ReviewCancers2021
Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ovarian cancer staging and follow-up: updated guidelines from the European Society of Urogenital Radiology female pelvic imaging working group.European radiology · 2025Guideline
- Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.Abdominal radiology (New York) · 2024Pooled it
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- Tumor stroma ratio-based radiomics model for predicting platinum resistance and prognosis in epithelial ovarian cancer.Journal of ovarian research · 2026Article
- Role of Imaging Techniques in Ovarian Cancer Diagnosis: Current Approaches and Future Directions.Cancers · 2026Review
- Radiomics and radiogenomics in ovarian cancer: a review with a focus on ultrasound applications.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics.Journal of ovarian research · 2024Article
- Intratumoral and peritumoral CT radiomics in predicting prognosis in patients with chondrosarcoma: a multicenter study.Insights into imaging · 2024Article
- Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2023Article
- A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer.Scientific reports · 2023Article
- Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.European radiology experimental · 2023Review
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
- Stage III Non-Small-Cell Lung Cancer: An Overview of Treatment Options.Current oncology (Toronto, Ont.) · 2023Review
- CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer.Journal of personalized medicine · 2022Article
- Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.AJR. American journal of roentgenology · 2022Review
- Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer.La Radiologia medica · 2022Article
- Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.Frontiers in oncology · 2022Article
- Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses on inter-site heterogeneity. This systematic review was conducted according to the PRISMA statement. After the initial retrieval of 145 articles, the final systematic review comprised six articles. Association between radiomic features and OS was evaluated in 3/6 studies (50%); all articles showed a significant association between radiomic features and OS. Association with PFS was evaluated in 5/6 (83%) articles; the period of follow-up ranged between six and 36 months. All the articles showed significant association between radiomic models and PFS. Inter-site textural features were used for analysis in 2/6 (33%) articles. They demonstrated that high levels of inter-site textural heterogeneity were significantly associated with incomplete surgical resection in breast cancer gene-negative patients, and that lower heterogeneity was associated with complete resectability. There were some differences among papers in methodology; for example, only 3/6 (50%) articles included validation cohorts. In conclusion, radiomic models have demonstrated promising results as predictors of survival in OC patients, although larger studies are needed to allow clinical applicability.
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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.