ArticleEuropean radiology2021
Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study.
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 62 papers, 3 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 3 syntheses or guidelines pooled it, 104 citations in OpenAlex.
- Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- Accuracy of radiomics in the diagnosis and preoperative high-risk assessment of endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- A comprehensive magnetic resonance imaging-based model for predicting lymphovascular space invasion in endometrial cancer: a retrospective observational study.Quantitative imaging in medicine and surgery · 2025Article
- Diagnostic accuracy of MRI radiomics in predicting lymph node metastasis in prostate cancer: A systematic review.European journal of radiology open · 2025Review
- Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [European journal of nuclear medicine and molecular imaging · 2025Article
- Preoperative prediction of the HER2 status and prognosis of patients with endometrial cancer using multiparametric MRI-based radiomics: a multicenter study.Scientific reports · 2025Article
- A radiomics model based on diffusion-weighted imaging developed using machine learning enables prediction of microsatellite instability in endometrial cancer.BMC medical imaging · 2025Article
- Predictive value of models based on MRI radiomics and clinical indicators for lymphovascular space invasion in endometrial cancer.BMC cancer · 2025Article
- MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.Abdominal radiology (New York) · 2025Article
- Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.Abdominal radiology (New York) · 2025Article
- External validation of CEA and CA125 prediction model for lymph node metastasis in endometrial cancer: A multi-institute cohort study.Cancer biomarkers : section A of Disease markers · 2025Article
- Multi-sequence MRI-based clinical-radiomics models for the preoperative prediction of microsatellite instability-high status in endometrial cancer.Precision radiation oncology · 2025Article
- Delta dual‑region DCE-MRI radiomics from breast masses predicts axillary lymph node response after neoadjuvant therapy for breast cancer.BMC cancer · 2025Article
- Diagnostic accuracy of artificial intelligence-assisted radiology assessment of cancer: a systematic review.BJR artificial intelligence · 2025Article
- Multiparametric MRI-based radiomics and deep learning for differentiating uterine serous carcinoma from endometrioid carcinoma: a multicenter retrospective study.Frontiers in oncology · 2025Article
- Enhancing dermatological diagnosis for differentiating actinic from seborrheic keratosis using deep learning model.Frontiers in medicine · 2025Article
- Intratumoral and peritumoral multiparametric MRI-based radiomics nomogram for preoperative risk stratification in patients with endometrial cancer.Frontiers in oncology · 2025Article
2 more citing papers are in PubMed but not listed here.
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8 authors at 5 institutions in 1 country.
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Abstract
objectiveTo construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively.
methodsDuring January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27-85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B-E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model.
resultsThe AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone.
conclusionsThe MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC. KEY POINTS: • A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. • The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2.
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