期刊
EBIOMEDICINE
卷 46, 期 -, 页码 160-169出版社
ELSEVIER
DOI: 10.1016/j.ebiom.2019.07.049
关键词
Radiomics; Magnetic resonance imaging; Neoadjuvant chemotherapy; Locally advanced cervical cancer
资金
- National Key Research andDevelopment Plan of China [2017YFA0205200]
- National Natural Science Foundation of China [81772012, 81227901, 81527805, 66161010]
- Nature Science Foundation of Guizhou province [20152044]
- Chinese Academy of Sciences [GJJSTD20170004, XDB32030200, QYZDJ-SSW-JSC005]
- Beijing Natural Science Foundation [7182109]
- Youth Innovation Promotion Association CAS [2019136]
- National Natural Science Foundation of Guangdong [2015A030311024]
- Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology [201508020264]
- National Key Technology Program of the Ministry of Science and Technology [863 program] [2014BAI05B03]
- Medical Scientific Research Foundation of Guangdong Province of China [A2015063]
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 training and testing sets (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 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 training set and 0.999 in testing set, which was significantly better (p < .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets 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. (C) 2019 The Authors. Published by Elsevier B.V.
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