ArticleEuropean radiology2020
Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess.Parasitology research · 2022Trial
- Preoperative prediction of parametrial invasion in early-stage cervical cancer: a radiomics nomogram fusing multi-parametric MRI and clinical biomarkers.Abdominal radiology (New York) · 2026Article
- Clinical translation of a DWI-radiomics nomogram integrating serum biomarkers for pretreatment staging of cervical cancer.BMC medical imaging · 2026Article
- Radiomics based on habitat analysis in predicting parametrial invasion of early stage cervical cancer.Frontiers in oncology · 2026Article
- Article
- Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study.Scientific reports · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Endovaginal coil combined with pelvic array coil magnetic resonance imaging for the preoperative staging of cervical cancer.Quantitative imaging in medicine and surgery · 2025Article
- Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.Journal of vascular surgery. Venous and lymphatic disorders · 2025Article
- Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features.Frontiers in oncology · 2025Article
- Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer.Frontiers in oncology · 2025Article
- Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Development and validation of a clinical and ultrasound features-based nomogram for preoperative differentiation of renal urothelial carcinoma and central renal cell carcinoma.World journal of urology · 2024Article
- Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.Journal of imaging informatics in medicine · 2024Article
- Intratumoral and peritumoral MRI radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma.European radiology · 2024Article
- Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer.Frontiers in medicine · 2024Article
- Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.Abdominal radiology (New York) · 2024Article
- MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.Cancer medicine · 2023Article
- High-resolution reduced field-of-view diffusion-weighted magnetic resonance imaging in the diagnosis of cervical cancer.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeTo develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC). MATERIALS AND
methodsAll 137 patients with ECC (FIGO stages IB-IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient's age, and pathological grade; its discrimination and calibration performances were estimated.
resultsFor T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682-0.911) vs 0.946 (95% CI, 0.899-0.994), and 0.780 (95% CI, 0.641-0.920) vs 0.921 (95% CI, 0.832-1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient's age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933-1) and 0.941 (95% CI, 0.868-1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement.
conclusionsThe radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC. KEY POINTS: • No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC. • Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC. • Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.
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