Relevant bibliographies by topics / Breast cancer prediction

Academic literature on the topic 'Breast cancer prediction'

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

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Breast cancer prediction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Breast cancer prediction"

1

Khubchandani, Pratham, Harshith Deeti, and Beeram Harsh. "Breast Cancer Prediction in Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 274–77. http://dx.doi.org/10.22214/ijraset.2022.47870.

Full text
Abstract:
Abstract: Breast maximum cancers is maximum cancers that office work withinside the cells of the breasts. After pores and pores and skin maximum cancers, breast maximum cancers is the most common location maximum cancers identified in girls withinside the United States. Breast maximum cancers can get up in every men and girls, but it's far more now no longer unusualplace in girls. Substantial resource for breast maximum cancers popularity and research funding has helped created advances withinside the evaluation and treatment of breast maximum cancers. Breast maximum cancers survival costs have increased, and the amount of deaths associated with this sickness is regularly declining, in big element due to factors along with earlier detection, a state-of-the-art custom designed approach to treatment and a better data of the sickness. Machine reading (ML) is a form of artificial intelligence (AI) allowing program application applications to emerge as more accurate at predicting results without the need of programmed to do so. Machine reading algorithms use anciental records as input to anticipate new output values.
APA, Harvard, Vancouver, ISO, and other styles
2

Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.

Full text
Abstract:
In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.
APA, Harvard, Vancouver, ISO, and other styles
3

Kumari, Madhu, and Vijendra Singh. "Breast Cancer Prediction system." Procedia Computer Science 132 (2018): 371–76. http://dx.doi.org/10.1016/j.procs.2018.05.197.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Das, Akhil Kumar, Saroj Kumar Biswas, and Ardhendu Mandal. "An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree." Indian Journal Of Science And Technology 15, no. 45 (December 5, 2022): 2441–50. http://dx.doi.org/10.17485/ijst/v15i45.756.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kumar, Mukesh, Saurabh Singhal, Shashi Shekhar, Bhisham Sharma, and Gautam Srivastava. "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning." Sustainability 14, no. 21 (October 27, 2022): 13998. http://dx.doi.org/10.3390/su142113998.

Full text
Abstract:
Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.
APA, Harvard, Vancouver, ISO, and other styles
6

Xiong, Wei, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, and Jiebo Luo. "Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders." BME Frontiers 2022 (April 7, 2022): 1–10. http://dx.doi.org/10.34133/2022/9763284.

Full text
Abstract:
Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal-related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational autoencoder (T-VAE) model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.
APA, Harvard, Vancouver, ISO, and other styles
7

Pankratz, V. Shane, Amy C. Degnim, Ryan D. Frank, Marlene H. Frost, Daniel W. Visscher, Robert A. Vierkant, Tina J. Hieken, et al. "Model for Individualized Prediction of Breast Cancer Risk After a Benign Breast Biopsy." Journal of Clinical Oncology 33, no. 8 (March 10, 2015): 923–29. http://dx.doi.org/10.1200/jco.2014.55.4865.

Full text
Abstract:
Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.
APA, Harvard, Vancouver, ISO, and other styles
8

Derouane, Françoise, Cédric van Marcke, Martine Berlière, Amandine Gerday, Latifa Fellah, Isabelle Leconte, Mieke R. Van Bockstal, Christine Galant, Cyril Corbet, and Francois P. Duhoux. "Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine." Cancers 14, no. 16 (August 11, 2022): 3876. http://dx.doi.org/10.3390/cancers14163876.

Full text
Abstract:
Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.
APA, Harvard, Vancouver, ISO, and other styles
9

Ye, Guolin, Suqun He, Ruilin Pan, Lewei Zhu, Dan Zhou, and RuiLiang Lu. "Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction." Journal of Healthcare Engineering 2022 (February 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/4477099.

Full text
Abstract:
Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.
APA, Harvard, Vancouver, ISO, and other styles
10

McCarthy, Anne Marie, Zoe Guan, Michaela Welch, Molly E. Griffin, Dorothy A. Sippo, Zhengyi Deng, Suzanne B. Coopey, et al. "Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort." JNCI: Journal of the National Cancer Institute 112, no. 5 (September 26, 2019): 489–97. http://dx.doi.org/10.1093/jnci/djz177.

