Дисертації з теми "Breast cancer prediction"
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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.
Повний текст джерелаRipley, 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 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 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.
Ahlgren, 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 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
Abdull, 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)
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 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 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
Pickles, Martin Darren. "Prediction of response of patients with breast cancer to neoadjuvant chemotherapy using advanced magnetic resonance imaging." Thesis, University of Hull, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440132.
Повний текст джерелаNicolò, Chiara. "Mathematical modelling of neoadjuvant antiangiogenic therapy and prediction of post-surgical metastatic relapse in breast cancer patients." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0183.
Повний текст джерелаFor patients diagnosed with early-stage cancer, treatment decisions depend on the evaluation of the risk of metastatic relapse. Current prognostic tools are based on purely statistical approaches that relate predictor variables to the outcome, without integrating any available knowledge of the underlying biological processes. The purpose of this thesis is to develop predictive models of the metastatic process using an established mechanistic modelling approach and the statistical mixed-effects modelling framework.In the first part, we extend the mathematical metastatic model to describe primary tumour and metastatic dynamics in response to neoadjuvant sunitinib in clinically relevant mouse models of spontaneous metastatic breast and kidney cancers. The calibrated model is then used to test possible hypothesis for the differential effects of sunitinib on primary tumour and metastases, and machine learning algorithms are applied to assess the predictive power of biomarkers on the model parameters.In the second part of this thesis, we develop a mechanistic model for the prediction of the time to metastatic relapse and validate it on a clinical dataset of breast cancer patients. This model offers personalised predictions of the invisible metastatic burden at the time of diagnosis, as well as forward simulations of metastatic growth, and it could be used as a personalised prediction tool to assist in the routine management of breast cancer patients
Raoufi-Danner, Torrin. "Effects of Missing Values on Neural Network Survival Time Prediction." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150339.
Повний текст джерелаWebster, Rebecca. "Complementary investigations of the molecular biology of cancer : assessment of the role of Grb7 in the proliferation and migration of breast cancer cells; and prediction and validation of microRNA targets involved in cancer." University of Western Australia. School of Medicine and Pharmacology, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0179.
Повний текст джерелаAssi, Valentina. "Clinical and epidemiological issues and applications of mammographic density." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/7855.
Повний текст джерелаЛазарук, О. В. "Експресія металопротеїназ у випадках протокової карциноми грудної залози з метастазами та без них для прогнозування пухлинних метастазів". Thesis, Сумський державний університет, 2017. http://essuir.sumdu.edu.ua/handle/123456789/54538.
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 джерелаGabriel, Augusto Ribeiro. "Expressão de marcadores biológicos em câncer de mama antes e após a quimioterapia neoadjuvante. I- Correlações com desfechos clínicos e entre marcadores." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4332.
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With the exception of skin cancer, breast cancer remains the most common malignant neoplasm affecting women both in Brazil and worldwide, inflicting severe economic, social and emotional consequences on patients and their families. Despite advances made in diagnostic and therapeutic techniques over recent decades, mortality rates from breast cancer remain expressive. To be able to treat tumors appropriately, not only profound knowledge of the cell mechanisms involved in their genesis, but also knowledge of the mechanisms involved in the success or failure of treatment is crucial. Various methods have been developed for this purpose, including the evaluation of biological tumor markers and the genetic studies. The objective of the present study was to evaluate some biological markers involved in the differentiation and evolution of breast cancer, using immunohistochemistry on tissue arrays. A retrospective study was conducted between 2006 and 2012 in which clinical data were obtained from patient charts, and tissue samples conserved in paraffin blocks were prospectively analyzed and correlated with each other and with the patient’s response to neoadjuvant chemotherapy. The results were presented in two papers. In the first article, biomarker expression was evaluated in biopsy specimens obtained at diagnosis and then following treatment with adjuvant chemotherapy, with correlations being drawn between the differences found. Statistically significant differences were found in Ki-67, IGF-1, topoisomerase II-alpha and CK5/6 marker expression, indicating the effect of chemotherapy on the proliferation index of the malignant breast tumors. On the other hand, no statistically significant differences were found in HER2, estrogen and progesterone receptors, PTEN or EGFR. In the second paper, correlations were sought between biological marker expression and the patient’s outcome response to previous chemotherapy, with results showing significant correlations between the HER2 and topoisomerase II-alpha markers and pathologic complete response despite the fact that the sample was small. No other statistically significant correlations were found with any of the other markers evaluated. When molecular subtypes were analyzed, the study showed a greater frequency of pathologic complete response for the HER2 subtype and this difference was statistically significant. Another important result was the correlation between the tendency towards a reduction in mean Ki-67 values and a clinical benefit from the treatment implemented a finding that led to the preparation of a third paper, which consisted of an integrative review of the Ki-67 marker. This review concluded that further studies need to be conducted on the Ki-67 marker and that its expression should be analyzed dynamically to establish whether a correlation exists between this marker and patients’ prognosis and whether Ki-67 is a predictor of treatment response.
