Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients
- 14 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in European Radiology
- Vol. 31 (9), 6938-6948
- https://doi.org/10.1007/s00330-021-07735-x
Abstract
Objective To investigate the feasibility of T2WI-based radiomics nomogram analysis to non-invasively predict normal-sized pelvic lymph node (LN) metastasis (LNM) in cervical cancer patients. Methods Preoperative images of 219 normal-sized pathologically confirmed LNs from 132 cervical cancer patients admitted to our hospital between January 2013 and March 2020 were retrospectively reviewed. Regions of interests (ROIs) were separately delineated on whole LNs and tumors. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the construction of radiomics signature. Logistic regression modeling was employed to build models based on clinical features on LN T2WI (model 1), model 1 combined with LN radiomics features (model 2), and model 2 combined with tumor score (model 3). Diagnostic performance was assessed and compared. Results Both model 2 and model 3 showed higher diagnostic accuracy (training: model 2 0.75, model 3 0.78, model 1 0.72; validation: model 2 0.77, model 3 0.69, model 1 0.66) and AUC (training: model 2 0.77, model 3 0.82, model 1 0.74; validation: model 2 0.75, model 3 0.74, model 1 0.70) than clinical model 1. Diagnostic performance of model 3 was improved compared with model 2 in primary cohort, but reduced in validation cohort. However, the differences did not show obvious statistical difference (p = 0.05 and p = 0.15). Conclusions T2WI-based radiomics nomogram incorporating the LN radiomics signature with the clinical morphological LN features is promising for predicting the normal-sized pelvic LNM in cervical cancer patients. The original tumor radiomics analysis did not significantly improve the differential diagnosis of LNM. Key Points • The combination of LN radiomics signature with LN clinical morphological features on T2WI could discriminate LNM relatively well. • The tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.This publication has 46 references indexed in Scilit:
- The value of 3.0Tesla diffusion-weighted MRI for pelvic nodal staging in patients with early stage cervical cancerEuropean Journal of Cancer, 2012
- Correlation between tumor size and surveillance of lymph node metastasis for IB and IIA cervical cancer by magnetic resonance imagesEuropean Journal of Radiology, 2012
- Contribution of pelvic and para-aortic lymphadenectomy with sentinel node biopsy in patients with IB2–IIB cervical cancerBritish Journal of Cancer, 2011
- Diagnostic performance of computer tomography, magnetic resonance imaging, and positron emission tomography or positron emission tomography/computer tomography for detection of metastatic lymph nodes in patients with cervical cancer: Meta‐analysisCancer Science, 2010
- Accuracy of integrated FDG-PET/contrast-enhanced CT in detecting pelvic and paraaortic lymph node metastasis in patients with uterine cancerEuropean Radiology, 2009
- Detection of lymph node metastasis in cervical and uterine cancers by diffusion‐weighted magnetic resonance imaging at 3TJournal of Magnetic Resonance Imaging, 2008
- MRI for Pretreatment Lymph Node Staging in Uterine Cervical CancerAmerican Journal of Roentgenology, 2006
- Contrast-Enhanced CT for Differentiation of Ovarian Metastasis from Gastrointestinal Tract Cancer: Stomach Cancer Versus Colon CancerAmerican Journal of Roentgenology, 2006
- Diagnostic Performance of Nanoparticle-Enhanced Magnetic Resonance Imaging in the Diagnosis of Lymph Node Metastases in Patients With Endometrial and Cervical CancerJournal of Clinical Oncology, 2005
- Comparison of Dynamic Helical CT and Dynamic MR Imaging in the Evaluation of Pelvic Lymph Nodes in Cervical CarcinomaAmerican Journal of Roentgenology, 2000