Trial reportEuropean radiology2021
CT texture analysis in histological classification of epithelial ovarian carcinoma.
Trial report in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The trial behind it
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Who cites it
19 citing papers in PubMed.
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- A multimodal MRI radiomics model for distinguishing borderline from malignant ovarian epithelial tumors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Can radiomics outperform CT morphological features in diagnosing ovarian clear cell carcinoma? A multicenter study.Abdominal radiology (New York) · 2026Article
- A clinicoradiological model based on clinical and CT features for preoperative prediction of histological classification in patients with epithelial ovarian cancers: a two-center study.Abdominal radiology (New York) · 2025Article
- Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma.BMC cancer · 2025Article
- Differentiating small intestinal stromal tumors from primary small intestinal lymphomas using contrast-enhanced CT and texture analysis: a diagnostic study.Frontiers in oncology · 2025Article
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.Physical and engineering sciences in medicine · 2024Article
- CT-based radiomic analysis for categorization of ovarian sex cord-stromal tumors and epithelial ovarian cancers.Abdominal radiology (New York) · 2024Article
- Radiomics Features on Enhanced Computed Tomography Predict FOXP3 Expression and Clinical Prognosis in Patients with Head and Neck Squamous Cell Carcinoma.Journal of imaging informatics in medicine · 2024Article
- CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.Quantitative imaging in medicine and surgery · 2023Article
- Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer.La Radiologia medica · 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
- Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative.European radiology · 2023Article
- Machine learning-based CT texture analysis in the differentiation of testicular masses.Frontiers in oncology · 2023Article
- Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors.Frontiers in oncology · 2022Article
- CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.Journal of clinical medicine · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesThe study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC).
methodsConsecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features.
resultsHGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464).
conclusionCT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features. KEY POINTS: • A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.
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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.