J Cancer 2021; 12(3):726-734. doi:10.7150/jca.50872

Research Paper

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

Jingya Chen1,5, Hailei Gu2, Weimin Fan3, Yaohui Wang4, Shuai Chen1, Xiao Chen1, Zhongqiu Wang1,5✉

1. Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese medicine, Nanjing, Jiangsu Province, China.
2. Department of radiology, Women's Hospital of Nanjing medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, Jiangsu Province, China.
3. Department of Clinical Laboratory, Women's Hospital of Nanjing medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, Jiangsu Province, China.
4. Department of Pathology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese medicine, Nanjing, Jiangsu Province, China.
5. Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong Province, China.

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Citation:
Chen J, Gu H, Fan W, Wang Y, Chen S, Chen X, Wang Z. MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer. J Cancer 2021; 12(3):726-734. doi:10.7150/jca.50872. Available from https://www.jcancer.org/v12p0726.htm

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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.

Keywords: MRI, radiomic, endometrial cancer, risk, nomogram