ArticleEuropean radiology2021
Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Quantitative and qualitative metrics of tumor stroma in predicting ovarian cancer outcomes and expansion of its study with AI-based tools.Molecular therapy. Oncology · 2025Review
- 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 and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Article
- Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma.Insights into imaging · 2024Article
- Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics.Frontiers in medicine · 2024Article
- Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI.Frontiers in oncology · 2024Article
- Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024.Frontiers in oncology · 2024Article
- A CT-based radiomics approach to predict intra-tumoral tertiary lymphoid structures and recurrence of intrahepatic cholangiocarcinoma.Insights into imaging · 2023Article
- Radiomics in the evaluation of ovarian masses - a systematic review.Insights into imaging · 2023Article
- Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.European radiology experimental · 2023Review
- Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram.European radiology · 2023Article
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
- Preoperative prediction of miliary changes in the small bowel mesentery in advanced high-grade serous ovarian cancer using MRI radiomics nomogram.Abdominal radiology (New York) · 2023Article
- A prediction nomogram for suboptimal debulking surgery in patients with serous ovarian carcinoma based on MRI T1 dual-echo imaging and diffusion-weighted imaging.Insights into imaging · 2022Article
- MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols.Journal of ovarian research · 2022Article
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13 authors.
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Abstract
objectivesTo develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC).
methodsIn total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set.
resultsIn total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively).
conclusionsThe proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.
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