Дисертації з теми "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.
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 джерелаHerschkowitz, Jason I. Perou Charles M. "Breast cancer subtypes, mouse models, and microarrays." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1728.
Повний текст джерелаTitle from electronic title page (viewed Sep. 16, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Genetics and Molecular Biology." Discipline: Genetics and Molecular Biology; Department/School: Medicine.
Leeper, Alexander D. "Developing three dimensional models of breast cancer." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/29218.
Повний текст джерелаJerevall, Piiha-Lotta. "Homeobox B13 in breast cancer : Prediction of tamoxifen benefit." Doctoral thesis, Linköpings universitet, Onkologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68137.
Повний текст джерелаShaheed, Sadr-ul. "Oncoproteomic applications for detection of breast cancer : proteomic profiling of breast cancer models and biopsies." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/14785.
Повний текст джерелаGray, Eoin. "Validating and updating lung cancer prediction models." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/19206/.
Повний текст джерелаIliouchina, Natalia V. (Natalia Vladimirovna) 1979. "Models for the effectiveness of breast cancer screening." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86804.
Повний текст джерелаIncludes bibliographical references (leaf 72).
by Natalia V. Iliouchina.
M.Eng.and S.B.
Lesurf, Robert. "Molecular pathway analysis of mouse models for breast cancer." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32499.
Повний текст джерелаLe cancer du sein est connue pour être une maladie très hétérogène, composé d'un nombre de différents sous-types avec différents niveaux de l'agressivité et distinctes, mais indéfini, profils moléculaires. Ici, nous avons analysé plusieurs nouveaux modèles de souris pour le cancer du sein, dans le cadre des sous-types, et nous avons trouver des parallèles à un certain nombre de niveaux pertinents biologiques. En outre, nous avons développé une méthodologie statistique pour aider à élucider les différents composants moléculaires qui sont à jouer dans un groupe de tumours de sein d'humains ou mammaires murins. Nos résultats indiquent que, même si aucun modèle de souris capte tous les aspects de la maladie chez l'homme, chacun contiennent des composants qui sont partagées par un sous-ensemble de tumeurs mammaires humaines. En outre, notre outil statistique offre de nombreux avantages par rapport aux précédentes méthodes, pour aider à révéler les voies moléculaires qui composent la biologie des tumeurs.
Adams, Caroline. "Exploring tsRNA Function in Aggressive Breast Cancer Cell Models." ScholarWorks @ UVM, 2020. https://scholarworks.uvm.edu/graddis/1157.
Повний текст джерелаOh, Daniel S. Perou Charles M. "Prediction of outcome in breast cancer patients using gene expression profiling." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,501.
Повний текст джерелаTitle from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum in Genetics and Molecular Biology." Discipline: Genetics and Molecular Biology; Department/School: Medicine.
Riggio, Alessandra I. "The role of Runx1 in genetic models of breast cancer." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/9103/.
Повний текст джерелаToh, Alan Kie Leong. "Functional roles of EMP-associated targets in breast cancer models." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/207818/1/Alan%20Kie%20Leong_Toh_Thesis.pdf.
Повний текст джерелаAhlgren, Johan. "Studies on Prediction of Axillary Lymph Node Status in Invasive Breast Cancer." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5221-3/.
Повний текст джерелаMojarad, Ameiryan Shirin. "A reliable neural network-based decision support system for breast cancer prediction." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1738.
Повний текст джерелаAbdull, Mohamed A. Salem. "Data mining techniques and breast cancer prediction : a case study of Libya." Thesis, Sheffield Hallam University, 2011. http://shura.shu.ac.uk/20611/.
Повний текст джерелаKadra, Gais. "Prediction of therapeutic response to paclitaxel, docetaxel and ixabepilone in breast cancer." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX20702.
