1.Survival Benefit of Tamoxifen in Estrogen Receptor-Negative and Progesterone Receptor-Positive Low Grade Breast Cancer Patients.
Li Heng YANG ; Hsin Shun TSENG ; Che LIN ; Li Sheng CHEN ; Shou Tung CHEN ; Shou Jen KUO ; Dar Ren CHEN
Journal of Breast Cancer 2012;15(3):288-295
PURPOSE: This study aimed to analyze the efficacy and prognostic significance of adjuvant tamoxifen in breast cancer patients with various hormone receptor statuses. METHODS: Typically, 1,260 female breast cancer patients were recruited in this study. The correlation between estrogen receptor (ER)/progesterone receptor (PR) phenotypes and clinical characteristics was investigated, and the survival rate was assessed after 5-year follow-up. RESULTS: The 5-year overall survival (85%) was better in women under the age of 50 years. Patients with ER+/PR+ tumors had a better 5-year survival rate (94%); those with ER-/PR- tumors experienced the worst outcome (74% survival rate); whereas single-positive cases were in between. In 97 out of 128 patients with ER-/PR+ tumors, tamoxifen was given as adjuvant hormonal therapy, and it increased the survival benefit in the lower grade group in terms of overall survival and disease-free survival (p=0.01 and p=0.03, respectively). CONCLUSION: For high-grade tumors with ER-/PR+, adjuvant tamoxifen therapy may have no survival benefit, whereas for the patients with low-grade ER-/PR+ tumors, adjuvant tamoxifen therapy is highly suggestive.
Breast
;
Breast Neoplasms
;
Disease-Free Survival
;
Estrogens
;
Female
;
Humans
;
Phenotype
;
Progesterone
;
Receptors, Progesterone
;
Survival Rate
;
Tamoxifen
2.Comparative Analysis of Logistic Regression, Support Vector Machine and Artificial Neural Network for the Differential Diagnosis of Benign and Malignant Solid Breast Tumors by the Use of Three-Dimensional Power Doppler Imaging.
Shou Tung CHEN ; Yi Hsuan HSIAO ; Yu Len HUANG ; Shou Jen KUO ; Hsin Shun TSENG ; Hwa Koon WU ; Dar Ren CHEN
Korean Journal of Radiology 2009;10(5):464-471
OBJECTIVE: Logistic regression analysis (LRA), Support Vector Machine (SVM) and a neural network (NN) are commonly used statistical models in computer-aided diagnostic (CAD) systems for breast ultrasonography (US). The aim of this study was to clarify the diagnostic ability of the use of these statistical models for future applications of CAD systems, such as three-dimensional (3D) power Doppler imaging, vascularity evaluation and the differentiation of a solid mass. MATERIALS AND METHODS: A database that contained 3D power Doppler imaging pairs of non-harmonic and tissue harmonic images for 97 benign and 86 malignant solid tumors was utilized. The virtual organ computer-aided analysis-imaging program was used to analyze the stored volumes of the 183 solid breast tumors. LRA, an SVM and NN were employed in comparative analyses for the characterization of benign and malignant solid breast masses from the database. RESULTS: The values of area under receiver operating characteristic (ROC) curve, referred to as Az values for the use of non-harmonic 3D power Doppler US with LRA, SVM and NN were 0.9341, 0.9185 and 0.9086, respectively. The Az values for the use of harmonic 3D power Doppler US with LRA, SVM and NN were 0.9286, 0.8979 and 0.9009, respectively. The Az values of six ROC curves for the use of LRA, SVM and NN for non-harmonic or harmonic 3D power Doppler imaging were similar. CONCLUSION: The diagnostic performances of these three models (LRA, SVM and NN) are not different as demonstrated by ROC curve analysis. Depending on user emphasis for the use of ROC curve findings, the use of LRA appears to provide better sensitivity as compared to the other statistical models.
Adolescent
;
Adult
;
Aged
;
Aged, 80 and over
;
*Artificial Intelligence
;
Breast Neoplasms/*ultrasonography
;
Diagnosis, Computer-Assisted
;
Diagnosis, Differential
;
Female
;
Humans
;
Image Interpretation, Computer-Assisted
;
Imaging, Three-Dimensional/*statistics & numerical data
;
Logistic Models
;
Middle Aged
;
*Neural Networks (Computer)
;
Predictive Value of Tests
;
ROC Curve
;
Sensitivity and Specificity
;
Ultrasonography, Doppler/*statistics & numerical data
;
Ultrasonography, Mammary/*statistics & numerical data