Diss. (sammanfattning) Stockholm : Karol. inst. / Härtill 5 uppsatser.

by Suk-yee Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 95-107). / Abstract --- p.i / Acknowledgement --- p.ii / Table of Contents --- p.iii / List of Tables --- p.v / List of Figures --- p.vii / List of Appendices --- p.viii / Chapter Chapter I - --- Introduction --- p.1 / Introduction --- p.1 / Epidemiology of Breast Cancer --- p.2 / The Aetiology of Breast Cancer --- p.4 / Prognosis --- p.4 / Effects of Breast Cancer --- p.5 / Management of Breast Cancer --- p.6 / Prevention and Early Detection of Breast Cancer --- p.8 / Theoretical Framework --- p.18 / Chapter Chapter II - --- Method --- p.39 / Research Design --- p.39 / Sample --- p.40 / Measures --- p.41 / Procedure --- p.48 / Data Analysis --- p.49 / Chapter Chapter III - --- Results --- p.50 / Chapter 1 --- Sample Characteristics --- p.50 / Chapter 1.1 --- Demographic profile --- p.50 / Chapter 1.2 --- Medical history and health practices --- p.52 / Chapter 1.3 --- Health status and health values --- p.53 / Chapter 1.4 --- Knowledge of breast cancer --- p.54 / Chapter 1.5 --- Attitudes toward breast cancer --- p.55 / Chapter 2 --- Breast Self-Examination Practices --- p.57 / Chapter 3 --- Social Influence on Breast Self-Examination Practices --- p.60 / Chapter 4 --- Health Belief Model Measures --- p.61 / Chapter 5 --- Comparison of Practicers and Non-practicers --- p.62 / Chapter 6 --- Predictors of breast self-examination practices --- p.67 / Chapter 6.1 --- Practicers vs Non-practicers --- p.67 / Chapter 6.2 --- Frequency of breast self-examination --- p.70 / Chapter 6.3 --- Breast self-examination intention --- p.75 / Chapter Chapter IV - --- Discussion & Conclusions --- p.77 / Discussion --- p.77 / Conclusions --- p.93 / References --- p.95 / Appendices --- p.108

Wang Ya-ping. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 111-120). / CONTENT / ACKNOWLEDGMENT / ABSTRACT / Chapter Chapter 1. --- Introduction / Chapter Chapter 2. --- Review of relevant literature / Chapter 2.1 --- Mammary structure and embryology / Chapter 2.2 --- Pathology of breast cancer / Chapter 2.3 --- Risk factors in breast cancer / Chapter 2.4 --- Prognostic factors in breast cancer / Chapter 2.5 --- Treatment of breast cancer / Chapter 2.6 --- Breast cancer research by flow cytometry / Chapter 2.7 --- c-erbB-2 oncogene research in breast cancer / Chapter 2.8 --- Tables and figures of chapter2 / Chapter Chapter 3. --- Materials and methods / Chapter 3.1 --- Flow cytometry assay of breast cancer tissue / Chapter 3.1.1 --- Sample collection / Chapter 3.1.2 --- Sample preparation for flow cytometric assay / Chapter 3.1.3 --- DNA content assay by flow cytometry / Chapter 3.1.4 --- Solutions for flow cytometric analysis / Chapter 3.2 --- c-erbB-2 and EGF receptor protein detection by immunohistochemical methods / Chapter 3.2.1 --- Preparation of sections / Chapter 3.2.2 --- Methods of staining / Chapter 3.2.3 --- Methods of analysis / Chapter 3.2.4 --- Confirmation of expression of c-erbB-2 protein by immunoblotting method / Chapter 3.2.5 --- Solutions for immunohistochemical and immunoblotting methods / Chapter 3.3 --- Clinical data from 346 breast cancer patients and method of analysis / Chapter 3.4 --- Tables and figures of chapter3 / Chapter Chapter 4. --- Results / Chapter 4.1 --- Results of flow cytometric analysis / Chapter 4.1.1 --- Tumour characteristics of 94 breast cancer patients / Chapter 4.1.2 --- Survival analysis by results of flow cytometry / Chapter 4.2 --- Results of immunohistochemical assay of c-erbB-2 and EGFr / Chapter 4.2.1 --- C-erbB-2 and EGFr expression in breast cancer / Chapter 4.2.2 --- The distribution of c-erbB-2 and EGFr expression in breast cancer / Chapter 4.2.3 --- Analysis of clinical outcome by c-erbB-2 and EGFr expression / Chapter 4.3 --- Clinical data of 346 patients with breast cancer / Chapter 4.3.1 --- Patients' characteristics / Chapter 4.3.2 --- Analysis of clinical data and outcome / Chapter 4.3.3 --- Analysis of clinical outcome according to histopathological characteristics of breast tumour / Chapter 4.3.4 --- Types of operation and clinical outcome / Chapter 4.3.5 --- Postoperative adjuvant therapy and clinical outcome / Chapter 4.3.6 --- Results from statistical analysis by Cox-regression / Chapter 4.4 --- Tables figures of chapter4 / Chapter Chapter 5. --- Discussion and conclusion / Chapter 5.1 --- Flow cytometric analysis of paraffin-embedded breast cancer tissue / Chapter 5.1.1 --- Evaluation of DNA flow cytometric results / Chapter 5.1.2 --- Correlation between tumor DNA aneuploidy or cell subpopulation and clinical outcome / Chapter 5.1.3 --- Correlation between S-phase fraction of breast tumour and clinical outcome / Chapter 5.1.4 --- Observation of high proportion of DNA