Повний текст джерелаThe aim of this thesis is to study the sensitivity of breast cancer cell lines BTCL to microtubule-stabilizing agents (Taxanes and ixabepilone) in order to:1- identify pharmaco-genomic predictor of response (resistance /sensitivity) as a molecular signature, and to validate this signature on others studies of which the genomic data are available on line, so gene expression set GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined, and the Taxanes GES predictors has significantly predicted Pac-sensitivity in BTCL, and pathological complete response to Pac-based neo-adjuvant chemotherapy in BC patients.2- study the role of cancer stem cell (ALDH+) on the therapeutic response to Taxanes and their we identify 4 BTCLs which present a significant enrichment in the percentage and the absolute numbers of ALDELFUOR-positive cells found in each of these 4 BTCLs after 5 days of treatment by Paclitaxel , In contrast to the previous results we found that in others 3 BTCLs these phenomenon is inversed with the significant decrease of the percentage, and the absolute numbers of ALDELFUOR-positive cells found in each of these 3 BTCLs after 5 days of treatment by Paclitaxel.A molecular signature of CSC resistant /sensitive of 243 pb with 179 genes of which 152 genes are up- regulated and 27 genes down-regulated in CSC resistant to Paclitaxel, so a genomic predictors for Tax-sensitivity in CSC resistant to Paclitaxel can be derived from BTCL and may be helpful for better understanding the mechanisms of resistance to Taxanes and the implication of CSC in this resistance in order to better select of cytotoxic treatment in breast cancer patients and identification of others potential markers for targeted therapies in the future .3- we tested the impact of the alteration of genomic and proteic parameters or the mutations of some genes like tau (MAPT),Tubulin K- ALPHA (TUB A1B) Tubulin alpha-6 (TUB A1C) Tubulin beta 3 (TUBB3) and Stathmin (STMN1), unfortunately we did'nt identify a mutations to be correlated to BTCL response to Taxanes .4- we try to study at the level of proteins by immunohistochemistry on the tissue micro- array and cyto-micro-array for some parameters which have been already proved (in vitro) to be correlated with response to Taxanes , ( this part is actually ongoing)
Wong, Oi-ling Irene. "Understanding and evaluating population preventive strategies for breast cancer using statistical and decision analytic models." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4284163X.
Повний текст джерелаAbrahamsson, Linda. "Statistical models of breast cancer tumour growth for mammography screening data." Thesis, Uppsala universitet, Matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-171980.
Повний текст джерелаOuinten, Y. "Models to evaluate schemes for an early detection of breast cancer." Thesis, University of Southampton, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380582.
Повний текст джерелаMeier-Hirmer, Carolina. "Multi-State models for the long-term prognosis of breast cancer." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB12046058.
Повний текст джерелаPortnoi, Tally E. "Improving breast cancer risk assessment with image-based deep learning models." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121635.
Повний текст джерелаThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-60).
Discriminative models for breast cancer risk prediction are needed in order to provide personalized patient care. Existing breast cancer risk models incorporate information about breast tissue using imaging biomarkers such as density scores. However, these imaging biomarkers are limited in that they suffer from variability in radiologists' assessments and they reduce the rich information contained in the image down to a single number. In this thesis, I present deep learning models that predict breast cancer risk directly from full images, specifically breast MRIs and mammograms. Our image-based deep learning models out-perform existing breast cancer risk models and our own risk-factor-only models. These results demonstrate that full images contain subtle but significant indicators of risk not captured by traditional risk factors, and that deep learning models can learn these patterns directly from the data.
by Tally E. Portnoi.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Bergqvist, Oscar. "Calibration of Breast Cancer Natural History Models Using Approximate Bayesian Computation." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273605.
Повний текст джерелаNatural history models för bröstcancer är statistiska modeller som beskriver det dolda sjukdomsförloppet. Dessa modeller brukar antingen anpassas till data på individnivå med likelihood-baserade metoder, eller kalibreras mot statistik för hela populationen. Fördelen med att använda data på individnivå är att identifierbarhet hos modellparametrarna kan garanteras. För dessa modeller händer det dock att det är beräkningsintensivt eller rent utav omöjligt att evaluera likelihood-funktionen. Huvudsyftet med denna uppsats är att utforska huruvida metoden Approximate Bayesian Computation (ABC), som används för skattning av statistiska modeller där likelihood-funktionen inte är tillgänglig, kan implementeras för en modell som beskriver bröstcancer hos individer som genomgår mammografiscreening. Som en del av bakgrunden presenteras en sammanfattning av modern ABC-forskning. Metoden består av två delar. I den första delen implementeras en ABC-MCMC algoritm för två enklare modeller. Båda dessa modeller beskriver tumörtillväxten hos individer som ej genomgår mammografiscreening, men modellerna antar olika typer av tumörtillväxt. Algoritmen testades i en simulationsstudie med syntetisk data genom att jämföra resultaten med motsvarande från likelihood-baserade metoder. I den andra delen av metoden undersöks huruvida ABC är kompatibelt med modeller för bröstcancer hos individer som genomgår screening. Genom att lägga till en modell för uppkomst av tumörer och göra det förenklande antagandet att alla individer i populationen genomgår screening vid samma ålder, kunde en ABC-MCMC algoritm utvecklas med hänsyn till data på individnivå. Algoritmen testades sedan i en simulationsstudie nyttjande syntetisk data. Framtida studier behövs för att undersöka algoritmens statistiska egenskaper (genom upprepad simulering av flera dataset) och för att testa den mot observationell data där tidigare parameterskattningar finns tillgängliga.
Arif, Km Taufiqul. "Functional association of Micrornas with molecular subtypes of breast cancer." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213110/1/Km%20Taufiqul_Arif_Thesis.pdf.
