ArticleScientific reports2021
Creation and validation of models to predict response to primary treatment in serous ovarian cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 27 citations in OpenAlex.
- Bioinformatic pipeline to identify potential therapeutic targets with subsequent isolation and characterization of novel human anti- DDR1 antibodies.Scientific reports · 2026Article
- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Mucinous cystic neoplasms of the pancreas and liver share a similar DNA methylation profile with mucinous ovarian tumors.The Journal of pathology · 2025Article
- scRNA-seq Can Identify Different Cell Populations in Ovarian Cancer Bulk RNA-seq Experiments.International journal of molecular sciences · 2025Article
- Identifying ovarian cancer with machine learning DNA methylation pattern analysis.Scientific reports · 2025Article
- Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Identification of Ovarian High-Grade Serous Carcinoma with Mitochondrial Gene Variation.International journal of molecular sciences · 2025Article
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning.BioMedInformatics · 2024Article
- Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel.Clinical epigenetics · 2024Article
- OvaPrint-A Cell-free DNA Methylation Liquid Biopsy for the Risk Assessment of High-grade Serous Ovarian Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Article
- Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.European radiology experimental · 2023Review
- Microbial Communities in Gynecological Cancers and Their Association with Tumor Somatic Variation.Cancers · 2023Article
- A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC.Frontiers in oncology · 2023Article
- Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.International journal of molecular sciences · 2022Article
- Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes.International journal of molecular sciences · 2022Article
- HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data.Journal of translational medicine · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case-control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.
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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.