ArticleCancer management and research2021
MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors.
Article in Cancer management and research, 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.
- Diagnostic Performance of Computed Tomography-Based Machine Learning Models in the Classification of Adnexal Masses - A Systematic Review.F1000Research · 2026Pooled it
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- Habitat radiomics based on CT for assessing BRCA mutation status in patients with high-grade serous ovarian cancer: a multicenter study.Frontiers in oncology · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma.Abdominal radiology (New York) · 2025Article
- Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging.Scientific reports · 2024Article
- Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.BMC medical imaging · 2024Article
- Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024.Frontiers in oncology · 2024Article
- Radiomics in the evaluation of ovarian masses - a systematic review.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
- Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.European radiology · 2023Article
- Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.Insights into imaging · 2023Article
- Radiomics nomogram for preoperative differentiation of early-stage serous borderline ovarian tumors and serous malignant ovarian tumors.Frontiers in oncology · 2023Article
- Virtual biopsy in abdominal pathology: where do we stand?BJR open · 2023Review
- AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis.Frontiers in oncology · 2023Review
- Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis.EClinicalMedicine · 2022Article
- Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors.Frontiers in oncology · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate whether multidetector computed tomography (MDCT)-based radiomics features can discriminate between serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs). PATIENTS AND
methodsEighty patients with SBOTs and 102 patients with SMOTs, confirmed by pathology (training set: n = 127; validation set: n = 55) from December 2017 to June 2020, were enrolled in this study. The interclass correlation coefficient (ICC) and least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics parameters derived from MDCT images on the arterial phase (AP), venous phase (VP), and equilibrium phase (EP). Receiver operating characteristic (ROC) analysis of each selected parameter was carried out. Heat maps were created to illustrate the distribution of the radiomics parameters. Three models incorporating selected radiomics parameters generated by support vector machine (SVM) classifiers in each phase were analyzed by ROC and compared using the DeLong test.
resultsThe most predictive features selected by ICC and LASSO regression between SBOTs and SMOTs included 9 radiomics parameters on AP, VP, and EP each. Three models on AP, VP, and EP incorporating the selected features generated by SVM classifiers produced AUCs of 0.80 (accuracy, 0.75; sensitivity, 0.74; specificity, 0.75), 0.86 (accuracy, 0.78; sensitivity, 0.80; specificity, 0.75), and 0.73 (accuracy, 0.69; sensitivity, 0.71; specificity, 0.67), respectively. There were no significant differences in the AUCs among the three models (AP vs. VP, P = 0.199; AP vs. EP, P = 0.260; VP vs. EP, P = 0.793).
conclusionMDCT-based radiomics features could be used as biomarkers for the differentiation of SBOTs and SMOTs.
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