Повний текст джерелаCartaxo, Ana L. "Tumor microenvironment models: ex vivo, in vitro and in silico approaches to address targeted therapies." Doctoral thesis, Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica António Xavier, 2020. http://hdl.handle.net/10362/105645.
Повний текст джерелаN/A
Schoen, Eva G. "Perceived existential meaning, coping, and quality of life in breast cancer patients : a comparison of two structural models." Virtual Press, 2003. http://liblink.bsu.edu/uhtbin/catkey/1263897.
Повний текст джерелаJamieson, Lauren Elizabeth. "Measuring redox potential in 3D breast cancer tumour models using SERS nanosensors." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/25964.
Повний текст джерелаArochena, H. E. "Modelling and prediction of parameters affecting attendance to the NHS breast cancer screening programme." Thesis, Coventry University, 2003. http://curve.coventry.ac.uk/open/items/3d5373c6-9442-4479-77a2-c1bc37662cf5/1.
Повний текст джерелаNAGANAWA, SHINJI, MASATAKA SAWAKI, AKIKO NISHIO, SATOKO ISHIGAKI, HIROKO SATAKE, and MARIKO KAWAMURA. "EARLY PREDICTION OF RESPONSE TO NEOADJUVANT CHEMOTHERAPY FOR LOCALLY ADVANCED BREAST CANCER USING MRI." Nagoya University School of Medicine, 2011. http://hdl.handle.net/2237/15357.
Повний текст джерелаAhnström, Waltersson Marie. "Cell cycle alterations and 11q13 amplification in breast cancer : prediction of adjuvant treatment response." Doctoral thesis, Linköpings universitet, Onkologi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17458.
Повний текст джерелаWong, Oi-ling Irene, and 黃愛玲. "Understanding and evaluating population preventive strategies for breast cancer using statistical and decision analytic models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4284163X.
Повний текст джерелаGarcía, Parra Jetzabel 1983. "PARP1 expression in breast cancer and effects of its inhibition in preclinical models." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/84173.
Повний текст джерелаEl càncer de mama és la principal causa de mort per càncer en dones. La millora dels tractaments i la detecció precoç estan reduint la taxa de mort, però segueix sent elevada. Identificar noves dianes per predir la resposta a tractaments és clau per millorar les teràpies contra aquest càncer i la supervivència. Els inhibidors de PARP van aparèixer com una teràpia prometedora, particularment en càncers BRCA-mutants, però, cal dur a terme més estudis preclínics i translacionals per fomentar un desenvolupament racional d’aquesta teràpia en càncer de mama. Aquest treball descriu l’expressió de PARP1 en mostres de tumors mamaris i caracteritza els efectes de la seva inhibició a models preclínics. Vam observar que la sobreexpressió nuclear de la proteïna PARP1 fou associada amb: la transformació maligna; mal pronòstic en càncer de mama; i fou més freqüent al subtipus triple-negatiu, però també es va detectar en un subgrup de càncers de mama receptors d’estrogen positius i HER2 positius. En models preclínics, PARP1 va exercir rols diferents als diferents subtipus de càncer de mama. Per altra banda, vam descriure que olaparib (inhibidor de PARP) té efectes antitumorals en els diversos subtipus, i combinat amb trastuzumab (anticòs anti-HER2) potencia els efectes antitumorals d’aquesta teràpia.
Lal, Suchita K. "The role of corticotropin-releasing hormone (CRH) in cellular models of breast cancer." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/61785/.
Повний текст джерелаLewis, Deana L. "Angiogenic Characteristics of Tumor-Associated Dendritic Cells in Ovarian and Breast Cancer Models." Ohio University Art and Sciences Honors Theses / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ouashonors1462296303.
Повний текст джерелаArnerlöv, Conny. "Prediction of prognosis in human breast cancer : a study on clinicopathologic and cytometric prognostic factors." Doctoral thesis, Umeå universitet, Patologi, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-100584.
Повний текст джерелаS. 3-38: sammanfattning, s. 39-94: 5 uppsatser
digitalisering@umu
Pochampalli, Mamata Rani. "Characterization of Effects of Muc1 Expression on Epidermal Growth Factor Receptor Signaling in Breast Cancer." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/194355.
Повний текст джерелаEstévez, Cebrero María de los Ángeles. "Influence of paracrine signalling within the tumour microenvironment on progression in breast cancer models." Thesis, University of Nottingham, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.727116.
Повний текст джерелаNaik, Shambhavi. "Characterisation of TRAIL receptor signalling to apoptosis in pre-clinical models of breast cancer." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/9913.
Повний текст джерелаSykes, Jennifer. "Behavioural healthcare modelling : incorporating behaviour into healthcare simulation models ; a breast cancer screening example." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438669.
Повний текст джерелаHUANG, SU-HSIN, and 黃素馨. "Prediction Models of 5-year Mortality Analysisafter Breast Cancer Surgery." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/89885625924226301792.
