Personalized Treatment of Breast Cancer (Hardcover, 2016)

Part I Treatment for the Patients Having Breast Cancer High-Risk.- Chapter I Risk-Reducing Surgery for Breast Cancer Patients with BRCA Mutations.- Chapter 2 Prophylactic Risk Reducing Surgery for Breast Cancer.- Chapter 3 Merits and Demerits of Practice for Hereditary Breast and Ovarian Cancer Syndrome (Advices and Issues).- Part II Axillary Treatment.- Chapter 4 Sentinel Lymph Node Biopsy and Neoadjuvant Chemotherapy in Breast Cancer Patients.- Chapter 5 Axillary Reverse Mapping (ARM) as a Means to Reduce Lymphedema During Sentinel Lymph Node or Axillary Node Dissection.- Chapter 6 Ultrasound for Axillary Staging.- Chapter 7 One Step Nucleic Acid Amplification(OSNA)Assay for Primary Breast Cancer.- Chapter 8 Management of the Clinically Node-Negative Axilla in Primary Breast Cancer.- Chapter 9 Lymphatic Mapping and Optimization of Sentinel Lymph Node Dissection.- Part III Radiation therapy.- Chapter 10 Personalization of Radiotherapy for Breast Cancer.- Chapter 11 New Technologies in Radiation Therapy.- Chapter 12 Radiotherapy Following Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer.- Part IV Preoperative Hormone Therapy.- Chapter 13 Novel Translational Research of Neoadjuvant Endocrine Therapy.- Chapter 14 Alterations of Biomarkers by Neoadjuvant Endocrine Therapy.- Part V Preoperative chemotherapy.- Chapter 15 Essence of Neoadjuvant Therapy.- Chapter 16 The challenge to Overcome Triple Negative Breast Cancer Heterogeneity.- Chapter 17 Surgical Management of Breast Cancer after Preoperative Systemic Treatment.- Chapter 18 Imaging of Tumor Response by Preoperative Systemic Treatment.- Part VI Preoperative anti-HER2 therapy.- Chapter 19 Human Epidermal Growth Factor Receptor (HER) Family Molecular Structure.- Chapter 20 Locoregional Therapy Following Neoadjuvant Therapy for HER2-Positive Breast Cancer: Opportunities and Challenges.- Part VII Mathematical prediction/assessment model.- Chapter 21 Nomograms to predict positive resection margin and to predict 3 or more positive lymph nodes.- Chapter 22 Practical Use of Nomograms.- Chapter 23 Data Mining and Mathematical Model Development.