ArticleFrontiers in oncology2021
Preoperative Nomogram for Differentiation of Histological Subtypes in Ovarian Cancer Based on Computer Tomography Radiomics.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024.Frontiers in oncology · 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
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 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
- Virtual biopsy in abdominal pathology: where do we stand?BJR open · 2023Review
- Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery.BMC medical imaging · 2022Article
- Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.Frontiers in oncology · 2022Article
- The role of CT, PET-CT, and MRI in ovarian cancer.The British journal of radiology · 2021Review
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8 authors.
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Abstract
objectivesNon-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated.
methodsRadiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC.
resultsEight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97.
conclusionsNomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.
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