ArticleEuropean radiology2021
Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Radiomics-based machine learning in the prediction of peritoneal metastasis in ovarian cancer: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- Imaging of peritoneal surface malignancies: diagnosis and clinical implications.Abdominal radiology (New York) · 2026Review
- Role of Imaging Techniques in Ovarian Cancer Diagnosis: Current Approaches and Future Directions.Cancers · 2026Review
- The use of MRI in the diagnosis of ovarian cancer.Frontiers in medicine · 2026Review
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 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
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer.Insights into imaging · 2024Article
- Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics.Journal of ovarian research · 2024Article
- Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.Scientific reports · 2024Article
- Imaging of Peritoneal Metastases in Ovarian Cancer Using MDCT, MRI, and FDG PET/CT: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Development and validation of an ultrasound‑based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis.Journal of ovarian research · 2024Article
- Simultaneous high-resolution whole-brain MR spectroscopy and [European journal of nuclear medicine and molecular imaging · 2024Article
- Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024.Frontiers in oncology · 2024Article
- Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques.Korean journal of radiology · 2024Review
- Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI.Frontiers in oncology · 2024Article
- Imaging of Peritoneal Carcinomatosis in Advanced Ovarian Cancer: CT, MRI, Radiomic Features and Resectability Criteria.Cancers · 2023Review
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 2023Article
- Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.European radiology experimental · 2023Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo develop a radiomics signature based on multisequence magnetic resonance imaging (MRI) to preoperatively predict peritoneal metastasis (PM) in ovarian cancer (OC).
methodsEighty-nine patients with OC were divided into a training cohort including patients (n = 54) with a single lesion and a validation cohort including patients (n = 35) with bilateral lesions. Radiomics features were extracted from the T2-weighted images (T2WIs), fat-suppressed T2WIs, multi-b-value diffusion-weighted images (DWIs), and corresponding parametric maps. A radiomics signature and nomogram incorporating the radiomics signature and clinical predictors were developed and validated on the training and validation cohorts, respectively.
resultsThe radiomics signature generated by 6 selected features showed a favorable discriminatory ability to predict PM in OC with an area under the curve (AUC) of 0.963 in the training cohort and an AUC of 0.928 in the validation cohort. The nomogram, comprising the radiomics signature, pelvic fluid, and CA-125 level, showed more favorable discrimination with an AUC of 0.969 in the training cohort and 0.944 in the validation cohort. Net reclassification index with values of 0.548 in the training cohort and 0.500 in the validation cohort.
conclusionRadiomics signature based on multisequence MRI serves as an effective quantitative approach to predict PM in OC patients. A nomogram of radiomics signature and clinical predictors could further improve the prediction ability of PM in patients with OC. KEY POINTS: • Multisequence MRI-based radiomics showed a favorable discriminatory ability to predict PM in OC. • The nomogram incorporating the radiomics signature and clinical predictors was clinically useful to preoperatively predict PM in patients with OC.
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