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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.01265

Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer using Magnetic Resonance Imaging-based Radiomics

 Wujie Chen1, 2,  Siwen Wang3, 4,  Di Dong3, 4, Xuning Gao1, 5, Kefeng Zhou1, 5, Jiaying Li1, 5,  Bin Lv1, 6,  Hailin Li3, 4,  Xiangjun Wu3, 4,  Mengjie Fang3, 4,  Jie Tian3, 7* and  Maosheng Xu1, 5*
  • 1First College of Clinical Medicine, Zhejiang Chinese Medical University, China
  • 2Department of Radiology, Department of Gastroenterology, First Affiliated Hospital, Zhejiang Chinese Medical University, China
  • 3CAS Key Laboratory of Molecular Imaging, Institute of Automation (CAS), China
  • 4University of Chinese Academy of Sciences, China
  • 5Department of Radiology, First Affiliated Hospital, Zhejiang Chinese Medical University, China
  • 6Department of Gastroenterology, First Affiliated Hospital, Zhejiang Chinese Medical University, China
  • 7Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, China

Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients.

Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator logistic regression and learning vector quantization methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis.

Results: A two-feature radiomic signature was found significantly associated with LNM (p <0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve (AUC) of 0.850 (95% confidence interval [CI], 0.758 – 0.942) in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714 – 1.000) in internal validation cohort and 0.878 (95%CI, 0.696 – 1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, 0.851 in internal validation cohort, and 0.714, 0.952, 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. Decision curve analysis demonstrated good clinical use of radiomic nomogram.

Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

Keywords: lymph node metastasis, Magnetic Resonance Imaging, Diffusion-weighted imaging, Advanced gastric cancer, Radiomics

Received: 11 Sep 2019; Accepted: 01 Nov 2019.

Copyright: © 2019 Chen, Wang, Dong, Gao, Zhou, Li, Lv, Li, Wu, Fang, Tian and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
PhD. Jie Tian, Institute of Automation (CAS), CAS Key Laboratory of Molecular Imaging, Beijing, 100190, Beijing Municipality, China, jie.tian@ia.ac.cn
MD. Maosheng Xu, Zhejiang Chinese Medical University, First College of Clinical Medicine, Hangzhou, 310053, Zhejiang Province, China, xums166@zcmu.edu.cn