DataSheet_1_Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT.docx

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).MethodsThis 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.ResultsThe 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

对于确定局部晚期宫颈癌（LACC）患者中高风险群体，以识别可能从更激进的治疗中受益的患者，迫切需要能够可靠预测无病生存期（DFS）的预后生物标志物。在本研究中，我们旨在构建一个多参数MRI衍生的影像组学特征，以预测接受同期放化疗（CCRT）的LACC患者的DFS。方法：本多中心回顾性研究纳入了263名国际妇产科联合会（FIGO）分期IB-IVA且接受了CCRT治疗的患者，并对他们进行了治疗前的MRI扫描。他们被随机分为两组：主要队列（n = 178）和验证队列（n = 85）。通过LASSO回归和Cox比例风险回归分析构建了影像组学特征（RS）。根据RS值的截断点，患者被分为低风险和高风险组。使用皮尔逊相关分析和Kaplan-Meier分析评估RS与DFS之间的关联。构建了RS、结合FIGO分期和淋巴结转移的多变量Cox比例风险模型构建的临床模型，以及结合RS和临床模型的联合模型，以独立估计DFS。结果：最终的影像组学特征由四个影像组学特征组成：T2W_wavelet-LH_ glszm_Size Zone NonUniformity，ADC_wavelet-HL-first order_ Median，ADC_wavelet-HH-glrlm_Long Run Low Gray Level Emphasis，以及ADC_wavelet _LL_gldm_Large Dependence High Gray Emphasis。较高的RS在主要队列和验证队列中与较差的DFS显著相关（均为p