Table_1_Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analys.docx (251.63 kB)
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Table_1_Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis.docx

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posted on 04.02.2020, 04:19 by Xin Tian, Caixia Sun, Zhenyu Liu, Weili Li, Hui Duan, Lu Wang, Huijian Fan, Mingwei Li, Pengfei Li, Lihui Wang, Ping Liu, Jie Tian, Chunlin Chen

Objective: To investigate whether pre-treatment 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: Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four 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] and remain relatively stable across centers. 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, 0.697–0.946) 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.

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