Повний текст джерела高雄醫學大學
醫學研究所
101
Background and Purpose Breast Cancer is the most common cancer in the world women. Also, the breast cancer is causing the highest mortality rate the main reason. The breast cancer is the number one killer for women and become the highest ranking in Asia. This study is therefore comparing artificial neural network (ANN) and logical regression (LR) prediction models to find the best of important effect factors. The purposes of this research are as follows: Ⅰ、To investigate long-term trend analysis of the breast cancer patient after surgery in 5-year mortality; Ⅱ、To compare the accuracy of different predict models for breast cancer patients after surgery in 5-year mortality; Ⅲ、To conduct the global sensitivity analyze and to estimate the significant predictors for breast cancer patients after surgery in 5-year mortality. Research Methods This study subject of "National Health Insurance Research Database" is the research framework. The study design used the retrospective method. The study period is from 1996 to 2010. The study subjects of the breast cancer patients are above sixteen years old after surgery. The samples of study are total 3,632 people. The use of the diagnostic codes ICD-9-CM174x (174.0-174.9) and disposal code 85.20-23,85.33-36,85.4 x, 85.5x, 85.6x, 85.7x, 85.8x, 85.95 find the predictor factors of meaningful. Investigate the important effect factors are by the breast cancer patient analysis after surgery in 5-year mortality and use of the patient''s characteristics, hospital characteristics and time characteristics, separately. In addition, the significant predictors are used in the ANN and LR to build model and to compare the accuracy. SPSS 19.0 statistical software was employed for the data collection and analysis. The main statistical methods include: descriptive statistics and inferential statistics (trend analysis, university analysis, and multivariate analysis - including logistic regression and neural networks and sensitivity analysis). Results In this study, the model build are used data mining technology of ANN and LR and seven important variables (age, charlson comorbidity index (CCI), hospital level, hospital volume, surgeon volume, chemotherapy, radiotherapy and hormone therapy). The results show that ANN model is better than LR model. The sensitivity of ANN and LR is 5.66% and 3.77%, respectively. The 1-specificity, positive predictive value, negative predictive value and accuracy are good performance. The AU-ROC curve is 0.70 and 0.52, respectively. Generally, the performance of ANN is better than LR model. The first three important predictors of ANN are surgeon volume, chemotherapy and age. The LR model are the surgeon volume, age, charlson comorbidity index (CCI). Conclusions and Recommendations The research results found that the distribution trend for the breast cancer patient after surgery in 5-year mortality is a significant change with time in patients’ characteristics and hospital characteristics. The means show that the correlation significant factors are worth applied in clinically and become improvement factors even if as standard of treatment guidelines. To compare the different predict models, found that the neural network can be used to expand predict variable items. The various diseases are easy to analyze and investigate, systematically. The death predicts model is use of appropriate research methods with development. In the future, this prediction model can applied to other cancers. In addition, the most important factor is surgeon volume. So that, how to enable health authorities to assist and train surgeons relevant experience reference. Secondary the impact factors such as age are increased with mortality rate. Therefore, the breast cancer should repeated think the relation with age and pay attention in how to promote and improve screening rates issues and to reach early detection and early treatment. Finally, this study expect can provide clinical reference value for medical staff and integrate medical treatment and to establish a predict model of medical decision.
Thongkam, Jaree. "Towards Breast Cancer Survivability Prediction Models in Thai Hospital Information Systems." Thesis, 2009. https://vuir.vu.edu.au/29496/.
Повний текст джерелаAndrade, Bruno Filipe Aveleira. "Prediction Model for Women Breast Cancer Recurrence." Master's thesis, 2015. http://hdl.handle.net/10316/35675.
Повний текст джерелаBreast Cancer (BC) is the second most frequently diagnosed cancer and the fth cause of cancer mortality worldwide. Among women, it is the leading cause of cancer deaths, with more than 500 000 registered deaths in 2012, and Portugal also re ects that reality. Survival prediction plays a crucial role in diseases with associated high mortality rates, since it has the power to help clinicians to de ne each patient's prognosis, thus allowing to personalize the corresponding treatments. Particularly for BC, prognosis is related to the patterns of recurrence (cancer that reappears after treatment), and it even di ers depending on the local involved. This work analyses the data of a cohort of 97 patients, with a total of 27 characteristics, more than 50% of them incomplete. Therefore, the rst step is to handle Missing Data (Imputation or Deletion), to perform Classi cation afterwards. The purpose is to study the prognostic factors that de ne recurrence of female BC, to try to build a model that accurately predicts recurrence patterns, which would create the possibility of more targeted treatments. The application of machine learning algorithms to the prediction of recurrence in di erent sites seems to be a novel application of these methodologies, and the results can lead the way to a better understanding of the pathways of BC recurrence.