Predicting Breast Cancer Survival byMulti-disciplinary Attributes
| Autor: | Sheng-Lin Wang, 王聖麟 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Background: Prognosis of breast cancer has been well documented but epidemiological and clinical profiles have been updated due to early detection and the advent of new therapy and treatments guided by new molecular biomarkers and imaging techniques. Aims: We aimed to develop a risk prediction model based on a constellation of mammographic appearances, molecular biomarkers, and clinical tumour attributes in order to classify different risk profiles of breast cancer. Data and Methods Data Sources: A retrospective cohort was designed by enrolling 2540 patients diagnosed with invasive breast cancer at Falun Central Hospital of Dalarna County between year 1996 to 2014 with information on the three main disciplines of predictors of conventional tumour attributes, expression of hormonal receptors, and mammographic appearance. Among the study population, 251 event of breast cancer death were ascertained till the year 2015. Factors associated with breast cancer survival such as modality of therapy and detection mode were also collected. Statistical Methods: The time-to-event design in conjunction with the cured model for the building of the prediction of the breast cancer survival and the cure rate was applied. The effect of each discipline of breast cancer predictors on the risk of breast cancer death were first evaluated separately. In addition to the main effect, their interactive influence on breast cancer survival, focusing on the expression of hormonal receptors and mammographic appearance were also assessed based on the multivariate accelerated failure time model. Based on the joint results of the assessment on each discipline of predictors, a multi-disciplinary prediction model for the risk of breast cancer survival was constructed. With the consideration of both cured and survival probability, a cured model with Bayesian approach was developed to predict 30-yer survival of breast cancer to provide risk stratification of breast cancer. Results: As far as mammographic appearances are concerned, powdery and crushed had a very low rate of death, suggesting high possibility of over-diagnosis, whereas casting type and architectural distortion has a higher death rate probably requiring aggressive treatment and therapies. Regarding molecular biomarkers, luminal A and B with low grade had lower death rate suggesting high possibility of over-diagnosis, but basal-like phenotype or triple negative breast cancer had a higher death rate probably requiring aggressive treatment and therapies. We also estimated 55.7% breast cancer with completely cured after initial treatment therapy or overdiagnosis. The cured rate was highest for powdery (66%), followed by circular (62%), crushed-stone (58%), stellate (56%). The cure rate was very low for casting type and architecture distortion (19%). The cured probability for the molecular phenotype of luminal A (56%), luminal B (46%), and HER-2 (56%)were higher compared with basal phenotype (29%) and triple negative (31%). Conclusion: We developed a risk prediction model for breast cancer by using multidisciplinary factors. Such a risk prediction model is not only useful for the identification of high risk group (casting type /architecture and basal phenotype) so as to provide adequate intensive surveillance aggressive medical regime but also for the identification of low risk (powdery and crushed stone and luminal A and B with low grade) in order to avoid unnecessary surveillance and treatment and therapy. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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