Tanto no Brasil quanto no mundo, excluindo-se o câncer de pele, o câncer de mama ainda é a neoplasia maligna que mais acomete as mulheres, trazendo prejuízo econômico, social e emocional para elas e suas famílias. Apesar dos avanços diagnósticos e terapêuticos observados nas últimas décadas, o câncer de mama ainda carrega taxas de mortalidade expressivas. Para que se possa tratar de forma adequada os tumores, é imprescindível o conhecimento profundo dos mecanismos celulares envolvidos na sua gênese, bem como dos mecanismos envolvidos no sucesso ou fracasso do tratamento. Vários métodos foram desenvolvidos neste sentido, como o estudo de marcadores biológicos dos tumores e estudos genéticos. O presente estudo teve como proposta avaliar alguns marcadores biológicos envolvidos na diferenciação e evolução do câncer de mama através da técnica de imunohistoquímica em amostras preparadas em matrizes de arranjo teciduais. Realizou-se um estudo retrospectivo no período compreendido entre 2006 e 2012, quando foram obtidos dados clínicos de prontuários e amostras de tecidos conservados em blocos de parafina, prospectivamente analisados e correlacionados entre si e com os desfechos de resposta à quimioterapia neoadjuvante. Os resultados foram apresentados em dois artigos. No primeiro artigo avaliou-se a expressão dos marcadores nas biópsias quando da realização do diagnóstico e após o tratamento com a quimioterapia adjuvante, correlacionando-se as diferenças encontradas. Diferenças de expressão dos marcadores Ki67, IGF-1, Topoisomerase II-alfa e CK5/6 foram observadas, com significado estatístico indicando o efeito da quimioterapia no índice de proliferação dos tumores malignos de mama. Por outro lado, os marcadores HER2, receptores de estrógenos, receptores de progesterona, PTEN e EGFR não apresentaram diferenças significativas. No segundo artigo, correlacionou-se a expressão dos marcadores biológicos com os desfechos de resposta à quimioterapia prévia e concluiu-se que, embora em uma amostra pequena, os marcadores HER2 e Topoisomerase II-alfa apresentaram correlação significativa com a resposta patológica completa, o que não aconteceu com os demais marcadores. Analisando subtipos moleculares este estudo evidenciou, de forma estatisticamente significativa, maior frequência de resposta patológica completa para o subtipo HER2. Outro achado importante foi evidenciado na correlação entre a tendência na redução da média dos valores de Ki67 e o benefício clínico do tratamento realizado, fato este que levou à elaboração do terceiro artigo, uma revisão integrativa acerca do marcador Ki67. Esta revisão permitiu concluir que o marcador em análise ainda precisa ser objeto de estudos e que sua expressão deve ser analisada de forma dinâmica, para avaliar se a mesma correlaciona-se com o prognóstico das pacientes e a predição de resposta ao tratamento.
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.
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
Rathnagiriswaran, Shruti. "Identifying genomic signatures for predicting breast cancer outcomes." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5906.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains viii, 85 p. : col. ill. Includes abstract. Includes bibliographical references (p. 81-85).
