Advances in Diagnosis of Gynecological Cancers
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 68
Special Issue Editors
Interests: molecular biology; gynecopathology; breast pathology
Special Issues, Collections and Topics in MDPI journals
Interests: molecular biology; gynecopathology; ovarian cancer
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Routinary clinical practice is moving towards personalized medicine by means of biological tissutal prognostic/predictive biomarkers and next-generation sequencing applied to solid samples, liquid biopsy or also by the use of single-cell DNA/RNA sequencing. Improvements in our understanding of the molecular altered background in different gynecological malignancies have allowed more precise pathological diagnoses, effective targeted therapies, the development of new therapeutical chances as modern immunotherapies and a more correct prognostic risk stratification. In addition to histopathology coupled with molecular diagnostics, it has been shown that the potential benefits of DP (digital pathology), AI (artificial intelligence) and ML (machine learning) technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. Oncological pretargeting has also been implemented and tested in several different ways in preclinical models and clinical trials for gynecological cancers, in particular for its suitability for imaging (PET imaging). Moreover, robotic surgical techniques are now a modern reality. The aim of this Special Issue is to highlight the different types of recent advances in the diagnosis and treatment of gynecological cancers.
Dr. Angela Santoro
Dr. Gian Franco Zannoni
Guest Editors
Manuscript Submission Information
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Keywords
- gynecological cancers
- histopathology
- next-generation sequencing
- liquid biopsy
- theranostics
- digital pathology
- artificial intelligence and machine learning
