Theranostics 2020; 10(5):2284-2292. doi:10.7150/thno.37429 This issue

Research Paper

Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer

Jin Fang1*, Bin Zhang1*, Shuo Wang2,3*, Yan Jin4*, Fei Wang1*, Yingying Ding4, Qiuying Chen1, Liting Chen1, Yueyue Li1, Minmin Li1, Zhuozhi Chen1, Lizhi Liu5✉, Zhenyu Liu3,6✉, Jie Tian2,3,6✉, Shuixing Zhang1✉

1. Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China;
2. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China;
3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China;
4. Department of Radiology, the Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, Yunnan, 650031, China;
5. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China;
6. University of Chinese Academy of Sciences, Beijing, 100080, China.
*Jin Fang, Bin Zhang, Shuo Wang, Yan Jin and Fei Wang contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Citation:
Fang J, Zhang B, Wang S, Jin Y, Wang F, Ding Y, Chen Q, Chen L, Li Y, Li M, Chen Z, Liu L, Liu Z, Tian J, Zhang S. Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics 2020; 10(5):2284-2292. doi:10.7150/thno.37429. Available from https://www.thno.org/v10p2284.htm

File import instruction

Abstract

Graphic abstract

Pre-treatment survival prediction plays a key role in many diseases. We aimed to determine the prognostic value of pre-treatment Magnetic Resonance Imaging (MRI) based radiomic score for disease-free survival (DFS) in patients with early-stage (IB-IIA) cervical cancer.

Methods: A total of 248 patients with early-stage cervical cancer underwent radical hysterectomy were included from two institutions between January 1, 2011 and December 31, 2017, whose MR imaging data, clinicopathological data and DFS data were collected. Patients data were randomly divided into the training cohort (n = 166) and the validation cohort (n=82). Radiomic features were extracted from the pre-treatment T2-weighted (T2w) and contrast-enhanced T1-weighted (CET1w) MR imagings for each patient. Least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazard model were applied to construct radiomic score (Rad-score). According to the cutoff of Rad-score, patients were divided into low- and high- risk groups. Pearson's correlation and Kaplan-Meier analysis were used to evaluate the association of Rad-score with DFS. A combined model incorporating Rad-score, lymph node metastasis (LNM) and lymphovascular space invasion (LVI) by multivariate Cox proportional hazard model was constructed to estimate DFS individually.

Results: Higher Rad-scores were significantly associated with worse DFS in the training and validation cohorts (P<0.001 and P=0.011, respectively). The Rad-score demonstrated better prognostic performance in estimating DFS (C-index, 0.753; 95% CI: 0.696-0.805) than the clinicopathological features (C-index, 0.632; 95% CI: 0.567-0.700). However, the combined model showed no significant improvement (C-index, 0.714; 95%CI: 0.642-0.784).

Conclusion: The results demonstrated that MRI-derived Rad-score can be used as a prognostic biomarker for patients with early-stage (IB-IIA) cervical cancer, which can facilitate clinical decision-making.

Keywords: cervical cancer, magnetic resonance imaging, radiomics, disease-free survival