ArticleScientific reports2021
A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in 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 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Effect of HIPEC on Peritoneal Recurrence in Peritoneal Metastasis Treated With Cytoreductive Surgery: A Systematic Review.Frontiers in oncology · 2021Pooled it
- A Two-Stage Hybrid Feature Selection and Ensemble Learning Framework With Explainable AI for Accurate PCOS Prediction.Health science reports · 2026Article
- Comparative effects of intranasal and intraperitoneal resveratrol on the eye-brain axis in a cisplatin-induced neurotoxic rat model.Scientific reports · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.Discover oncology · 2025Article
- Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis.Heliyon · 2024Review
- Enhancing precision medicine: a nomogram for predicting platinum resistance in epithelial ovarian cancer.World journal of surgical oncology · 2024Article
- A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer.Computational and structural biotechnology journal · 2023Article
- A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.Archives of gynecology and obstetrics · 2022Observational
- Review
- Perspectives on Ovarian Cancer 1809 to 2022 and Beyond.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma.BMC cancer · 2022Article
- Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplatin sensitivity was generated using the Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas databases. Four potential biomarkers (CYTH3, GALNT3, S100A14, and ERI1) were identified and optimized for immunohistochemistry (IHC). Validation was performed on a cohort of patients (n = 50) treated with surgical resection followed by adjuvant carboplatin. Predictive models were established to predict chemosensitivity. The four biomarkers were also assessed for their ability to prognosticate overall survival in three ovarian cancer microarray expression datasets from The Gene Expression Omnibus. The extreme gradient boosting (XGBoost) algorithm was selected for the final model to validate the accuracy in an independent validation dataset (n = 10). CYTH3 and S100A14, followed by nodal stage, were the features with the greatest importance. The four gene signature had comparable prognostication as clinical information for two-year survival. Assessment of tumour biology by means of gene expression can serve as an adjunct for prediction of chemosensitivity and prognostication. Potentially, the assessment of molecular markers alongside clinical information offers a chance to further optimise therapeutic decision making.
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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.