hypoaneuploidy in this study / Chapter 5.2 --- c-erbB-2 oncogene overexpression in breast cancer / Chapter 5.2.1 --- c-erbB-2 oncoprotein expression in other studies / Chapter 5.2.2 --- Correlation between c-erbB-2 oncoprotein expression status and breast cancer pathogenesis / Chapter 5.3 --- Evaluation of EGFr expression in breast cancer / Chapter 5.4 --- Analysis of clinical data / Chapter 5.4.1 --- Clinical characteristics of patients with breast cancer / Chapter 5.4.2 --- Clinical characteristics of breast cancer and clinical outcome / Chapter 5.4.3 --- Clinical outcome by types of postoperative treatment / Chapter 5.5 --- Prognostic factors / Chapter 5.5.1 --- Our observations in comparison to other studies / Chapter 5.5.2 --- Prognostic factors for clinical application / Chapter 5.6 --- Tables and figures of chapter5 / References:

INTRODUÇÃO: O status axilar é o fator prognóstico mais importante em câncer de mama. A biópsia de linfonodo sentinela (BLS) tornou-se o procedimento padrão no estadiamento axilar em pacientes com axila clinicamente negativa. O procedimento recomendado para pacientes com metástase em linfonodo sentinela (LS) inclui a linfadenectomia axilar completa (LAC). Porém questiona-se a necessidade da LAC em todos os pacientes com metástase em LS (MLS), particularmente naqueles com baixo risco de metástase em linfonodos axilares adicionais (Não-LS). Estimar de forma precisa a probabilidade de MLS e metástase em Não-LS (MNão-LS) pode auxiliar em muito o processo de decisão terapêutica. Os dados publicados referentes aos fatores preditivos de MLS e MNão-LS são de certa forma escassos. A forma de como esses dados são apresentados – geralmente expressos como razão de chance (odds-ratio) – dificulta o cálculo de probabilidade de MLS ou MNão-LS para um paciente em específico. Nesta tese, dois modelos de previsão computadorizados de fácil utilização foram desenvolvidos, utilizando-se um grande número de casos, para facilitar o cálculo de probabilidade de MLS e MNão-LS. MÉTODOS: Todos os dados clínico-patológicos foram coletados do banco de dados prospectivo de LS do Memorial Sloan-Kettering Cancer Center (MSKCC), Nova York, EUA. Os projetos de desenvolvimento de ambos os modelos foram aprovados pelo Institutional Review Board do MSKCC. Dois modelos foram desenvolvidos: Modelo de MLS (Modelo LS) e Modelo de metástase em Não-LS (Modelo Não-LS). No Modelo LS, os achados clínico-patológicos de 4.115 procedimentos subseqüentes de BLS (amostra de modelagem) foram submetidos à análise de regressão logística multivariada para se criar um modelo de previsão de MLS. Um software baseado nesse modelo foi desenvolvido utilizando-se as variáveis: idade, tamanho do tumor, tipo histológico, invasão vásculo-linfática (IVL), localização do tumor e multifocalidade. Esse modelo foi validado em uma amostra distinta (amostra de validação) com 1.792 BLSs subseqüentes. No Modelo Não-LS, os achados patológicos do tumor primário e das MLS, obtidas de 702 procedimentos de BLS (amostra de modelagem) em pacientes submetidos à LAC, foram submetidos à análise de regressão logística multivariada para se criar um modelo de previsão de MNão-LS. Um nomograma e um software baseados nesse modelo foram desenvolvidos utilizando-se as variáveis: tamanho do tumor, tipo histológico, grau nuclear, IVL, multifocalidade, receptor de estrógeno, método de detecção da MLS, número de LS positivos, número de LS negativos. Esse modelo foi validado em uma amostra distinta (amostra de validação) com 373 casos subseqüentes. RESULTADOS: O software do Modelo LS na amostra de modelagem mostrou-se adequado, com a área sob a curva de características operacionais (ROC) de 0,76. Quando aplicado na amostra de validação, o Modelo LS também previu de forma acurada as probabilidades de MLS (ROC = 0,76). O software e o nomograma do Modelo Não-LS na amostra de modelagem apresentaram uma área ob a curva ROC de 0,76 e, na amostra de validação, 0,77. CONCLUSÕES: Dois softwares de fácil utilização foram desenvolvidos, utilizando-se informações comumente disponíveis pelo cirurgião para calcular para o paciente a probabilidade de MLS e MNão-LS de forma precisa, fácil e individualizada. O software do Modelo LS não deve ser utilizado, porém, para se evitar a BLS. Para download dos softawares clique em: <A HREF="http://www.mastologia.com" TARGET="_BLANK">www.mastologia.com . / INTRODUCTION: Axillary lymph node status is the most significant prognostic factor in breast cancer. Sentinel lymph node biopsy (SLNB) has become the standard of care as the axillary staging procedure in clinically node negative patients. The standard procedure for patients with sentinel lymph node (SLN) metastasis includes complete axillary lymph node dissection (ALND). However, many experts question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of