ArticleInternational journal of gynecological cancer : official journal of the International Gynecological Cancer Society2018
Tumor Size at Magnetic Resonance Imaging Association With Lymph Node Metastasis and Lymphovascular Space Invasion in Resectable Cervical Cancer: A Multicenter Evaluation of Surgical Specimens.
Article in International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, 2018. 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, 34 citations in OpenAlex.
- Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning.Current oncology (Toronto, Ont.) · 2022Trial
- Preoperative prediction of the need for postoperative adjuvant therapy in stage IB cervical cancer using tumor size measured on magnetic resonance imaging and serum squamous cell carcinoma antigen levels.AJOG global reports · 2026Article
- Robotic Versus Open Radical Hysterectomy in Early-Stage Cervical Cancer: A Comparative Cohort Study.Journal of clinical medicine · 2026Article
- A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer.Journal of imaging informatics in medicine · 2026Article
- Multi-parametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in early-stage cervical adenocarcinoma.Frontiers in oncology · 2025Article
- Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.Frontiers in oncology · 2025Article
- Amide proton transfer-weighted habitat radiomics: a superior approach for preoperative prediction of lymphovascular space invasion in cervical cancer.Frontiers in oncology · 2025Article
- Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features.BMC medical imaging · 2024Article
- A novel nomogram and risk stratification for early metastasis in cervical cancer after radical radiotherapy.Cancer medicine · 2023Article
- Current Update on PET/MRI in Gynecological Malignancies-A Review of the Literature.Current oncology (Toronto, Ont.) · 2023Review
- What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?Insights into imaging · 2022Article
- Feasibility of TEuropean radiology · 2021Article
- MRI-based three-dimensional reconstruction for staging cervical cancer and predicting high-risk patients.Annals of translational medicine · 2021Article
- Preoperative magnetic resonance imaging criteria for predicting lymph node metastasis in patients with stage IB1-IIA2 cervical cancer.Cancer medicine · 2021Article
- Value of integrated PET-IVIM MRI in predicting lymphovascular space invasion in cervical cancer without lymphatic metastasis.European journal of nuclear medicine and molecular imaging · 2021Article
- Article
- Diagnostic efficiency of whole-bodyCancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Tumor Volume Predicts High-Risk Patients and Guides Initial Chemoradiotherapy for Early Cervical Cancer.Frontiers in oncology · 2021Article
- Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics -Based Nomogram.Frontiers in oncology · 2021Article
- Correlation between hematological parameters and outcome in patients with locally advanced cervical cancer treated by concomitant chemoradiotherapy.Cancer medicine · 2020Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo determine whether gross tumor volume (GTV) and the maximum diameter of resectable cervical cancer at magnetic resonance imaging (MRI) could predict lymph node metastasis (LNM) and lymphovascular space invasion (LVSI). MATERIALS AND
methodsA total of 315 consecutive patients with cervical cancer were retrospectively identified. Gross tumor volume and the maximum diameter of tumor were evaluated on MRI. Univariate and multivariate logistic regression analyses were performed to determine whether tumor size could predict LNM and LVSI. Cutoffs of GTV, maximum diameter, and the International Federation of Gynecology and Obstetrics (FIGO) classification of tumor were first investigated in 255 patients (group A) and then validated in an independent cohort of 60 patients (group B) using area under the receiver operating characteristic curve (AUC) analysis for predicting the presence of LNM and LVSI.
resultsUnivariate analysis showed that GTV and the maximum diameter of tumor could predict LNM and LVSI (all P < 0.0001). Multivariate analyses indicated GTV as an independent risk factor of LNM and LVSI (all P < 0.0001). In group A, GTV, the maximum diameter, and the FIGO stage could identify LNM (AUC, 0.813, 0.741, and 0.69, respectively) and LVSI (AUC, 0.806, 0.751, and 0.684, respectively). In group B, GTV, the maximum diameter, and the FIGO stage could help identify LNM (AUC, 0.902, 0.825, and 0.759, respectively) and LVSI (AUC, 0.771, 0.748, and 0.700, respectively).
conclusionsGross tumor volume and the maximum diameter of resectable cervical cancer at MRI demonstrated capability in predicting LNM and LVSI, which were more accurate than FIGO stage.
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