Hupperets, Pierre Stefanus Gerardus Johannes. "Prognostic and predictive factors in primary breast cancer." Maastricht : Maastricht : Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 1995. http://arno.unimaas.nl/show.cgi?fid=8354.
Повний текст джерелаVillman, Kenneth. "Chemosensitivity in Breast Cancer." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : [Univ.-bibl. [distributör]], 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7459.
Повний текст джерелаJames, C. R. "BRCA1, a predictive biomarker in breast and ovarian cancer." Thesis, Queen's University Belfast, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479243.
Повний текст джерелаCizkova, Magdalena. "Pronostic and Predictive Markers in Breast Cancer - PI3K Signaling Pathway." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA11T021.
Повний текст джерелаResults of the presented research projects bring information about several aspects of the PI3K signaling pathway roles in breast cancer development and treatment response. The particular projects covered the subjects connected with the signaling pathway, ranging from the HER family receptors activating the pathway, and PI3K to the downstream levels of signalisation. The prognostic and predictive effect of PI3K deregulation was the central subject of the described research. The decreased expression of PIK3R1 associated with reduced survival of our patients. A special focus was put on the PIK3CA mutations which are common in breast cancer. Whereas the PIK3CA mutations act as a good prognostic marker in patients non-treated with the HER2 inhibitors, these mutations predict a negative response to trastuzumab treatment. The described results, furthermore, draw attention to the role of several altered molecular signaling pathways in breast cancer development, especially to the Wnt signaling pathway. The lapatinib plasma levels showing the relevant increase in comparison with the already described efficient steady-state levels were also described in one of the projects. Moreover, various modifications to EGFR status assessment were compared and showed that EGFR FISH and IHC count interpretation depended significantly on method and thresholds used. All these subjects are connected by the PI3K pathway, the need to deepen current knowledge and bring new useful information applicable in future clinical practice
Kiltie, Anne Elizabeth. "DNA damage as a predictor of normal tissue response to radiotherapy." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244711.
Повний текст джерелаFields, Cheryl B. "Predicting Breast Cancer Screening Among African American Lesbians and Bisexual Women." ScholarWorks, 2011. https://scholarworks.waldenu.edu/dissertations/926.
Повний текст джерелаWebster, Elizabeth Natalie. "Health care Facilities as a Predictor of Breast Cancer Survival Rates." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6145.
Повний текст джерелаPorock, Davina. "Predicting the severity of radiation skin reactions in women with breast cancer." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1998. https://ro.ecu.edu.au/theses/992.
Повний текст джерелаPark, Keon-Young. "Predicting patient-to-patient variability in proteolytic activity and breast cancer progression." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53479.
Повний текст джерелаZhu, Li, and 朱麗. "Determination of predictive markers related to micro-metastasis in breast cancer patients." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30330919.
Повний текст джерелаDesmedt, Christine. "Multi-marker detection approach for improving breast cancer treatment tailoring." Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210415.
Повний текст джерела\
Doctorat en Sciences biomédicales et pharmaceutiques
info:eu-repo/semantics/nonPublished
Machaj, Agnieszka S. "Breast Cancer in PTEN Hamartoma Tumor Syndrome: Can a Predictive Fingerprint be Identified?" Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1397736695.
Повний текст джерелаFernandes, 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.
Hopp, Alix. "Effectiveness of Using Texture Analysis in Evaluating Heterogeneity in Breast Tumor and in Predicting Tumor Aggressiveness in Breast Cancer Patients." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/603653.
