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MR‐Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph‐Vascular Space Invasion preoperatively
Author(s) -
Li Zhicong,
Li Hailin,
Wang Shiyu,
Dong Di,
Yin Fangfang,
Chen An,
Wang Siwen,
Zhao Guangming,
Fang Mengjie,
Tian Jie,
Wu Sufang,
Wang Han
Publication year - 2019
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.26531
Subject(s) - nomogram , medicine , radiomics , univariate , logistic regression , radiology , receiver operating characteristic , confidence interval , univariate analysis , retrospective cohort study , cervical cancer , oncology , multivariate analysis , cancer , multivariate statistics , machine learning , computer science
Background Lymph‐vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. Purpose To develop and validate an axial T 1 contrast‐enhanced (CE) MR‐based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. Study Type Retrospective. Population In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. Field Strength/Sequence T 1 CE MRI sequences at 1.5T. Assessment Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. Statistical Tests The Mann–Whitney U ‐test and the chi‐square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum‐redundancy/maximum‐relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression. Results Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non‐LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326–0.8745) in the training cohort and 0.727 (95% CI, 0.5449–0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. Data Conclusion T 1 CE MR‐based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively. Level of Evidence : 4 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2019;49:1420–1426.