Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre studyResearch in context

Caixia Sun; Xin Tian; Zhenyu Liu; Weili Li; Pengfei Li; Jiaming Chen; Weifeng Zhang; Ziyu Fang; Peiyan Du; Hui Duan; Ping Liu; Lihui Wang; Chunlin Chen; Jie Tian

EBioMedicine (Aug 2019)

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre studyResearch in context

Caixia Sun,
Xin Tian,
Zhenyu Liu,
Weili Li,
Pengfei Li,
Jiaming Chen,
Weifeng Zhang,
Ziyu Fang,
Peiyan Du,
Hui Duan,
Ping Liu,
Lihui Wang,
Chunlin Chen,
Jie Tian

Affiliations

Caixia Sun: Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Xin Tian: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Zhenyu Liu: CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Weili Li: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Pengfei Li: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Jiaming Chen: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Weifeng Zhang: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Ziyu Fang: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Peiyan Du: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Hui Duan: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
Ping Liu: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China; Corresponding author at: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, China.
Lihui Wang: Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China; Corresponding author at: Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, No.2708 South Section of Huaxi Avenue, Guiyang, 550025, China.
Chunlin Chen: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China; Corresponding author at: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, China.
Jie Tian: CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Engineering Research Center of Molecular and NeSuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China; Correspondence to: J. Tian, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Journal volume & issue: Vol. 46
pp. 160 – 169

Abstract

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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 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 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. Keywords: Radiomics, Magnetic resonance imaging, Neoadjuvant chemotherapy, Locally advanced cervical cancer

Published in EBioMedicine

ISSN: 2352-3964 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Medicine: Medicine (General)
Website: http://www.journals.elsevier.com/ebiomedicine/

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