Gynecological Cancers: An Interdisciplinary Approach

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 incr...

Disgrifiad llawn

Wedi'i Gadw mewn:
Manylion Llyfryddiaeth
Awdur Corfforaethol: SpringerLink (Online service)
Awduron Eraill: Rezaei, Nima (Golygydd)
Fformat: Electronig eLyfr
Iaith:Saesneg
Cyhoeddwyd: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Rhifyn:1st ed. 2025.
Cyfres:Interdisciplinary Cancer Research, 17
Pynciau:
Mynediad Ar-lein:https://doi.org/10.1007/978-3-031-90398-4
Tagiau: Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
Tabl Cynhwysion:
  • 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.