Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT

Bing Liu; Zhen Sun; Zi-Liang Xu; Hong-Liang Zhao; Di-Di Wen; Yong-Ai Li; Fan Zhang; Bing-Xin Hou; Yi Huan; Li-Chun Wei; Min-Wen Zheng

doi:10.3389/fonc.2021.812993

Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT

Front Oncol. 2022 Jan 25:11:812993. doi: 10.3389/fonc.2021.812993. eCollection 2021.

Authors

Bing Liu¹, Zhen Sun², Zi-Liang Xu¹, Hong-Liang Zhao¹, Di-Di Wen¹, Yong-Ai Li³, Fan Zhang⁴, Bing-Xin Hou⁵, Yi Huan¹, Li-Chun Wei⁵, Min-Wen Zheng¹

Affiliations

¹ Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi'an, China.
² Department of Orthopaedics, Xijing Hospital, Airforce Military Medical University, Xi'an, China.
³ Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China.
⁵ Department of Radiation Oncology, Xijing Hospital, Airforce Military Medical University, Xi'an, China.

Abstract

Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT).

Methods: This multicenter retrospective study recruited 263 patients with International Federation of Gynecology and Obetrics (FIGO) stage IB-IVA treated with CCRT for whom pretreatment MRI scans were performed. They were randomly divided into two groups: primary cohort (n = 178) and validation cohort (n = 85). The LASSO regression and Cox proportional hazard regression were conducted to construct the radiomic signature (RS). According to the cutoff of the RS value, patients were dichotomized into low- and high-risk groups. Pearson's correlation and Kaplan-Meier analysis were conducted to evaluate the association between the RS and DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by the multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model were constructed to estimate DFS individually.

Results: The final radiomic signature consisted of four radiomic features: T2W_{_wavelet-LH_ glszm_Size Zone NonUniformity}, ADC_{_wavelet-HL-first order_ Median}, ADC_{_wavelet-HH-glrlm_Long Run Low Gray Level Emphasis}, and ADC_{_wavelet _LL_gldm_Large Dependence High Gray Emphasis}. Higher RS was significantly associated with worse DFS in the primary and validation cohorts (both p<0.001). The RS demonstrated better prognostic performance in predicting DFS than the clinical model in both cohorts (C-index, 0.736-0.758 for RS, and 0.603-0.649 for clinical model). However, the combined model showed no significant improvement (C-index, 0.648, 95% CI, 0.571-0.685).

Conclusions: The present study indicated that the multiparametric MRI-derived radiomic signature could be used as a non-invasive prognostic tool for predicting DFS in LACC patients.

Keywords: concurrent chemoradiotherapy; disease-free survival; locally advanced cervical cancer; multiparametric magnetic resonance imaging; radiomics.