ArticleJapanese journal of radiology2024
Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study.
Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 13 citations in OpenAlex.
- Two case reports of ovarian clear cell carcinoma with central necrosis representing a mille-feuille-like appearance on contrast-enhanced MRI.Radiology case reports · 2026Article
- Can radiomics outperform CT morphological features in diagnosing ovarian clear cell carcinoma? A multicenter study.Abdominal radiology (New York) · 2026Article
- MRI-based morphology of ovarian clear cell carcinomas.Abdominal radiology (New York) · 2026Review
- Prognostic imaging features of ovarian clear cell carcinoma on multiparametric MRI.Abdominal radiology (New York) · 2026Article
- MRI Findings of Ovarian Carcinosarcoma: A Retrospective Comparison With Endometrioid Carcinoma.Cureus · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- The use of MRI in the diagnosis of ovarian cancer.Frontiers in medicine · 2026Review
- Multi-b-value diffusion-weighted imaging-derived parameters for differentiating high-grade serous ovarian carcinoma from other epithelial ovarian cancers.Japanese journal of radiology · 2025Article
- An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL).Journal of ultrasound · 2025Review
- Two case reports of ovarian clear cell carcinoma: Atypical MRI findings with a renal corticomedullary contrast-like appearance.Radiology case reports · 2025Article
- Prediction of grading of ovarian endometrioid carcinoma using conventional MRI features.Japanese journal of radiology · 2025Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). MATERIALS AND
methodsThirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC.
resultsFour features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models.
conclusionConventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.
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