1. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study
- Author
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Peiyan Du, Ping Liu, Lihui Wang, Caixia Sun, Pengfei Li, Xin Tian, Hui Duan, Weifeng Zhang, Jie Tian, Zhenyu Liu, Chunlin Chen, Ziyu Fang, Jiaming Chen, and Weili Li
- Subjects
0301 basic medicine ,medicine.medical_specialty ,medicine.medical_treatment ,Locally advanced ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,0302 clinical medicine ,Informed consent ,medicine ,In patient ,Medical physics ,Cervical cancer ,Chemotherapy ,Training set ,medicine.diagnostic_test ,Receiver operating characteristic ,business.industry ,Magnetic resonance imaging ,General Medicine ,Institutional review board ,medicine.disease ,Chinese academy of sciences ,Single sequence ,030104 developmental biology ,Feature (computer vision) ,030220 oncology & carcinogenesis ,Cohort ,Radiology ,business - Abstract
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
- Published
- 2019
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