Radiomic Analysis for Pretreatment Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer: A Multicentre Study

Background: We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to primary and independent validation cohorts (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve. Findings: The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images ,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in primary cohort and 0.999 in validation cohort, which was significantly better (p<0.05) than the other radiomic models. Moreover, no significant variation in performance was found if different primary cohorts were used. Interpretation: This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment. Funding: This work was supported by the National Key Research and Development Plan of China [Grant No. 2017YFA0205200]; the National Natural Science Foundation of China [Grant No. 81772012, 81227901, 81527805, and 66161010]; the Nature Science Foundation of Guizhou province [Grant No. 20152044]; the Chinese Academy of Sciences [Grant No. GJJSTD20170004, XDB32030200, and QYZDJ-SSW-JSC005]; the Beijing Natural Science Foundation [Grant No. 7182109]; the Youth Innovation Promotion Association CAS [Grant No. 2019136]; the National Natural Science Foundation of Guangdong [Grant No. 2015A030311024]; the Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology [No. 201508020264]; the National Key Technology Program of the Ministry of Science and Technology [863 program, Grant No. 2014BAI05B03]; the Medical Scientific Research Foundation of Guangdong Province of China [Grant No. A2015063]. Declaration of Interest: The authors declare no potential conflicts of interest. Ethical Approval: This study design was approved by an institutional review board and waived the informed consent requirement.