Full text
Abstract:
Abstract Background Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. Methods We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40–84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. Results Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. Conclusions In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Breast cancer prediction"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Jerevall, 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.

Full text
Abstract:
A major issue in the management of breast cancer is to identify patients who are less likely to be cured after primary treatment and would benefit from adjuvant chemotherapy. Of great importance is also identification of patients with only local disease who traditionally would be given chemotherapy but would survive without. In this thesis we have validated the utility of the two-gene ratio HOXB13:IL17BR, which previously has been demonstrated to predict disease-free survival in tamoxifen-treated breast cancer patients. We have also studied the prognostic and predictive utility of a single gene as a biomarker in breast cancer medicine. We could confirm that HOXB13:IL17BR may classify patients with different treatment benefit; only patients with a low value showed benefit from prolonged duration of tamoxifen therapy, whereas for the group with high ratios, the long-term recurrence rate did not improve with longer treatment duration. The combination of HOXB13:IL17BR and the molecular grade index (MGI), another prognostic marker, has been shown to outperform either alone in predicting risk of breast cancer recurrence. We validated the prognostic utility of HOXB13:IL17BR+MGI in a large randomized patient cohort and found that this risk classification identified more than 50% of the tamoxifen-treated lymph node-negative patients as having a less than 3% risk of distant recurrence and breast cancer death. Furthermore, we developed and tested a continuous risk model of HOXB13:IL17BR+MGI called Breast Cancer Index (BCI), for estimation of recurrence risk at the individual level. Our study shows that BCI has the ability to identify more than 50% of patients with a low risk of recurrence more accurately than using traditional risk assessment. These results suggest that BCI may help clinicians to make better informed treatment decisions and spare toxic chemotherapy for a large group of breast cancer patients. The protein expression of HOXB13 was also shown to be a valuable predictor in postmenopausal patients. High expression was associated with worse outcome after tamoxifen therapy. In a premenopausal cohort, patients with hormone receptor-positive tumors showed benefit from tamoxifen regardless of HOXB13 expression. Further analysis indicated that estrogen receptor β (ERβ) modified the performance of HOXB13 as a predictor of treatment effect and should be taken into account when identifying patients less likely to respond to the therapy given. In conclusion, BCI identifies patients with a very low risk of distant recurrence. It may be utilized in the management of breast cancer patients to optimize the use of chemotherapy. HOXB13 protein expression may be used as a marker for tamoxifen benefit, but its performance in premenopausal patients might be modified by ERβ.
APA, Harvard, Vancouver, ISO, and other styles
4

Oh, 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.

Full text
Abstract:
Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.
Title 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.
APA, Harvard, Vancouver, ISO, and other styles
5

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/.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mojarad, 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.