additional lymph node (Non-SLN) metastasis. Accurate estimates of the likelihood of SLN metastases and additional disease in the axilla could greatly assist in decision-making treatment. The published data on predictive factors for SLN and Non-SLN metastases is somewhat scarce. It is also difficult to apply these data – usually expressed as odds ratio – to calculate the probability of SLN or Non-SLN metastases for a specific patient. In this thesis, two user-friendly computerized prediction models based on large datasets were developed, to assist the prediction of the presence of SLN and Non-SLN metastases. METHODS: All clinical and pathological data were collected from the prospective SLN database of Memorial Sloan-Kettering Cancer Center (MSKCC), New York, USA. The development projects of both models were approved by the Institutional Review Board of MSKCC. Two models were developed: Model for predicting SLN metastases (SLN Model) and Model for predicting Non-SLN metastases (Non-SLN Model). In the SLN Model, clinical and pathological features of 4,115 sequential SLNB procedures (modeling sample) were assessed with multivariable logistic regression to predict the presence SLN metastases. A software based on the logistic regression model was created using age, tumor size, tumor type, lymphovascular invasion, tumor location and multifocality. This model was subsequently applied to another set of 1,792 sequential SLNBs (validation sample). In the Non-SLN Model, pathological features of the primary tumor and SLN metastases, identified in 702 SLNBs (modeling sample) on patients who underwent complete ALND, were assessed with multivariable logistic regression to predict the presence of additional disease in the Non-SLNs of these patients. A nomogram and a software were created using tumor size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. This model was subsequently applied to another set of sequential 373 procedures (validation sample). RESULTS: The software of the SLN Model for the modeling sample was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the validation sample, the SLN-Model accurately predicted likelihood of SLN metastases (ROC = 0.76). The software and nomogram of the Non-SLN Model was also accurately predicted likelihood of Non-SLN metastases, with an area under ROC curve of 0.76 for the modeling sample, and 0.77 for the validation sample. CONCLUSION: Two user-friendly softwares were developed, using information commonly available to the surgeon to easily and accurately calculate the likelihood of having SLN metastases or additional, Non-SLN metastases for individual patients. However, the software of the SLN Model should not be used to avoid SLNB. Click on <A HREF="http://www.mastologia.com" TARGET="_BLANK">www.mastologia.com to download these softwares.

INTRODUÇÃO: O status axilar é o fator prognóstico mais importante em câncer de mama. A biópsia de linfonodo sentinela (BLS) tornou-se o procedimento padrão no estadiamento axilar em pacientes com axila clinicamente negativa. O procedimento recomendado para pacientes com metástase em linfonodo sentinela (LS) inclui a linfadenectomia axilar completa (LAC). Porém questiona-se a necessidade da LAC em todos os pacientes com metástase em LS (MLS), particularmente naqueles com baixo risco de metástase em linfonodos axilares adicionais (Não-LS). Estimar de forma precisa a probabilidade de MLS e metástase em Não-LS (MNão-LS) pode auxiliar em muito o processo de decisão terapêutica. Os dados publicados referentes aos fatores preditivos de MLS e MNão-LS são de certa forma escassos. A forma de como esses dados são apresentados – geralmente expressos como razão de chance (odds-ratio) – dificulta o cálculo de probabilidade de MLS ou MNão-LS para um paciente em específico. Nesta tese, dois modelos de previsão computadorizados de fácil utilização foram desenvolvidos, utilizando-se um grande número de casos, para facilitar o cálculo de probabilidade de MLS e MNão-LS. MÉTODOS: Todos os dados clínico-patológicos foram coletados do banco de dados prospectivo de LS do Memorial Sloan-Kettering Cancer Center (MSKCC), Nova York, EUA. Os projetos de desenvolvimento de ambos os modelos foram aprovados pelo Institutional Review Board do MSKCC. Dois modelos foram desenvolvidos: Modelo de MLS (Modelo LS) e Modelo de metástase em Não-LS (Modelo Não-LS). No Modelo LS, os achados clínico-patológicos de 4.115 procedimentos subseqüentes de BLS (amostra de modelagem) foram submetidos à análise de regressão logística multivariada para se criar um modelo de previsão de MLS. Um software baseado nesse modelo foi desenvolvido utilizando-se as variáveis: idade, tamanho do tumor, tipo histológico, invasão vásculo-linfática (IVL), localização do tumor e multifocalidade. Esse modelo foi validado em uma