Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT

• Type of Material: Article
• Authors: Jie Meng, Nanjing University; Shunli Liu, Nanjing University; Lijing Zhu, Nanjing University; Li Zhu, Nanjing University; Huanhuan Wang, Nanjing University; Li Xie, Nanjing University; Yue Guan, Nanjing University; Jian He, Nanjing University; Xiaofeng Yang, Emory University; Zhengyang Zhou, Nanjing University
• Language: English
• Date: 2018-07-30
• Publisher: Nature Research (part of Springer Nature): Fully open access journals
• Publication Version: Final Published Version
• Copyright Statement: © 2018, The Author(s)
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-018-29838-0
• Title of Journal or Parent Work: Scientific Reports
• ISSN: 2045-2322
• Volume: 8
• Issue: 1
• Start Page: 11399
• End Page: 11399
• Grant/Funding Information: This work was supported by the National Natural Science Foundation of China (ID: 81371516, 81501441, 81671751), Social Development Foundation of Jiangsu Province (BE2015605), Foundation of National Health and Family Planning Commission of China (W201306), the Natural Science Foundation of Jiangsu Province (ID: BK20150109, BK20150102), Jiangsu Province Health and Family Planning Commission Youth Scientific Research Project (ID: Q201508), Six Talent Peaks Project of Jiangsu Province (ID: 2015-WSN-079) and Key Project supported by Medical Science and technology development Foundation, Nanjing Department of Health (YKK15067).; The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources.
• Keywords: NEOADJUVANT CHEMORADIOTHERAPY; Science & Technology; FEATURES; F-18-FDG PET; PROSTATE-CANCER; TUMOR HETEROGENEITY; RECTAL-CANCER; CHEMOTHERAPY; Multidisciplinary Sciences; MAGNETIC-RESONANCE; CONCURRENT CHEMORADIOTHERAPY; Science & Technology - Other Topics; GLIOBLASTOMA
• Research Categories: Engineering, Electronics and Electrical; Health Sciences, Oncology

Abstract

This prospective study explored the application of texture features extracted from T2WI and apparent diffusion coefficient (ADC) maps in predicting recurrence of advanced cervical cancer patients treated with concurrent chemoradiotherapy (CCRT). We included 34 patients with advanced cervical cancer who underwent pelvic MR imaging before, during and after CCRT. Radiomic feature extraction was performed by using software at T2WI and ADC maps. The performance of texture parameters in predicting recurrence was evaluated. After a median follow-up of 31 months, eleven patients (32.4%) had recurrence. At four weeks after CCRT initiated, the most textural parameters (four T2 textural parameters and two ADC textural parameters) showed significant difference between the recurrence and nonrecurrence group (P values range, 0.002~0.046). Among them, RunLengthNonuniformity (RLN) from T2 and energy from ADC maps were the best selected predictors and together yield an AUC of 0.885. The support vector machine (SVM) classifier using ADC textural parameters performed best in predicting recurrence, while combining T2 textural parameters may add little value in prognosis. T2 and ADC textural parameters have potential as non-invasive imaging biomarkers in early predicting recurrence in advanced cervical cancer treated with CCRT.