Full text
Abstract:
Axillary lymph node (ALN) metastasis status is an important prognostic marker in breast cancer and is widely employed for tumour staging and defining an adjuvant therapy. In an attempt to avoid invasive procedures which are currently employed for the diagnosis of nodal metastasis, several markers have been identified and tested for the prediction of ALN metastasis status in recent years. However, the nonlinear and complex relationship between these markers and nodal status has inhibited the effectiveness of conventional statistical methods as classification tools for diagnosing metastasis to ALNs. The aim of this study is to propose a reliable artificial neural network (ANN) based decision support system for ALN metastasis status prediction. ANNs have been chosen in this study for their special characteristics including nonlinear modelling, robustness to inter-class variability and having adaptable weights which makes them suitable for data driven analysis without making any prior assumptions about the underlying data distributions. To achieve this aim, the probabilistic neural network (PNN) evaluated with the .632 bootstrap is investigated and proposed as an effective and reliable tool for prediction of ALN metastasis. For this purpose, results are compared with the multilayer perceptron (MLP) neural network and two network evaluation methods: holdout and cross validation (CV). A set of six markers have been identified and analysed in detail for this purpose. These markers include tumour size, oestrogen receptor (ER), progesterone receptor (PR), p53, Ki-67 and age. The outcome of each patient is defined as metastasis or non-metastasis, diagnosed by surgery. This study makes three contributions: firstly it suggests the application of the PNN as a classifier for predicting the ALN metastasis, secondly it proposes a the .632 bootstrap evaluation of the ANN outcome, as a reliable tool for the purpose of ALN status prediction, and thirdly it proposes a novel set of markers for accurately predicting the state of nodal metastasis in breast cancer. Results reveal that PNN provides better sensitivity, specificity and accuracy in most marker combinations compared to MLP. The results of evaluation methods’ comparison demonstrate the high variability and the existence of outliers when using the holdout and 5-fold CV methods. This variability is reduced when using the .632 bootstrap. The best prediction accuracy, obtained by combining ER, p53, Ki-67 and age was 69% while tumour size and p53 were the most significant individual markers. The classification accuracy of this panel of markers emphasises their potential for predicting nodal spread in individual patients. This approach could significantly reduce the need for invasive procedures, and reduce post-operative stress and morbidity. Moreover, it can reduce the time lag between investigation and decision making in patient management.
APA, Harvard, Vancouver, ISO, and other styles
7

