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Books on the topic 'Breast cancer prediction'

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

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1

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

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2

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

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3

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

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4

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

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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.

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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.

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7

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

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8

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

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9

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

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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.

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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.
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11

Breast cancer screening. New York: Chapman & Hall, 1997.

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12

Lee, Christoph I. Breast MRI for Women with Recently Diagnosed Breast Cancer. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0041.

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This chapter, found in the cancer screening and management section of the book, provides a succinct synopsis of a key study examining the efficacy of breast magnetic resonance imaging for extent of disease evaluation among women with recently diagnosed breast cancer. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. The study showed that breast MRI in newly diagnosed unilateral breast cancer patients can detect contralateral early-stage breast cancers missed by mammography and clinical breast examination. In addition, breast MRI has an extremely high negative predictive value, and may provide helpful information to women and physicians weighing the relative value of prophylactic contralateral mastectomy in the setting of unilateral breast cancer. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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13

Walker, Rosemary A., and Alistair M. Thompson. Prognostic and Predictive Factors in Breast Cancer. Taylor & Francis Group, 2019.

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14

Walker, Rosemary A., and Alistair M. Thompson. Prognostic and Predictive Factors in Breast Cancer. Taylor & Francis Group, 2008.

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15

Walker, Rosemary A., and Alistair M. Thompson. Prognostic and Predictive Factors in Breast Cancer. Taylor & Francis Group, 2008.

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16

Walker, Rosemary A. Prognostic and Predictive Factors in Breast Cancer. Informa Healthcare, 2003.

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17

Shao, Zhi-Ming, Yi-Zhou Jiang, Zhijie Jason Liu, and Shengtao Zhou, eds. Predictive, Prognostic Biomarkers and Therapeutic Targets in Breast Cancer. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-83250-101-6.

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18

Prat, Aleix, and Mothaffar Rimawi, eds. New Approaches to Classification and Diagnostic Prediction of Breast Cancers. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-796-6.

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19

Durand, Melissa A. Architectural Distortion (Cancer). Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0029.

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An architectural distortion (AD) is an alteration of the breast parenchyma, which results in radiating lines or spicules emanating from a point without a distinct mass. It can occur as the primary finding, or it may be an associated feature of a mass, asymmetry, or calcifications. AD is a mammographic finding with a high positive predictive value for malignancy and is a major cause of false-negative screening exams. This chapter, appearing in the section on asymmetry, mass, and distortion, reviews the key imaging and clinical features, imaging protocols, differential diagnoses, management recommendations, and potential pitfalls for a malignant architectural distortion. Topics discussed include superimposition of breast tissue, localization, workup of tomosynthesis-detected architectural distortion, and image-guided biopsy options.
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20

Zhou, Shengtao, Zhi-Ming Shao, Zhijie Jason Liu, and Yi-Zhou Jiang, eds. Predictive, Prognostic Biomarkers and Therapeutic Targets in Triple Negative Breast Cancer. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-746-0.

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21

Canzonieri, Vincenzo, and Massimiliano Berretta. Prognostic and Predictive Response Therapy Factors in Cancer Disease (Colorectal, Breast, Liver, Lung, Gastric, Renal and Prostate Cancers). Nova Science Publishers, Incorporated, 2015.

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22

Biomarkers in breast cancer: Molecular diagnostics for predicting and monitoring therapeutic effect. Totowa, N.J: Humana Press, 2006.

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23

Toi, Masakazu, and San-Gang Wu, eds. Advancements in Metastatic Breast Cancer: Predictive and Prognostic Biomarkers, and Molecular Mechanisms. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-83250-085-9.

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24

Lynda, McGahan, and Canadian Coordinating Office for Health Technology Assessment, eds. BRCA1 and BRCA2 predictive genetic testing for breast and ovarian cancers: A systematic review of clinical evidence. Ottawa: CCOHTA, 2006.

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25

D’Orsi, Carl. Breast Imaging Reporting and Data System (BI-RADS). Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0005.

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This chapter, devoted to the Breast Imaging Reporting and Data System (BI-RADS), describes the standardized language applied to findings in mammography, breast ultrasound, and breast MRI. BI-RADS terms most frequently used are described, and most are illustrated by figures. In addition, the rules for a facility and radiologist audit are described, with definitions of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) findings. Sensitivity (Se), specificity (Sp), positive predictive values 1, 2, and 3 (PPV1, 2, 3), and cancer detection rate are defined. An example of an audit is provided to clarify the use of these metrics.
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26

Lee, Christoph I. Efficacy of Screening Breast MRI for High-Risk Women. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0039.

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This chapter, found in the cancer screening and management section of the book, provides a succinct synopsis of a key study examining the efficacy of screening breast magnetic resonance imaging (MRI) for high-risk women. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. Breast MRI screening has higher sensitivity than mammography for high-risk women, but both the specificity and positive predictive value are lower than for mammography screening. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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27

Krist, Alex H., and Vivian Jiang. Provider-Level Factors Influencing Implementation. Edited by David A. Chambers, Wynne E. Norton, and Cynthia A. Vinson. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190647421.003.0016.

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Cancer treatment is increasingly complex. The tools for diagnosis, staging, and predicting prognosis are rapidly evolving, as are the therapies, treatment modalities, and treatment protocols. The complexity of care, the need for a multidisciplinary team across settings, and patient-level factors all present providers with a unique set of challenges. The three case studies presented in this chapter explore strategies that help providers by (1) ensuring low-income patients with breast cancer receive care consistent with guidelines through patient engagement and navigation, (2) promoting and incorporating the routine use of shared decision-making in determining prostate cancer treatment, and (3) supporting the adoption of concurrent palliative care for patients with advanced cancer. The specific challenges and needs for future implementation science are highlighted throughout each case.
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