基于治疗前多参数MRI影像组学特征预测局部晚期宫颈癌患者新辅助化疗后脉管浸润OA北大核心CSTPCD

Objective:To develop a model utilizing radiomic features from pre-treatment multiparametric magnetic resonance imaging (mpMRI) to predict lymphovascular space invasion (LVSI) status after neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC). Materials and Methods:A retrospective analysis was conducted on clinical and imaging data of 300 patients with locally advanced cervical cancer (LACC) who underwent neoadjuvant chemotherapy (NACT) followed by radical hysterectomy. These patients were divided into a training set (187 patients,with 73 LVSI positive cases) from Henan Provincial People's Hospital and a validation set (113 patients,with 31 LVSI positive cases) from Henan Provincial Cancer Hospital. Tumor regions of interest (ROIs) were delineated on axial diffusion-weighted imaging (Ax_DWI),sagittal T2-weighted imaging (Sag_T2WI),and sagittal T1-weighted contrast-enhanced imaging (Sag_T1C),and features were extracted. Radiomic features were selected using recursive feature elimination (RFE) algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently,single-sequence models,dual-sequence models,and combined model based on three-sequence radiomic features were established using logistic regression classifiers. The performance of each model was evaluated using receiver operating characteristic (ROC) curves,with area under the curve (AUC) compared using the Delong test. Clinical utility was assessed using decision curves. Results:In the validation set,the AUCs of the single-sequence models constructed based on Ax_DWI,Sag_T2WI,and Sag_T1C were 0.717[95％ confidence interval (CI):0.605−0.829],0.734 (95％ CI:0.633−0.836),and 0.733 (95％ CI:0.626−0.841) respectively. The AUCs of the dual-sequence models constructed based on Ax_DWI+Sag_T2WI,Ax_DWI+Sag_T1C,and Sag_T2WI+Sag_T1C were 0.763 (95％ CI:0.660−0.866),0.786 (95％ CI:0.692−0.881),and 0.815 (95％ CI:0.731−0.899) respectively. The AUC of the combined model was 0.829 (95％ CI:0.740−0.914),which was higher than that of the single-sequence and dual-sequence models,however,the difference in AUC between the combined sequence model and the Ax_DWI model,Sag_T2WI model,as well as the Ax_DWI+Sag_T2WI model was not statistically significant (P=0.015−0.047). Decision curves showed that the clinical net benefit of the joint-sequence model was higher than that of the single-sequence and dual-sequence models. Conclusions:The combined model constructed based on pre-treatment multiparametric MRI radiomic features can effectively predict the LVSI status after NACT in LACC patients based on pre-treatment mpMRI.