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

Full text
Abstract:
Introducció: S'ha demostrat que el cribratge mamogràfic redueix la mortalitat per càncer de mama. Seguint les recomanacions de la Comissió Europea, els països europeus han establert programes poblacionals de cribratge que ofereixen mamografies biennals a dones d'entre 50 i 69 anys d'edat. No obstant això, el cribratge de càncer de mama no està lliure de controvèrsia ja que existeix un debat en relació a l'equilibri entre la reducció de la mortalitat i els efectes adversos. Per a millorar aquest equilibri, l'evidència científica actual dóna suport al cribratge personalitzat. Els estudis de modelització han demostrat que modificar l'interval de cribratge, la prova de cribratge o el rang d'edat de la població objectiu en funció del risc individual de les dones produeix un major benefici que les estratègies convencionals. Per tant, és necessari ampliar la informació actual sobre els factors de risc d'aquesta malaltia i crear models de predicció del risc individual mitjançant l'anàlisi de grans bases de dades poblacionals. Objectiu: L'objectiu general d'aquesta tesi és aprofundir en l'anàlisi del cribratge poblacional del càncer de mama. En concret, aquesta tesi pretén avaluar diferents factors de risc de càncer de mama per a desenvolupar i validar un model de predicció de risc individual d'aquesta malaltia. Es va analitzar com la densitat mamària afecta als diferents indicadors del cribratge en el context de la mamografia digital. A continuació, es van avaluar les diferències en el risc de càncer de mama en funció de si una lesió benigna de mama es va diagnosticar en un cribratge prevalent o un cribratge incident. També es va analitzar la interacció entre la densitat mamària i les lesions benignes en el risc de desenvolupar càncer de mama. Posteriorment, es va realitzar una revisió sistemàtica per a actualitzar l'evidència existent, dur a terme una valoració crítica i una avaluació del risc de biaix i resumir els resultats dels models de risc individualitzats que s'utilitzen per a estimar el risc de càncer de mama en les dones de la població general. Finalment, es va dissenyar un model de predicció individual del risc de càncer de mama i es va validar internament, a partir d'informació fàcilment accessible en un episodi de cribratge. Conclusions: i) Els diferents indicadors de cribratge es veuen afectats negativament per la densitat mamària, disminuint la sensibilitat i el valor predictiu positiu de la prova a mesura que augmenta la densitat mamària. ii) El risc de càncer de mama conferit per una lesió benigna difereix segons la mena de cribratge (prevalent o incident). Fins on sabem, aquest és el primer estudi que analitza l'impacte del tipus de cribratge en el pronòstic de la lesió benigna. iii) El risc de càncer de mama augmenta de manera independent amb la presència d'una lesió benigna i amb una major densitat mamària i es manté elevat durant més de 15 anys. iv) Els models de predicció són eines prometedores per a implementar polítiques de cribratge basades en el risc individualitzat. No obstant això, és un repte recomanar qualsevol d'ells per a la personalització del cribratge ja que necessiten millorar la seva qualitat i capacitat discriminatòria. v) Es va dissenyar i validar internament un model de predicció de risc capaç d'estimar el risc de càncer de mama a curt i llarg termini utilitzant la informació recollida de manera rutinària en el cribratge mamogràfic. El model inclou edat, antecedents familiars de càncer de mama, antecedents de lesió benigna i patrons mamogràfics previs, que van resultar estar relacionats amb un augment del risc de càncer de mama. El model ha de ser validat externament i actualitzat amb noves variables.
Introducción: Se ha demostrado que el cribado mamográfico reduce la mortalidad por cáncer de mama. Siguiendo las recomendaciones de la Comisión Europea, los países europeos han establecido programas poblacionales de cribado que ofrecen mamografías bienales a mujeres de entre 50 y 69 años de edad. Sin embargo, el cribado de cáncer de mama no está libre de controversia ya que existe un debate en cuanto al equilibrio entre la reducción de la mortalidad y los efectos adversos. Para mejorar este equilibrio, la evidencia científica actual apoya el cribado personalizado. Los estudios de modelización han demostrado que modificar el intervalo de cribado, la prueba de cribado o el rango de edad de la población objetivo en función del riesgo individual de las mujeres produce un mayor beneficio que las estrategias convencionales. Por lo tanto, es necesario ampliar la información actual sobre los factores de riesgo de esta enfermedad y crear modelos de predicción del riesgo individual mediante el análisis de grandes bases de datos poblacionales. Objetivo: El objetivo general de esta tesis es profundizar en el análisis del cribado poblacional del cáncer de mama. En concreto, esta tesis pretende evaluar diferentes factores de riesgo de cáncer de mama para desarrollar y validar un modelo de predicción de riesgo individual de esta enfermedad. Se analizó cómo la densidad mamaria afecta a los distintos indicadores de cribado en el contexto de la mamografía digital. A continuación, se evaluaron las diferencias en el riesgo de cáncer de mama en función de si una lesión benigna de mama se diagnosticó en un cribado prevalente o un cribado incidente. También se analizó la interacción entre la densidad mamaria y las lesiones benignas en el riesgo de cáncer de mama. Posteriormente, se realizó una revisión sistemática para actualizar la evidencia existente, llevar a cabo una valoración crítica y una evaluación del riesgo de sesgo y resumir los resultados de los modelos de riesgo individualizados que se utilizan para estimar el riesgo de cáncer de mama en las mujeres de la población general. Por último, se diseñó un modelo de predicción individual del riesgo de cáncer de mama y se validó internamente, basado en información fácilmente accesible en un episodio de cribado. Conclusiones: i) Los distintos indicadores de cribado se ven afectados negativamente por la densidad mamaria, disminuyendo la sensibilidad y el valor predictivo positivo de la prueba a medida que aumenta la densidad mamaria. ii) El riesgo de cáncer de mama conferido por una lesión benigna difiere según el tipo de cribado (prevalente o incidente). Hasta donde sabemos, este es el primer estudio que analiza el impacto del tipo de cribado en el pronóstico de la lesión benigna. iii) El riesgo de cáncer de mama aumenta de forma independiente con la presencia de una lesión benigna y con una mayor densidad mamaria y se mantiene elevado durante más de 15 años. iv) Los modelos de predicción son herramientas prometedoras para implementar políticas de cribado basadas en el riesgo individualizado. Sin embargo, es un reto recomendar cualquiera de ellos para la personalización del cribado ya que necesitan mejorar su calidad y capacidad discriminatoria. v) Diseñamos y validamos internamente un modelo de predicción de riesgo capaz de estimar el riesgo de cáncer de mama a corto y largo plazo utilizando la información recogida de forma rutinaria en el cribado mamográfico. El modelo incluye edad, antecedentes familiares de cáncer de mama, antecedentes de lesión benigna y patrones mamográficos previos, que resultaron estar relacionados con un aumento del riesgo de cáncer de mama. El modelo debe ser validado externamente y actualizado con nuevas variables.
Background: Mammographic screening has been shown to reduce mortality from breast cancer. Following the recommendations of the European Council, European countries have started population-based screening programs that offer biennial mammograms to women aged between 50 and 69 years. The results of the effectiveness of population-based screening are controversial in terms of the balance between mortality reduction and adverse effects. To improve this balance, current evidence supports personalized screening. Modeling studies have shown that modifying the screening interval, screening modality, or age range of the target population based on women's individual risk yields a greater benefit than conventional standard strategies. Several risk models have been designed to estimate women's individual breast cancer risk based on their personal characteristics. However, most of these models have not been specifically developed to estimate the risk of women targeted for breast cancer screening. There is therefore a need to broaden current information on risk factors for breast cancer and the estimation of individual risk prediction models through the analysis of large population-based databases. Aims: The general objective of the thesis is to deepen the analysis of population-based breast cancer screening. Specifically, the aim of this thesis is to assess different breast cancer risk factors in order to develop and validate an individualized breast cancer risk prediction model. We evaluated how breast density affects screening performance indicators in a digital mammography context. Then, we assessed differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens. To our knowledge, this is the first time that such an approach has been used. We also evaluated the interaction between breast density and benign breast disease. Subsequently, we performed a systematic review to update the existing evidence, conduct a critical appraisal and risk of bias assessment and summarize the results of the individualized risk models that are used to estimate the risk of breast cancer in women in the general population. Finally, a breast cancer risk prediction model was designed and internally validated, based on information easily accessible at screening. Conclusions: i) Performance screening measures are negatively affected by breast density, with sensitivity and positive predictive value decreasing as breast density increases. ii) The risk of breast cancer conferred by benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyze the impact of screening type on the prognosis of benign breast disease. iii) The risk of breast cancer independently increased with the presence of benign breast disease and with greater breast density and remained elevated for over 15 years. iv) Individualized risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity. v) We designed and internally validated a risk prediction model able to estimate short- and long-term breast cancer risk using information routinely reported at screening participation. The model included age, family history of breast cancer, benign breast disease and previous mammographic findings, which were found to be related to an increase in breast cancer risk. The model should be externally validated and updated with new variables.
Universitat Autònoma de Barcelona. Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública
APA, Harvard, Vancouver, ISO, and other styles
8