Повний текст джерелаObjective and Hypothesis We hypothesize that tumor heterogeneity or tissue complexity, as measured by quantitative texture analysis (QTA) on mammogram, is a marker of tumor aggressiveness in breast cancer patients. Methods Tumor heterogeneity was assessed using QTA on digital mammograms of 64 patients with invasive ductal carcinoma (IDC). QTA generates six different values – Mean, standard deviation (SD), mean positive pixel value (MPPV), entropy, kurtosis, and skewness. Tumor aggressiveness was assessed using patients’ Oncotype DX® Recurrence Score (RS), a proven genomic assay score that correlates with the rate of remote breast cancer recurrence. RS and hormonal receptor status ‐ estrogen receptor (ER) and progesterone receptor (PR) ‐ were collected from pathology reports. Data were analyzed using statistical tools including Spearman rank correlation, linear regression, and logistic regression. Results Linear regression analysis showed that QTA parameter, SD, was a good predictor of RS (F=6.89, p=0.0108, R2=0.0870) at SSF=0.4. When PR status was included as a predictor, PR status and QTA parameter Skewness‐Diff, achieved linear model of greater fit (F=15.302, p<0.0001, R2=0.2988) at SSF=1. Among PR+ patients, Skewness‐Diff was a good linear predictor of RS (F=9.36, p=0.0034, R2=0.1320) at SSF=0.8. Logistic regression analysis showed that QTA parameters were good predictors of high risk RS probability, using different cutoffs of 30 and 25 for high risk RS; these QTA parameters were Entropy‐Diff for RS>30 (chi2=10.98, p=0.0009, AUC=0.8424, SE=0.0717) and Mean‐Total for RS>25 (chi2=9.98, p=0.0016, AUC=0.7437, SE=0.0612). When PR status was included, logistic models of higher log‐likelihood chi2 were found with SD‐Diff for RS>30 (chi2=18.69, p=0.0001, AUC=0.9409, SE=0.0322), and with Mean‐Total for RS>25 (chi2=25.56, p<0.0001, AUC=0.8443, SE=0.0591). For PR+ patients, good predictors were SD‐Diff for RS>30 (chi2=6.87, p=0.0087, AUC=0.9212, SE=0.0515), and MPP‐Diff and Skewness‐Diff for RS>25 (chi2=16.17, p=0.0003, AUC=0.9103, SE=0.0482). Significance Quantitative measurement of breast cancer tumor heterogeneity using QTA on digital mammograms may be used as predictors of RS and can potentially allow a non‐invasive and cost‐effective way to quickly assess the likelihood of RS and high risk RS.
Clarke, Matthew Alan. "Predictive computational modelling of the c-myc gene regulatory network for combinatorial treatments of breast cancer." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/284163.
Повний текст джерелаWon, Jennifer Renae. "Clinical performance of diagnostic, prognostic and predictive immunohistochemical biomarkers for hormone receptor-negative breast cancer." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/53534.
Повний текст джерелаMedicine, Faculty of
Pathology and Laboratory Medicine, Department of
Graduate
Karmakar, Monita. "Predicting Adherence to Aromatase Inhibitor Therapy in Patients with Breast Cancer Using Protection Motivation Theory." University of Toledo Health Science Campus / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=mco1365094849.
Повний текст джерелаTing-YuChou and 周亭余. "The Study of the Breast Cancer Prediction." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/brtctp.
Повний текст джерела國立成功大學
醫學資訊研究所
102
In recent years, with the rapid advances in science and technology, people have paid more attentions to self-health conditions by using health examination. The health examination can avoid people missing the best time of disease diagnosis and treatment. The medical records of patients and mammogram diagnosis are contributory factors of breast cancer. Instead of using medical records or mammogram apart, the proposed method combines features automatically extracted from mammograms and medical records of patients to build a breast cancer prediction model. In preprocessing step of imaging data, the proposed method uses fast and adaptive bidimensional empirical mode decomposition (FABEMD) to segment the mammograms for glandular tissue. After integrating imaging data and clinical data, the proposed method uses search constraints to select significant features. The proposed approach solves the problem of the traditional decision tree which has complicated branches, not only saves time but also effectively improves the accuracy of prediction model of breast cancer. Our method was applied to real dataset which consists of 579 patients, and the results show that the proposed method attains high accuracy of 98%.
Andrade, 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.
Lee, Hsiao-Ping, and 李曉萍. "Prediction of microRNAs Targeting Breast Cancer mRNAs." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/63916658849528113982.
