Multi-parametric MRI-based Peritumoral Radiomics for Stage IIA and IIB Classification of Cervical Cancer：A Multicenter Study

Objective: The aim of the study is to establish a multiparametric MRI (mpMRI)-based peritumoral radiomics nomogram for preoperatively predicting IIA and IIB classification of cervical Cancer preoperatively. Methods: 208 patients with histologically confirmed cervical cancer from three institutions were enrolled in this study. All the cases were randomly divided into the training cohort (n=145) and the validation cohort (n=63). The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The independent-sample t test and the Chi-squared test were conducted to assess the significance of clinical factors between the training cohort and the validation cohort. The Pearson correlation coefficient analysis and recursive feature elimination algorithm were adopted successively to obtain the well-representative features. Different classifiers were compared to develop the optimal radiomics signature across 5-fold cross validation. The calibration curves and decision curve analysis were conducted to evaluate the clinical utility of the optimal model. The radiomics model was constructed using logistic regression. Results: The peritumoral radiomics models were superior to the intratumoral radiomics models, regardless of single sequence model or fusion model (all P <0.001*). DWI-based peritumoral radiomics model performed best with the AUCs of 0.975 (0.965−0.983) and 0.899 (0.880−0.916) in the training and validation cohort, respectively. There was no significant difference between the validation AUCs of DWI-based and fusion peritumoral radiomics model (0.899 vs. 0.895, P=0.566). In addition, 3 pixel peritumoral regions of radiomic signatures have a much better discrimination performance in distinguishing IIA and IIB stage by comparing the 2,4,5 pixels extension surrounding the tumor. Conclusion: MRI-based radiomics model from peritumoral regions of cervical cancer outperformed radiologists for the preoperative diagnosis of IIA and IIB stage, which could provide a noninvasive and reliable way of individualized treatment plans for patients with cervical cancer.