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/.

Full text
Abstract:
Different forms of cancer have been widely studied and documented in various studies across the world. However, there have not been many similar studies in the developing countries - particularly those on the African continent (Parkin, et al., 2005). This thesis seeks to uncover the geo-demographic occurrence patterns of the disease by applying three Data mining Techniques, namely Logistic Regression (LR), Neural Networks (NNs) and Decision Trees (DTs), to learn the underlying rules in the overall behaviour of breast cancer. The data, 3,057 observations on 29 variables obtained from four cancer treatment centres in Libya (2004-2008), were interrogated using multiple K-folds cross validation. The predictive strategy yielded a list of breast cancer predictor factors ordered according to their importance in predicting the disease. Comparison between our results and those obtainable from conventional LR, NN and DT models shows that our strategy out-performs the conventional variable selection. It is expected that the findings from this thesis will provide an input into comparative geo-ethnic studies of cancer and provide informed intervention guidelines in the prevention and cure of the disease, not only in Libya but also in other parts of the world.
APA, Harvard, Vancouver, ISO, and other styles
9

Kadra, Gais. "Prediction of therapeutic response to paclitaxel, docetaxel and ixabepilone in breast cancer." Thesis, Aix-Marseille 2, 2011. http://www.theses.fr/2011AIX20702.

