Special Issue Title:

Prognostic Models Based on Machine Learning for Gynecological Cancers

Deadline for manuscript submissions: 31 January 2023

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Special Issue Editor

  • Guest Editor

    Feng Jiang, MDE-MailWebsite

    Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

    Interests: Bioinformatics; Machine Learning; Cancer Biology; Cancer Immunology

Special Issue Information

Dear Colleagues,

Cancer of the female genital organs is specifically known as gynecological cancers, including primary peritoneal cancer, tubo-ovarian cancer, uterine and cervical cancer, vaginal cancer, and vulvar cancer. Although surgery is the main treatment for gynecological cancers patients, except in cases of late-stage cervix, vulvar, and vagina cancers. In addition, for those patients with far-advanced or recurrent diseases, the ourcome after treatment is often disappointing. Significant unmet needs exist in the diagnosis and treatment of these cancers. Recently, with advances in modern technology, the use of next-generation whole genomic sequences has become much plausible in the diagnosis and treatment of these patients.

Prognostic models combine multiple prognostic factors to estimate the risk of future outcomes in individuals with a particular disease or health condition. Such models can assist in the decision-making process aimed at achieving specific clinical outcomes, as well as guide the allocation of healthcare resources. Prognostic models are based on prognostic information that generally addresses the patient rather than the disease or treatment. Examples include statements that predict chance or duration of survival, progression of disease, and prediction of certain clinical events related to therapy or treatment response. Through machine learning, genomic profiles have significantly improved our ability to prognosticate in gynecological cancers patients. Many studies have examined the prognostic significance of genomic biomarkers and clinical-pathological variables and have often shown that both provide independent information. Some of these have been developed to track activated molecular signaling pathways and particular biological processes such as cell proliferation, hypoxia, cell differentiation, immune cell processes and wound responses; other signatures have been specifically designed to predict sensitivity to drug sensitivity or biologic therapies.

This special issue will focus on prognostic models based on machine learning for gynecological cancers. We welcome original research as well as review articles.

Dr. Feng Jiang

Guest Editor


Bioinformatics; Machine Learning; Signature; Prognostic Models; Gynecological Cancers

Manuscript Submission Information

Manuscripts should be submitted online by submit system. Manuscripts can be submitted until the deadline. All papers will be 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. Original articles, case reports or comprehensive reviews 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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. European Journal of Gynaecological Oncology is an international peer-reviewed open access journal published by MRE Press. Please visit the Instructions for Authors page before submitting a manuscript.The Article Processing Charge (APC) for publication in this open access journal is $1200. We normally offer a discount greater than 30% to all contributors invited by the Editor-in-Chief, Guest Editor (GE) and Editorial board member. Submitted papers should be well formatted and use good English.

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