ReviewJournal of ovarian research2020
Prediction of the treatment response in ovarian cancer: a ctDNA approach.
Review in Journal of ovarian research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A vision transformer-radiomics approach for enhanced chemotherapy outcome prediction in ovarian cancer.Frontiers in radiology · 2026Article
- Ovarian Tumor Biomarkers: Correlation Between Tumor Type and Marker Expression, and Their Role in Guiding Therapeutic Strategies.International journal of molecular sciences · 2025Review
- Nutrition-Based Paternal Influence on Gynecological Diseases in Female Offspring via Epigenetic Mechanisms.Nutrients · 2025Review
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- The interaction between klotho protein and epigenetic alteration in diabetes and treatment options.Journal of diabetes and metabolic disorders · 2024Review
- Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs.Current medicinal chemistry · 2024Article
- Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.Nature communications · 2023Article
- Predicting chemoresponsiveness in epithelial ovarian cancer patients using circulating small extracellular vesicle-derived plasma gelsolin.Journal of ovarian research · 2023Article
- Prognostic Impact of Circulating Methylated Homeobox A9 DNA in Patients Undergoing Treatment for Recurrent Ovarian Cancer.Cancers · 2022Article
- Circulating tumor DNA in cancer diagnosis, monitoring, and prognosis.Journal of the Egyptian National Cancer Institute · 2022Review
- Role of the Glyoxalase System in Breast Cancer and Gynecological Cancer-Implications for Therapeutic Intervention: a Review.Frontiers in oncology · 2022Review
- Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data.BioMed research international · 2022Article
- Copy Number Variation of Circulating Tumor DNA (ctDNA) Detected Using NIPT in Neoadjuvant Chemotherapy-Treated Ovarian Cancer Patients.Frontiers in genetics · 2022Article
- Role of Circulating miRNAs in Therapeutic Response in Epithelial Ovarian Cancer: A Systematic Revision.Biomedicines · 2021Review
- Review
- Joint IARC/NCI International Cancer Seminar Series Report: expert consensus on future directions for ovarian carcinoma research.Carcinogenesis · 2021Article
- Polymeric Nanoparticle Delivery of Combination Therapy with Synergistic Effects in Ovarian Cancer.Nanomaterials (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is the eighth most commonly occurring cancer in women. Clinically, the limitation of conventional screening and monitoring approaches inhibits high throughput analysis of the tumor molecular markers toward prediction of treatment response. Recently, analysis of liquid biopsies including circulating tumor DNA (ctDNA) open new way toward cancer diagnosis and treatment in a personalized manner in various types of solid tumors. In the case of ovarian carcinoma, growing pre-clinical and clinical studies underscored promising application of ctDNA in diagnosis, prognosis, and prediction of treatment response. In this review, we accumulate and highlight recent molecular findings of ctDNA analysis and its associations with treatment response and patient outcome. Additionally, we discussed the potential application of ctDNA in the personalized treatment of ovarian carcinoma. ctDNA-monitoring usage during the ovarian cancer treatments procedures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.