Advances in the Diagnosis and Management of Gynecological Cancers

Dear Colleagues,

Gynecological cancers are defined as the overgrowth of neoplastic cells mainly in the cervix, ovaries, uterus, vagina, and vulva. The diagnosis and management of these cancers have evolved over the years and molecular diagnosis and artificial intelligence (AI) are helping and improving early detection. Currently, the advancement of AI can speed up and help pathologists to detect tumor cells from cytologic images. Moreover, applying the most advanced molecular techniques, such as next-generation sequencing (NGS) of RNA, together with genetic methylation profiles, can help differentiate uterine sarcomas and uterine mesenchymal neoplasms, respectively. However, despite advances in medical science, human papillomavirus (HPV) is still prevalent in low-income countries in spite of the vaccine recommendation for preteens aged 11 to 12.

We are pleased to invite you to publish in Diagnostics (ISSN 2075-4418), an international scholarly open-access journal on medical diagnosis, for the Special Issue entitled Diagnosis and Management of Gynecological Cancers. In this issue, we are focusing on molecular diagnostics, medical screening, prognosis, and artificial intelligence in diagnostics, in order to cover the past, present and future of gynecological cancers and understand how the most advanced technologies are improving the molecular detection and management of these cancers.

In this Special Issue, we would like to (i) cover the history of gynecological cancer, (ii) show how the most advanced technologies are improving detection and management, (iii) assess the genetic background of these cancers, and (iv) understand the epidemiology of gynecological cancer between high/low-income countries.

Research areas may include (but are not limited to) the following:

• The history of gynecological cancers: diagnosis and management;
• The detection of HPV subtypes and their risk;
• The epidemiology of gynecological cancer (high- vs low-income countries);
• Genetic screening;
• Targeted therapies for genomic instabilities (GIs) in gynecological carcinomas;
• The molecular classification of uterine sarcomas using NGS for RNA;
• DNA methylation profile for the molecular classification of uterine mesenchymal neoplasia;
• The use of AI in diagnosis and management.

We look forward to receiving your contributions.

Dr. Luiz Gustavo De Almeida
Dr. Leandro Ucela Alves
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• gynecologic oncology
• molecular biology
• artificial intelligence
• NGS
• DNA
• RNA
• methylation
• epidemiology