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Multiparametric MRI‐Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early‐Stage Cervical Cancer
Author(s) -
Xiao Meiling,
Ma Fenghua,
Li Ying,
Li Yongai,
Li Mengdie,
Zhang Guofu,
Qiang Jinwei
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27101
Subject(s) - nomogram , medicine , receiver operating characteristic , radiomics , confidence interval , radiology , effective diffusion coefficient , stage (stratigraphy) , logistic regression , retrospective cohort study , nuclear medicine , magnetic resonance imaging , oncology , paleontology , biology
Background Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early‐stage cervical cancer. Purpose To establish a multiparametric MRI (mpMRI)‐based radiomics nomogram for preoperatively predicting LNM status. Study Type Retrospective. Population Among 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort. Field Strength Radiomic features were extracted from a 1.5T mpMRI scan (T 1 ‐weighted imaging [T 1 WI], fat‐saturated T 2 ‐weighted imaging [FS‐T 2 WI], contrast‐enhanced [CE], diffusion‐weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps). Assessment The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated. Statistical Tests The least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t ‐test and chi‐squared test were used to compare the differences in continuous and categorical variables, respectively. Results The radiomic signature allowed a good discrimination between the LNM and non‐LNM groups, with a C‐index of 0.856 (95% confidence interval [CI], 0.794–0.918) in the primary cohort and 0.883 (95% CI, 0.809–0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C‐index, 0.882 [95% CI, 0.827–0.937], C‐index, 0.893 [95% CI, 0.822–0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Data Conclusion A radiomics nomogram was developed by incorporating the radiomics signature with the MRI‐reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early‐stage cervical cancer. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2020;52:885–896.