10.3389/fonc.2018.00648.s001
Shuangshuang Li
Shuangshuang
Li
Kongcheng Wang
Kongcheng
Wang
Zhen Hou
Zhen
Hou
Ju Yang
Ju
Yang
Wei Ren
Wei
Ren
Shanbao Gao
Shanbao
Gao
Fanyan Meng
Fanyan
Meng
Puyuan Wu
Puyuan
Wu
Baorui Liu
Baorui
Liu
Juan Liu
Juan
Liu
Jing Yan
Jing
Yan
Data_Sheet_1_Use of Radiomics Combined With Machine Learning Method in the Recurrence Patterns After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma: A Preliminary Study.docx
Frontiers
2018
intensity-modulated radiotherapy
nasopharyngeal carcinoma
recurrence pattern
radiomic analysis
prediction
2018-12-21 04:16:48
Dataset
https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Use_of_Radiomics_Combined_With_Machine_Learning_Method_in_the_Recurrence_Patterns_After_Intensity-Modulated_Radiotherapy_for_Nasopharyngeal_Carcinoma_A_Preliminary_Study_docx/7496897
<p>Objective: To analyze the recurrence patterns and reasons in patients with nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT) and to investigate the feasibility of radiomics for analysis of radioresistance.</p><p>Methods: We analyzed 306 NPC patients treated with IMRT from Jul-2009 to Aug-2016, 20 of whom developed with recurrence. For the NPCs with recurrence, CT, MR, or PET/CT images of recurrent disease were registered with the primary planning CT for dosimetry analysis. The recurrences were defined as in-field, marginal or out-of-field, according to dose-volume histogram (DVH) of the recurrence volume. To explore the predictive power of radiomics for NPCs with in-field recurrences (NPC-IFR), 16 NPCs with non-progression disease (NPC-NPD) were used for comparison. For these NPC-IFRs and NPC-NPDs, 1117 radiomic features were quantified from the tumor region using pre-treatment spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI). Intraclass correlation coefficients (ICC) and Pearson correlation coefficient (PCC) was calculated to identify influential feature subset. Kruskal-Wallis test and receiver operating characteristic (ROC) analysis were employed to assess the capability of each feature on NPC-IFR prediction. Principal component analysis (PCA) was performed for feature reduction. Artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM) models were trained and validated by using stratified 10-fold cross validation.</p><p>Results: The median follow up was 26.5 (range 8–65) months. 9/20 (45%) occurred in the primary tumor, 8/20 (40%) occurred in regional lymph nodes, and 3/20 (15%) patients developed a primary and regional failure. Dosimetric and target volume analysis of the recurrence indicated that there were 18 in-field, and 1 marginal as well as 1 out-of-field recurrence. With pre-therapeutic SPAIR T2W MRI images available, 11 NPC-IFRs (11 of 18 NPC-IFRs who had available pre-therapeutic MRI) and 16 NPC-NPDs were subsequently employed for radiomic analysis. Results showed that NPC-IFRs vs. NPC-NPDs could be differentiated by 8 features (AUCs: 0.727–0.835). The classification models showed potential in prediction of NPC-IFR with higher accuracies (ANN: 0.812, KNN: 0.775, SVM: 0.732).</p><p>Conclusion: In-field and high-dose region relapse were the main recurrence patterns which may be due to the radioresistance. After integration in the clinical workflow, radiomic analysis can be served as imaging biomarkers to facilitate early salvage for NPC patients who are at risk of in-field recurrence.</p>