Повний текст джерела亞洲大學
生物資訊學系碩士班
96
MicroRNAs (miRNAs) are non-coding RNA with a site of about 22 nucleotides long. They are tailored from the hairpin stem-loop miRNA precursors. MiRNAs do not translate into proteins, but taking part in many kinds of in vivo biological process. Many biological researches indicated that miRNAs are involved in the tumor developing processes, and they could possibly regulate tumor suppressor genes (TSG) or oncogenes. In the first part of this thesis, we investigate the possibility that miRNAs could possibly targeting human breast cancer TSG. In the second part, a large-scale search of over- and under-expressed breast mRNAs is performed by using the ArrayExpress microarray data. The binding strength of miRNA and mRNA is obtained by using miRanda, and the correlation of miRNAs and mRNAs expression levels in different tissues is obtained by computing the PEARSON correlation coefficients. Certain miRNAs are identified that could possibly regulating TSG and OG. It is hoped the result of this research could provide useful information for a better understanding of breast cancer induction.
Chang, Wei-Pin, and 張偉斌. "Construction genetic algorithm prediction model in breast cancer / liver cancer." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/39719888564623775416.
Повний текст джерела國立陽明大學
公共衛生研究所
96
In recent years, Data Mining attracts great concern from information industries, its main reason is that a large amount of extant materials can be used extensively, and there are urgent demands to be changed these materials into useful information and knowledge. The information and knowledge obtained are admissible to improve and promote efficiency, the field used includes very much, and the application case that Data Mining in the medical field increases gradually. According to records from Department of Health, Breast cancer and Liver cancer were major manifestations among Taiwanese population leading to deaths of top ten causes in Taiwan. These two indications had some characteristics in common as increasing risk with increasing age and sharing the same pool of risk factors in our living environment. The central role of data mining uses artificial intelligence and statistical methods to extract meaningful information from puzzles of variables and data. The present study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer and liver cancer. The artificial neural network, decision tree, logistic regression, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients, 729 records acquire from the liver cancer patient. In breast cancer data, 9 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of logistic regression model were 0.9637 (sensitivity 0.9716 and specificity 0.9482), the decision tree model 0.9435 (sensitivity 0.9615, specificity 0.9105), the neural network model 0.9502 (sensitivity 0.9628, specificity 0.9273), the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the logistic regression model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9435 which was the lowest of all 4 predictive models. The standard deviation of the 10-fold cross-validation was rather unreliable. On other hand, liver cancer data include 12 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of logistic regression model were 0.7658 (sensitivity 0.7682 and specificity 0.7630, the decision tree model 0.7636 (sensitivity 0.7497, specificity 0.7793), the neural network model 0.7760 (sensitivity 0.7875, specificity 0.7679), the genetic algorithm model 0.8072 (sensitivity 0.8444, specificity 0.0.763). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.7684. The predicted outcome of the neural network model was higher than that of the logistic regression model and decision model but no significant difference was observed. The present study indicated that the genetic algorithm model yielded better results than other data mining models for the analysis of the data of breast cancer and liver cancer patient in terms of the overall accuracy of the patient classification, the expression and complexity of the classification rule. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data/liver cancer data and the classification rule identified was more acceptable and comprehensible.
Lien, Chang Chun, and 張純蓮. "A data mining approach to prediction of breast cancer relapse." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78447519470451170091.
Повний текст джерела南台科技大學
企業管理系
95
The incidence and mortality rate of breast cancer in Taiwanese Women have increased gradually due to the urban life style and westen style food.In the recent 5 years, the incidence of breast cancer in Taiwanese Women became the first in all cancers. The highest perioid of incidence of breast cancer is between 45 to 55 years old. In the early stage of breast cancer, it is almost asymptoatic and keep the patients from medical help.When the breast cancer was diagnosed, many of them aleady have lymph node metastasis. This situation also lifts the recurrent rate. Due to the progress of information technology and medical information system, hospitals also have accumlated a large amount of data in the database of information systems. Therefore, much useful medical knowledge could be mined from the history data. The prediction of breast cancer relapse is very helpful for post-operative treatment and followup. The statistical methods had been applied to predict breast cancer relapse. However, this study employed data mining techciques, including C4.5 decision tree and SVM, to construct recurrence prediction models of breast cancer. To improve the prediction efficiency, this study also applied committee machine methods, including AdaBoost and Bagging, to increase the relapse prediction accuracy. The empirical results show that AdaBoost mechanism can ehance prognosis accuracy of C4.5 and SVM models on breast cancer relapse. Keyword:Breast cancer relapse、C4.5 decision tree、Support Vector Machine、Committee Machine
HUANG, 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.
