ArticleBMC medical imaging2021
Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiogenomic profiling to determine BRCA alteration status-a systematic review and meta-analysis.The British journal of radiology · 2025Pooled it
- Spatiotemporal immune heterogeneity and precision immunotherapy for ovarian cancer.Clinical and translational medicine · 2026Review
- Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study.NPJ precision oncology · 2026Article
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- 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
- Predicting pathogenic DNA damage repair gene mutations in prostate cancer patients: a multi-center magnetic resonance imaging radiomics study.Quantitative imaging in medicine and surgery · 2025Article
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Radiomics on slice-reduced versus full-chest computed tomography for diagnosis and staging of interstitial lung disease in systemic sclerosis: A comparative analysis.European journal of radiology open · 2024Article
- CT-based radiomics predicts HRD score and HRD status in patients with ovarian cancer.Frontiers in oncology · 2024Article
- Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.Insights into imaging · 2023Article
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
- CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Performance of CT radiomics in predicting the overall survival of patients with stage III clear cell renal carcinoma after radical nephrectomy.La Radiologia medica · 2022Article
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
- Article
- Prognostic and predictive value of radiomics features at MRI in nasopharyngeal carcinoma.Discover oncology · 2021Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer.
methodsThe clinical and imaging data of 106 patients with ovarian cancer confirmed by surgery and pathology were retrospectively analyzed and genetic testing was performed. Radiomics features extracted from the 2D and 3D regions of interest of the patients' primary tumor lesions were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso tenfold cross-validation. Feature subsets were employed to establish a radiomics model. The model's performance was evaluated via area under the receiver operating characteristic curve analysis and its clinical validity was assessed by using the model's decision curve.
resultsOn the validation set, the area under the curve values of the 2D, 3D, and 2D + 3D combined models were 0.78 (0.61-0.96), 0.75 (0.55-0.92), and 0.82 (0.61-0.96), respectively. However, the DeLong test P values between the three pairs of models were all > 0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities.
conclusionsThe three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the prediction performance of the three models.
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