Full text
Abstract:
L'objectif de cette thèse est d'étudier la sensibilité des lignes cellulaires du cancer du sein BTCL aux agents stabilisants des microtubules (taxanes et ixabépilone) afin de: 1 - identifier la pharmaco-génomique prédictif de la réponse (résistance / sensibilité) comme une signature moleculaire, et de valider cette signature sur d'autres études dont les données génomiques sont disponibles en ligne, donc mis l'expression des gènes prédictifs de GES pour Tax- sensibilité (333 gènes ) et Ixa-sensibilité (79 gènes) ont été définis, et les Taxanes prédicateurs GES a considérablement prédit Pac-sensibilité dans BTCL, et pathologiques réponse complète à base de Pac-chimiothérapie néoadjuvante chez les patients du cancer du sein. 2 - étudier le rôle des cellules souches du cancer (ALDH +) sur la réponse thérapeutique aux Taxanes et donc, Nous identifions quatre lignes BTCL qui présentent un enrichissement significative dans le pourcentage et le nombre absolu de ALDELFUOR cellules positives dans chacun de ces quatre BTCLs après 5 jours de traitement par le paclitaxel, en contraste avec les résultats précédents, nous avons constaté que dans ces autres 3 BTCLs le phénomène est inversé avec la diminution significative du pourcentage et le nombre absolu de cellules positives ALDELFUOR trouve dans chacun de ces trois BTCLs après 5 jours du traitement par le paclitaxel. Une signature moléculaire de SCC résistant / sensible de 243 pb avec 179 gènes dont 152 gènes sont régulés à la hausse et 27 gènes régulés à la baisse au CSC résistantes au paclitaxel, une sorte prédicteurs génomiques pour Tax - sensibilité au CSC résistantes au paclitaxel peut être dérivée à partir BTCL et peut être utile pour mieux comprendre les mécanismes de résistance aux taxanes et de l'implication de la CSC dans cette résistance, afin de mieux sélectionner des traitements cytotoxiques chez les patients du cancer du sein et l'identification des d'autres marqueurs potentiels de thérapies ciblées dans l'avenir. 3 - Nous avons testé l'impact de l'altération des paramètres génomiques et protéiques ou les mutations de certains gènes comme tau (MAPT), K-alpha tubuline (TUB A1B) tubuline alpha-6 (A1C TUB) tubuline beta 3 (TUBB3) et stathmine (STMN1), malheureusement nous n'avions jamais identifier une mutation pour être corrélée à la réponse des BTCL aux Taxanes. 4 - Nous essayons d'étudier au niveau de protéines par immunohistochimie sur le tissu de micro-array et cyto-micro-array pour certains paramètres qui ont été déjà prouvé (in vitro) pour être corrélée à la réponse aux Taxanes, (cette partie est en fait en cours)
The 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)
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
This research study investigated the association of microRNA related single nucleotide polymorphisms (miRSNPs) with breast cancer susceptibility in Australian Caucasian women. The thesis then progressed with developing an in silico methodology for miRNA-target identification followed by the validation of miRNA-target relationships regarding the distinctive molecular subtypes of human breast cancers.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Breast cancer prediction"

1

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

M, Thompson Alastair, ed. Prognostic and predictive factors in breast cancer. 2nd ed. London: Informa Healthcare, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Prognostic and predictive factors in breast cancer. London: Martin Dunitz, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Noorani, Hussein Zafer. Predictive genetic testing for breast and prostate cancer. Ottawa, Ont: Canadian Coordinating Office for Health Technology Assessment, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Giampietro, Gasparini, and Hayes Daniel 1951-, eds. Biomarkers in breast cancer: Molecular diagnostics for predicting and monitoring therapeutic effect. Totowa, N.J: Humana Press, 2006.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Henderson, Mary G. Predicting costs of hospitalization for cancer care: Final report to HCFA : a DRG-based casemix for cancer care. [Waltham, Mass., etc.?: Bigel Institute for Health Policy, etc.?], 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Cassidy, Jim, Donald Bissett, Roy A. J. Spence OBE, Roy A. J. Spence OBE, Miranda Payne, and Gareth Morris-Stiff. Biomarkers and cancer. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199689842.003.0040.

