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

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.812993

DOI=10.3389/fonc.2021.812993

ISSN=2234-943X

ABSTRACT=Abstract
Prognostic biomarkers that can reliably predict 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 more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients underwent concurrent chemoradiotherapy (CCRT).
Methods:
This multicenter retrospective study recruited 263 patients with 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 least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazard model were applied to construct the radiomic signature (RS). According to the cutoff of RS, patients were divided into low- and high-risk groups. Pearson’s correlation and Kaplan-Meier analysis were used to evaluate the association of RS with DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model was 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 proposed a non-invasive prognostic tool for predicting DFS in patients with LACC based on multiparametric MRI. Taking the insufficient medical resource into account, multiparametric MRI-derived radiomic signature may improve the prediction of DFS for LACC patients underwent CCRT.