Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis
Xin Tian, Caixia Sun, Zhenyu Liu, Weili Li, Hui Duan, Lu Wang, Lu Wang, Huijian Fan, Mingwei Li, Pengfei Li, Lihui Wang, Lihui Wang, Ping Liu, Jie Tian, Chunlin Chen
Abstract
Objective: To investigate whether pretreatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC). Patients and Methods: 277 LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. 1294 radiomic features were extracted from venous contrast enhanced and non-enhanced CT imaging for each patient. Five combined methods of feature selection were used to reduce dimension of features. Radiomics signature was constructed by Random Forest (RF) method in a primary cohort of 221 patients. A combined model incorporating radiomics signature with clinical factors was developed using multivariable logistic regression. Prediction performance was then tested in a validation cohort of 56 patients. Results: Radiomics signature containing pre and post contrast imaging features can adequately distinguish chemotherapeutic responders from non-responders in both primary and validation cohorts [AUCs: 0.773 (95% CI, 0.701-0.845) and 0.816 (95% CI, 0.690-0.942), respectively]. The combined model has a better predictive performance with an AUC of 0.803 (95% CI, 0.734-0.872) in the primary set and an AUC of 0.821 (95% CI, 0.697 to 0.946) in the validation set, compared to radiomics signature alone. Both models showed good discrimination, calibration. Conclusion: Newly developed radiomic model provided an easy-to-use predictor of chemotherapeutic response with improved predictive ability, which might facilitate optimal treatment strategies tailored for individual LACC patients