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

Front. Oncol. | doi: 10.3389/fonc.2019.01050

Pretreatment Prediction of Adaptive Radiation Therapy Eligibility using MRI-based Radiomics for Advanced Nasopharyngeal Carcinoma Patients

Tingting Yu1,  Sai Kit Lam1, Lok Hang To1, Ka Yan Tse1, Nong Yi Cheng1, Yeuk Nam Fan1, Cheuk Lai Lo1, Ka Wa Or1, Man Lok Chan1,  Ka Ching Hui1, Fong Chi Chan1, Wai Ming Hui1,  Lo Kin Ngai1, Kar Ho Lee2,  Kwok Hung Au2,  Wai Yi Yip2,  Yong Zhang3 and  Jing Cai1*
  • 1Hong Kong Polytechnic University, Hong Kong
  • 2Queen Elizabeth Hospital (QEH), Hong Kong
  • 3Xiamen University, China

Background and purpose: Adaptive radiotherapy (ART) can compensate for the dosimetric impacts induced by anatomic and geometric variations in patients with nasopharyngeal carcinoma (NPC); Yet, the need for ART can only be assessed during the radiation treatment and the implementation of ART is resource intensive. Therefore, we aimed to determine tumoral biomarkers using pre-treatment MR images for predicting ART eligibility in NPC patients prior to the start of treatment.
Methods: Seventy patients with biopsy-proven NPC (Stage II-IVB) in 2015 were enrolled into this retrospective study. Pre-treatment contrast-enhanced T1-w (CET1-w), T2-w MR images were processed and filtered using Laplacian of Gaussian (LoG) filter before radiomic features extraction. A total of 479 radiomics features, including the first-order (n=90), shape (n=14) and texture features (n=375), were initially extracted from Gross-Tumor-Volume of primary tumor (GTVnp) using CET1-w, T2-w MR images. Patients were randomly divided into a training set (n=51) and testing set (n=19). The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for radiomic model construction in training set to select the most predictive features to predict patients who were replanned and assessed in the testing set. A double cross-validation approach of 100 resampled iterations with three-fold nested cross-validation was employed in LASSO during model construction. The predictive performance of each model was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC).
Results: In the present cohort, 13 of 70 patients (18.6%) underwent ART. Average AUCs in training and testing sets were 0.962 (95%CI 0.961-0.963) and 0.852 (95%CI 0.847-0.857) with 8 selected features for CET1-w model; 0.895 (95%CI 0.893-0.896) and 0.750 (95%CI 0.745-0.755) with 6 selected features for T2-w model; and 0.984 (95%CI 0.983-0.984) and 0.930 (95%CI 0.928-0.933) with 6 selected features for joint T1-T2 model, respectively. In general, the joint T1-T2 model outperformed either CET1-w or T2-w model alone.
Conclusions: Our study successfully showed promising capability of MRI-based radiomics features for pre-treatment identification of ART eligibility in NPC patients.

Keywords: Radiomics, nasopharyngeal carcinoma, adaptive radiation therapy, Tumor shrinkage, Magnetic Resonance Imaging

Received: 03 Jul 2019; Accepted: 26 Sep 2019.

Copyright: © 2019 Yu, Lam, To, Tse, Cheng, Fan, Lo, Or, Chan, Hui, Chan, Hui, Ngai, Lee, Au, Yip, Zhang and Cai. 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: Dr. Jing Cai, Hong Kong Polytechnic University, Kowloon, Hong Kong,