MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer.

Abstract:

:Introduction: Preoperative risk stratification is crucial for clinical treatment of endometrial cancer (EC). This study aimed to establish a model based on magnetic resonance imaging (MRI) and clinical factors for risk classification of EC. Materials and Methods: A total of 102 patients with pathologically proven Stage I EC were included. Preoperative MRI examinations were performed in all the patients. 720 radiomic features were extracted from T2-weighted images. Least absolute shrinkage and selection operator (LASSO) regression model was performed to reduce irrelevant features. Logistic regression was used to build clinical, radiomic and combined predictive models. A nomogram was developed for clinical application. Results: The radiomic model has a better performance than the model based on clinical and conventional MRI characteristics [AUC of 0.946 (95% CI: 0.882-0.973) vs AUC of 0.756 (95% CI: 0.65, 0.86)]. The combined model consisting of radiomic features and tumor size showed the best predictive performance in the training cohort with AUC of 0.955 in the training and 0.889 in the validation cohorts. Conclusions: MRI-based radiomic model has great potential in prediction of low-risk ECs.

journal_name

J Cancer

journal_title

Journal of Cancer

authors

Chen J,Gu H,Fan W,Wang Y,Chen S,Chen X,Wang Z

doi

10.7150/jca.50872

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

726-734

issue

3

issn

1837-9664

pii

jcav12p0726

journal_volume

12

pub_type

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