Full text
Abstract:
Biomarkers and cancer defines these markers and outlines their role in diagnosis, prognosis, prediction of response, and response assessment of a variety of cancers. Established biomarkers are reviewed, and the potential for development of new biomarkers offered by the dramatic progress in both the understanding of molecular biology and the development of laboratory techniques is emphasised. The field of signal transduction has already proved fruitful, with identification of markers allowing successful targeted therapy in a range of cancers. Progress is anticipated also in tumour imaging, with developments in both MRI and PET. Areas of clinical interest are summarised for breast, lung, colorectal, renal, and CNS malignancies.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Breast cancer prediction"

1

Singh, Sonam Jawahar, Ramanathan Rajaraman, and Tanmay Tulsidas Verlekar. "Breast Cancer Prediction Using Auto-Encoders." In Data Management, Analytics and Innovation, 121–32. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Nguyen, Anvy, Jennifer K. Plichta, Jessica Cintolo-Gonzalez, Kinyas Kartal, Molly Elizabeth Griffin, and Kevin Hughes. "Genetic Risk Prediction in Breast Cancer." In Changing Paradigms in the Management of Breast Cancer, 217–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60336-0_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Harbeck, Nadia. "Breast Cancer Molecular Testing for Prognosis and Prediction." In Management of Breast Diseases, 195–202. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46356-8_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Carley, Helena, and Anju Kulkarni. "Hereditary Breast Cancer Genetics and Risk Prediction Techniques." In Breast Cancer Management for Surgeons, 43–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56673-3_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Keller, Brad M., Emily F. Conant, Huen Oh, and Despina Kontos. "Breast Cancer Risk Prediction via Area and Volumetric Estimates of Breast Density." In Breast Imaging, 236–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Baum, M. "Prognosis and Prediction for Early Breast Cancer." In Adjuvant Therapy of Breast Cancer IV, 89–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84745-5_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Coopey, Suzanne B., and Kevin S. Hughes. "Breast Cancer Risk Prediction in Women with Atypical Breast Lesions." In Atypical Breast Proliferative Lesions and Benign Breast Disease, 103–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92657-5_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Prateek. "Breast Cancer Prediction: Importance of Feature Selection." In Advances in Intelligent Systems and Computing, 733–42. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6861-5_62.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Valencia-Moreno, José Manuel, Everardo Gutiérrez López, José Felipe Ramírez Pérez, Juan Pedro Febles Rodríguez, and Omar Álvarez Xochihua. "Exploring Breast Cancer Prediction for Cuban Women." In Advances in Intelligent Systems and Computing, 480–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40690-5_47.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jantre, Shrutika, and Prakash M. Mainkar. "Breast Cancer Prediction Using Machine Learning Techniques." In Smart Intelligent Computing and Applications, Volume 2, 355–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9705-0_36.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Breast cancer prediction"

1

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

Full text
Abstract:
Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an efficient process for prediction. The best overall accuracy for breast cancer detection is achieved equal to 94%. using Gaussian Naive Bayes.
APA, Harvard, Vancouver, ISO, and other styles
2

Sontrop, H., W. Verhaegh, Rene van den Ham, M. Reinders, and P. Moerland. "Subtype specific breast cancer event prediction." In 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2010. http://dx.doi.org/10.1109/gensips.2010.5719684.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Octaviani, T. L., and Z. Rustam. "Random forest for breast cancer prediction." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132477.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yarabarla, Mamatha Sai, Lakshmi Kavya Ravi, and A. Sivasangari. "Breast Cancer Prediction via Machine Learning." In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. http://dx.doi.org/10.1109/icoei.2019.8862533.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tee, Ir Cath, and Ali H. Gazala. "A novel breast cancer prediction system." In 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2011. http://dx.doi.org/10.1109/inista.2011.5946170.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Shanmugasundaram, G., S. Balaji, R. Saravanan, V. Malarselvam, and S. Yazhini. "SYSTEMATIC ANALYSIS ON BREAST CANCER PREDICTION." In 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2018. http://dx.doi.org/10.1109/icscan.2018.8541239.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Aili, Lisa X. Xu, George A. Sandison, and Jiayao Zhang. "A Microscale Model for Prediction of Breast Cancer Cell Damage During Cryosurgery." In ASME 2003 Heat Transfer Summer Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/ht2003-47110.

