Use of Radiomics Combined With Machine Learning Method in the Recurrence Patterns After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma: A Preliminary Study - Li, Shuangshuang; Wang, Kongcheng; Hou, Zhen; Yang, Ju; Ren, Wei; Gao, Shanbao; Meng, Fanyan; Wu, Puyuan; Liu, Baorui; Liu, Juan; Yan, Jing (Frontiers in Oncology, 2018)
Objective To analyze the recurrence patterns and reasons in patients with head and neck cancer(HNC) treated with intensity-modulated radiotherapy(IMRT) and to investigate the feasibility of radiomics for analysis of nasopharyngeal carcinoma(NPC) radioresistance. Methods We analyzed 504 HNC patients treated with IMRT from Jul-2009 to Aug-2016, 26 of whom developed with recurrence. For the HNCs 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. Results The median follow up was 26(range3-65) months. 13/26(50%) occurred in the primary tumor, 8/26(31%) occurred in regional lymph nodes, and 5/26(19%) patients developed a primary and regional failure. Dosimetric and target volume analysis of the recurrence indicated that there were 24 in-field, and 1 marginal as well as 1 out-of-field recurrence. Among the HNCs with recurrence, 20 NPCs developed in-field failure(NPC-IFR). With pre-therapeutic SPAIR T2W MRI images available, 11 NPC-IFRs(11 of 20 NPC-IFRs who had available pre-therapeuticMRI) and 16 NPC-NPDs were subsequently employed for radiomic analysis. Results showed that NPC-IFRs versus 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). 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.