Full Description
The "Gynecological Cancers: An Interdisciplinary Approach" is the seventeenth volume of the "Interdisciplinary Cancer Research" series, publishes comprehensive volume on diagnosis and treatment of gynecological cancers. The volume starts with a general chapter on polycystic ovarian syndrome and increased risk of female cancers. Cervical cancer natural history, diagnosis, and management as well as the roles of infiltrated immune cells in the tumor microenvironment and metabolomic profiling as a promising tool for the noninvasive detection of endometrial cancer are discussed in this volume. An update on diagnosis and treatment of ovarian cancers is also fully discussed. This is the main concept of Cancer Immunology Project (CIP), which is a part of Universal Scientific Education and Research Network (USERN). This interdisciplinary book will be of special value for gynecologists and oncologists who wish to have an update on diagnosis and treatment of gynecological cancers.
Contents
Polycystic Ovarian Syndrome and Increased Risk of Female Cancers.- Tumor-infiltrating lymphocytes (TILs) and Gynecological Cancers.- CD133 as Biomarker and Therapeutic Target in Gynecological Cancers.- Cervical Cancer Natural History, Diagnosis, and Management: From Molecular Events to Clinical Management.- Potential Roles of Infiltrated Immune Cells in the Tumour Microenvironment of Endometrial Cancer.- Metabolomic Profiling as a Promising Tool for the Noninvasive Detection of Endometrial Cancer.- Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.- The Gene Expression and Mutations in Ovarian Cancer: Current Findings and Applications.- The Role of Secondary Cytoreductive Surgery in Ovarian Cancer.- Piwi interacting RNAs (piRNAs) in Ovarian Cancer.- The Role of PARP Inhibitors in the Treatment of Advanced Epithelial Ovarian Carcinoma.- Bariatric Surgery and Female Cancers.- Quantum MicroRNA Surveillance against Cancer: Parallel Dimensional Analysis of Integrated Networks by Quantum MicroRNA Language in Female Genital Neoplasms.- Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning.
