Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis - Tian, Xin; Sun, Caixia; Liu, Zhenyu; Li, Weili; Duan, Hui; Wang, Lu; Fan, Huijian; Li, Mingwei; Li, Pengfei; Wang, Lihui; Liu, Ping; Tian, Jie; et al. (Frontiers in Oncology, 2020)
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