ArticleBioMed research international2022
Application Values of 2D and 3D Radiomics Models Based on CT Plain Scan in Differentiating Benign from Malignant Ovarian Tumors.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- 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
- Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.Insights into imaging · 2024Article
- Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomographyDiagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors.European journal of medical research · 2023Article
- Lack of incremental value of three-dimensional measurement in assessing invasiveness for lung cancer.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2023Article
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
- Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review.Insights into imaging · 2023Review
- CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.Journal of ovarian research · 2023Article
- Article
- Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis.EClinicalMedicine · 2022Article
- 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
- Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features.Frontiers in neurology · 2022Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate identification of ovarian tumors as benign or malignant is highly crucial. Radiomics is a new branch of imaging that has emerged in recent years to replace the traditional naked eye qualitative diagnosis.
objectiveThis study is aimed at exploring the difference in the application potential of two- (2D) and three-dimensional (3D) radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors.
methodA retrospective analysis was performed on 140 patients with ovarian tumors confirmed by surgery and pathology in our hospital from July 2017 to August 2020. These 140 patients were divided into benign group and malignant group according to the pathological results. The ITK-SNAP software was used to outline the regions-of-interest (ROI) of 2D or 3D tumors on the CT plain scan image of each patient; the texture features were extracted through analysis kit (AK), and the cases were randomly divided into training groups (
resultsThere were 396 quantitative radiomics feature parameters extracted from 2D group and the 3D group, respectively. The area under the curve (AUC) of the radiomics nomogram of the 2D training group and the validation group were 0.96 and 0.97, respectively. The accuracy, specificity, and sensitivity of the training set were 92.9%, 88.9%, and 96.3%, respectively, and those of the validation set were 90.2%, 82.6%, and 100.0%, respectively. The AUCs of the radiomics nomogram of the 3D training group and validation group were 0.96% and 0.99%, respectively. The accuracy, sensitivity, and specificity of the training set were 92.9%, 96.3%, and 88.9%, respectively, and those of the validation set were 97.6%, 95.7%, and 100.0%, respectively. DeLong's test indicated that there was no statistical significance between the two sets (
conclusionsFor the differential diagnosis of benign and malignant ovarian tumors, the 2D and 3D radiomics nomogram models exhibited comparable diagnostic performance. Considering that the 2D model was cost-effective and time-efficient, it was more recommended to use 2D features in future research.
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