Ovarian Cancer Progression: From Experimental Models to Clinical Applications

Dear Colleagues,

The combination of cytoreductive surgery and chemotherapy for ovarian cancer is initially successful in decreasing tumor volume; however, the management of recurrent and progressive disease remains a significant treatment challenge. Discerning the enormous complexity of disease progression, through efforts that integrate disease biology with high-throughput computation and image-guided approaches, will contribute to the development of new treatment strategies. This Special Issue will be a platform to present interdisciplinary views about ovarian cancer progression. We would like to invite leaders in the field of ovarian cancer cell biology, computational biology, clinical diagnosis, and management. Authors should discuss the latest advances in ovarian cancer models (tissue and mouse models) and computational (omics, artificial intelligence) and imaging approaches. We especially encourage translational studies that identify links between biology and clinical application. We encourage submitting both original research and review articles. The focus areas include, but are not limited to:

A. Cell biology of disease progression.

• Spheroids, organoids, patient-derived xenografts, and genetically modified mouse models to study the biology of disease progression.
• Biology of tumor microenvironment: The contribution of mesothelial, mesenchymal, neuronal, fibroblastic, and immune cells to tumor progression.

B. Omics and computational approaches to discover vulnerabilities of progressive disease.

• Drug screens
• Genomics/proteomics
• Immunogenomics
• Radiomics
• Artificial intelligence-guided experimental approaches.

C. Clinical diagnosis and management of disease progression.

• Impact of chemotherapy on metastasis
• Targeted therapies in relapsed tumors
• Imaging of recurrent disease
• Surgical management of recurrent disease
• Intraoperative imaging
• Artificial intelligence-based patient stratification.

We strongly encourage studies that combine focus areas and methodologies.

Dr. Marcin Iwanicki
Dr. Mikołaj Piotr Zaborowski
Guest Editors

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