amostra distinta (amostra de validação) com 1.792 BLSs subseqüentes. No Modelo Não-LS, os achados patológicos do tumor primário e das MLS, obtidas de 702 procedimentos de BLS (amostra de modelagem) em pacientes submetidos à LAC, foram submetidos à análise de regressão logística multivariada para se criar um modelo de previsão de MNão-LS. Um nomograma e um software baseados nesse modelo foram desenvolvidos utilizando-se as variáveis: tamanho do tumor, tipo histológico, grau nuclear, IVL, multifocalidade, receptor de estrógeno, método de detecção da MLS, número de LS positivos, número de LS negativos. Esse modelo foi validado em uma amostra distinta (amostra de validação) com 373 casos subseqüentes. RESULTADOS: O software do Modelo LS na amostra de modelagem mostrou-se adequado, com a área sob a curva de características operacionais (ROC) de 0,76. Quando aplicado na amostra de validação, o Modelo LS também previu de forma acurada as probabilidades de MLS (ROC = 0,76). O software e o nomograma do Modelo Não-LS na amostra de modelagem apresentaram uma área ob a curva ROC de 0,76 e, na amostra de validação, 0,77. CONCLUSÕES: Dois softwares de fácil utilização foram desenvolvidos, utilizando-se informações comumente disponíveis pelo cirurgião para calcular para o paciente a probabilidade de MLS e MNão-LS de forma precisa, fácil e individualizada. O software do Modelo LS não deve ser utilizado, porém, para se evitar a BLS. Para download dos softawares clique em: <A HREF="http://www.mastologia.com" TARGET="_BLANK">www.mastologia.com . / INTRODUCTION: Axillary lymph node status is the most significant prognostic factor in breast cancer. Sentinel lymph node biopsy (SLNB) has become the standard of care as the axillary staging procedure in clinically node negative patients. The standard procedure for patients with sentinel lymph node (SLN) metastasis includes complete axillary lymph node dissection (ALND). However, many experts question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of additional lymph node (Non-SLN) metastasis. Accurate estimates of the likelihood of SLN metastases and additional disease in the axilla could greatly assist in decision-making treatment. The published data on predictive factors for SLN and Non-SLN metastases is somewhat scarce. It is also difficult to apply these data – usually expressed as odds ratio – to calculate the probability of SLN or Non-SLN metastases for a specific patient. In this thesis, two user-friendly computerized prediction models based on large datasets were developed, to assist the prediction of the presence of SLN and Non-SLN metastases. METHODS: All clinical and pathological data were collected from the prospective SLN database of Memorial Sloan-Kettering Cancer Center (MSKCC), New York, USA. The development projects of both models were approved by the Institutional Review Board of MSKCC. Two models were developed: Model for predicting SLN metastases (SLN Model) and Model for predicting Non-SLN metastases (Non-SLN Model). In the SLN Model, clinical and pathological features of 4,115 sequential SLNB procedures (modeling sample) were assessed with multivariable logistic regression to predict the presence SLN metastases. A software based on the logistic regression model was created using age, tumor size, tumor type, lymphovascular invasion, tumor location and multifocality. This model was subsequently applied to another set of 1,792 sequential SLNBs (validation sample). In the Non-SLN Model, pathological features of the primary tumor and SLN metastases, identified in 702 SLNBs (modeling sample) on patients who underwent complete ALND, were assessed with multivariable logistic regression to predict the presence of additional disease in the Non-SLNs of these patients. A nomogram and a software were created using tumor size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. This model was subsequently applied to another set of sequential 373 procedures (validation sample). RESULTS: The software of the SLN Model for the modeling sample was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the validation sample, the SLN-Model accurately predicted likelihood of SLN metastases (ROC = 0.76). The software and nomogram of the Non-SLN Model was also accurately predicted likelihood of Non-SLN metastases, with an area under ROC curve of 0.76 for the modeling sample, and 0.77 for the validation sample. CONCLUSION: Two user-friendly softwares were developed, using information commonly available to the surgeon to easily and accurately calculate the likelihood of having SLN metastases or additional, Non-SLN metastases for individual patients. However, the software of the SLN Model should not be used to avoid SLNB. Click on <A HREF="http://www.mastologia.com" TARGET="_BLANK">www.mastologia.com to download these softwares.