ReviewMolecular and clinical oncology2022
Organoids and epithelial ovarian cancer - a future tool for personalized treatment decisions? (Review).
Review in Molecular and clinical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Organoids in the Clinic: A Systematic Review of Outcomes.Cells, tissues, organs · 2023Pooled it
- Re-Thinking Pharmacokinetics in Ovarian Cancer: What Do Organoids Add?International journal of molecular sciences · 2026Review
- Organoids and AI-integrated models in ovarian cancer research: the future of personalized therapy.Frontiers in oncology · 2026Review
- Culturally responsive strategies and practical considerations for live tissue studies in Māori participant cohorts.Frontiers in research metrics and analytics · 2024Article
- Immunobiology of high-grade serous ovarian cancer: lessons for clinical translation.Nature reviews. Cancer · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epithelial ovarian cancer (EOC) is the 5th leading cause of cancer-associated death in females worldwide. Although 80% of cases respond well to initial treatment, >70% develop recurrent disease and become chemoresistant within the first two years. Therefore, there is a great need for predictive biomarkers to guide treatment. In the era of precision medicine, organoids are studied as a functional method to predict treatment response to oncological treatment. The overall purpose of the present systematic review was to uncover the current status of patient-derived organoids and their ability to perform drug screenings for EOC. A systematic search for studies investigating ovarian cancer and organoids was performed using PubMed and the Cochrane Library. A total of 10 studies fulfilled the inclusion criteria. The growth rates of organoids were described in six studies and varied between 29 and 90%. Only four studies included data on clinical outcomes and indicated a positive correlation between clinical response and drug screening results. Inter- and intratumoral heterogeneity was examined in seven studies. They all suggested that the organoids recapture the tumor heterogeneity. Only one study performed drug screenings on organoids obtained from different tumor sites and metastasis from the same patient with EOC and revealed a different response to at least one drug for all patients. In conclusion, organoids may provide a platform for predicting the clinical response to chemotherapy and gene-targeting therapy. However, the results are only exploratory and the number of published drug screening studies is minimal. Further research is required to prove that organoids are able to support the choice of oncological treatment in patients with EOC.
Indexed as
Identifiers
What OpenQuestion holds
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.