Yun, Tsai Meng, and 蔡孟芸. "A Diagnosis system for Breast Cancer Classification and Pathological Section Prediction." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/39811780671582660899.
Повний текст джерела國立中正大學
電機工程研究所
91
The objective of this thesis is to build a diagnosis system for breast cancer classification and pathological section prediction. In the classification part, the tumor images were first sampled by two methods: taking (1) the largest rectangular region inside the tumor image, and (2) the 64×64 rectangular part that has the darkest grey-levels. The features were extracted from the co-occurrence matrix and multi-resolution decomposition by wavelet transform. Linear discrimant analysis and k-NN methods were used to classify the tumors into benign and malignant ones. Best results were found in the combination of using the largest rectangular segment of the tumor image, extracting the contrast of the co-occurrence matrix as features, and classifying by linear discriminant analysis. The recognition rates were 84.6 % for the benign and 78.38 % for the malignant tumors, respectively. The second part of this study is trying to predict the possible pathological section appearance by analyzing the ultrasound images. Regions of interest (ROIs) of the ultrasound images were marked by the doctors. Two neighbor small (32×32) image sections were segmented from the marked region and one is used in training the classifier and the other for testing. Based on the studies in the classification part, contrast and entropy calculated from the co-occurrence matrix were chosen as the prominent features for the image query, and the first five images in the database that are nearest in the features space were retrieved. From the 34 image query tests, 24 (70.58%) of their corresponding images were retrieved and were ranked in the first order, and 31 (91.18%) were found in the first five nearest images.
Nugraha, Leo, and 陳德禮. "Metastasis Cancer Prediction of Breast Ultrasound Using Deep Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dse3hd.
Повний текст джерела國立臺灣大學
資訊工程學研究所
106
Breast cancer is the most commonly diagnosed cancer and is the second leading cause of death among women after lung cancer. Patients who suffer from breast cancer can still recuperate with early diagnosis and proper treatment, despite the fact that breast cancer possesses a high mortality rate. If untreated, breast cancer can surreptitiously spread and invade other organs by transporting its cells via nearby hematogeneous or lymphatic routes. This type of breast cancer is classified as metastatic breast cancer. Whereas, the non-metastatic cancer does not possess this ability. This thesis presents a breast cancer classification between the metastasis and non-metastasis using the densely connected convolutional neural network (DenseNet). Several studies have also successfully revealed the presence of suspicious tissue surrounding the tumor region (peritumor). Inspired by a previous study that utilized image matting to obtain the peritumor from the trimap, the peritumor can further be extracted at different pixel thicknesses by adjusting the unknown region thickness of the trimap. Thus, this study trained the neural network using peritumor images (instead of the tumor only) at different pixel thicknesses: 5, 10, 15, 20, 25, and 30 pixels. This study finds that the peritumor 15 pixels achieved the best performance with an accuracy of 84.8%, a sensitivity of 88.8%, a specificity of 81.3%, and an area under the curve (AUC) of receiver operating characteristic (ROC) of 0.926. In addition, based on the results, the peritumor 10 pixels may not be too farfetched from being considered for future studies as it scored an accuracy of 84.1%, a sensitivity of 83.2%, a specificity of 84.8%, and an AUC score of 0.906.
Huang, Chuan En, and 黃傳恩. "Using Machine Learning Methods for Breast Cancer Metastasis and Recurrence Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4fs52k.
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