Full text
Abstract:
The morphology of most ductal carcinoma is characterized by tightly packed groups of small malignant cells. This special structure can make these breast cancer cells have different osmotic responses to freezing and affect the probability of damage from cellular dehydration and intracellular ice formation. A mathematical model has been developed to study the microscale damage of the breast cancer cells during cryosurgery by taking its unique structure into consideration. The model was built based on a spherical unit comprised of an extracellular region that surrounds several layers of cancer cells, as experimentally observed by other researchers [13]. In this model, cell to cell contact and water transportation were both taken into consideration. Temperature transients in the breast cancer undergoing cryosurgery were calculated numerically using the Pennes equation. When subjected to various types of thermal histories, both cell dehydration and intracellular ice formation in the unit structure were examined at the microscale level using the model developed in this study. It was found that the cells in the inner layers hardly dehydrated while those in the outermost layer did greatly. The results were used to explain the experimental phenomena observed in freezing of breast cancer tissues that intracellular ice formation existed even at the slow cooling rate of −3°C/min [13]. In the attempt to better define an optimal freeze-thaw cryosurgery procedure for breast cancer, both serious dehydration and intracellular ice formation (IIF) need to be considered. This study also found that use of constant heat flux is able to induce greater dehydration and higher IIF probability simultaneously. It is recommended that a constant heat flux protocol should be used in cryosurgery to ensure better treatment results.
APA, Harvard, Vancouver, ISO, and other styles
8

Gioia, Sandra, Renata Galdino, Lucia Brigagão, Cristiane Torres, Sandra San Miguel, Lindsay Krush, and Paul Goss. "PREDICTION OF ATTENDANCE TO THE “60 DAYS LAW” WITHIN THE PATIENT NAVIGATION PROGRAM TO BREAST CANCER IN RIO DE JANEIRO." In Brazilian Breast Cancer Symposium. v29s1, 2019. http://dx.doi.org/10.29289/259453942019v29s1ep04.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Pawluczyk, Olga, Martin J. Yaffe, Norman F. Boyd, and Roberta A. Jong. "Estimation of volumetric breast density for breast cancer risk prediction." In Medical Imaging 2000, edited by James T. Dobbins III and John M. Boone. SPIE, 2000. http://dx.doi.org/10.1117/12.384495.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Uyar, Kaan, Umit Ilhan, Ahmet Ilhan, and Erkut Inan Iseri. "Breast Cancer Prediction Using Neuro-Fuzzy Systems." In 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2020. http://dx.doi.org/10.1109/iceee49618.2020.9102476.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Breast cancer prediction"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wang, Ying yuan, Zechang Chen, Luxin Zhang, Shuangyi Chen, Zhuomiao Ye, Tingting Xu, and Yingying Zhang c. A systematic review and network meta-analysis: Role of SNPs in predicting breast carcinoma risk. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0092.

Full text
Abstract:
Review question / Objective: P: Breast cancer patient; I: Single nucleotide polymorphisms associated with breast cancer risk; C: Healthy person; O: By comparing the proportion of SNP mutations in the tumor group and the control group, the effect of BREAST cancer risk-related SNP was investigated; S: Case-control study. Condition being studied: Breast cancer (BC) is one of the most common cancers among women, and its morbidity and mortality have continued to increase worldwide in recent years, reflecting the strong invasiveness and metastasis characteristics of this cancer. BC is a complex disease that involves a sequence of genetic, epigenetic, and phenotypic changes. Polymorphisms of genes involved in multiple biological pathways have been identified as potential risks of BC. These genetic polymorphisms further lead to differences in disease susceptibility and severity among individuals. The development of accurate molecular diagnoses and biological indicators of prognosis are crucial for individualized and precise treatment of BC patients.
APA, Harvard, Vancouver, ISO, and other styles
8

Chang, Jenny. To Ascertain Distinctive Gene Expression Patterns for the Prediction of Docetaxel (Taxotere Chemosensitivity or Chemoresistance in Human Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada430707.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Buckley, Jonathan D. Predicting Time-to-Relapse in Breast Cancer Using Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada300396.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hudachek, Susan F. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574076.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Breast cancer prediction' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography