng, 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.
Повний текст джерелаXiong, 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.
Повний текст джерелаMontazeri, 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.
Повний текст джерелаChaurasia, 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.
Повний текст джерелаZhao, 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.
Повний текст джерелаДисертації з теми "Breast cancer prediction models"
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.
Повний текст джерелаLouro, 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.
Повний текст джерелаIntroducción: 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
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.
Повний текст джерелаThere 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.
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.
Повний текст джерелаStephen, 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.
Повний текст джерелаTakada, 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.
Повний текст джерелаPawluczyk, 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.
Повний текст джерелаDartois, 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.
Повний текст джерелаBackground: 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
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.
Повний текст джерелаCariati, Massimiliano. "Breast cancer stem cells and xenograft models." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612710.
Повний текст джерелаКниги з теми "Breast cancer prediction models"
Pawluczyk, Olga. Volumetric estimation of breast density for breast cancer risk prediction. Ottawa: National Library of Canada, 2001.
Знайти повний текст джерелаUeno, Naoto T. Inflammatory Breast Cancer: An Update. 2nd ed. Dordrecht: Springer Netherlands, 2012.
Знайти повний текст джерелаA more difficult exercise. London: Bloomsbury, 1989.
Знайти повний текст джерелаO'Connell, Fiona Claire. Morphology and gene expression in the postnatal mouse mammary gland. Dublin: University College Dublin, 1997.
Знайти повний текст джерелаFocus on Breast Cancer Research. Nova Science Publishers, 2004.
Знайти повний текст джерелаJames, Paul, and Alison H. Trainer. Constitutional Genomic Variation in Disease Prediction and Prevention: Breast Cancer. Academic Press, 2022.
Знайти повний текст джерелаJames, Paul, and Alison H. Trainer. Constitutional Genomic Variation in Disease Prediction and Prevention: Breast Cancer. Elsevier Science & Technology Books, 2022.
Знайти повний текст джерелаAhlgren, Johan. Studies on Prediction of Axillary Lymph Node Status in Invasive Breast Cancer. Uppsala Universitet, 2002.
Знайти повний текст джерелаNg, Raymond Hin Wai. Plasminogen activators in cancer (experimental tumor models and human breast). 1986.
Знайти повний текст джерелаCheung, Alison Min Yan. Characterization of the biological functions of breast cancer gene BRCA2 using conditionally-inactivated mouse models. 2003.
Знайти повний текст джерелаЧастини книг з теми "Breast cancer prediction models"
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.
Повний текст джерелаAl-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.
Повний текст джерелаDawngliani, 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.
Повний текст джерелаTaghipour, 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.
Повний текст джерелаAL 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.
Повний текст джерелаJena, 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.
Повний текст джерелаDi, 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.
Повний текст джерелаSinha, 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.
Повний текст джерелаIversen, 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.
Повний текст джерелаMohanty, 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.
Повний текст джерелаТези доповідей конференцій з теми "Breast cancer prediction models"
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.
Повний текст джерелаPriya, 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.
Повний текст джерелаAli, 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.
Повний текст джерелаNaveen, 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.
Повний текст джерелаChen, 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.
Повний текст джерелаChen, 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.
Повний текст джерелаGhani, 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.
Повний текст джерелаChun, 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.
Повний текст джерелаEl 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.
Повний текст джерелаHong, 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.
Повний текст джерелаЗвіти організацій з теми "Breast cancer prediction models"
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.
Повний текст джерелаHartmann, 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.
Повний текст джерелаHartmann, 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.
Повний текст джерелаHartmann, 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.
Повний текст джерелаGriep, 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.
Повний текст джерелаRohan, 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.
Повний текст джерелаEuhus, 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.
Повний текст джерелаGregerson, 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.
Повний текст джерелаOlefsky, 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.
Повний текст джерелаSun, 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.
Повний текст джерела