Use of Radiomics Combined With Machine Learning Method in the Recurrence Patterns After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma: A Preliminary Study

Abstract

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.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.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).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.

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy

“…[ 42 ] Zhang et al found textural features correlate not just to local failure but also to distant treatment failure. [ 37 ] Another study by Li et al showed that 8 radiomics features could differentiate in field recurrence from pre-treatment spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) MRI. [ 34 ] Zhang et al produced a radiomics signature built with 11 features that outperformed conventional clinical variables in predicting local recurrence-free survival in patients with non-metastatic T4 NPC.…”

“…[30,31] Magnetic resonance imaging (MRI) is the imaging of choice in the diagnosis and local staging in NPC due to its superior soft tissue contrast and allows for accurate delineation of target volumes for purposes of radiotherapy. [32][33][34][35][36][37] Whilst some research has been carried out on the application of radiomics in nasopharyngeal cancer, an approach that utilizes MRI radiomics as a predictive signature for intra-tumoral radio-resistance has not yet been developed. Comprehensive image analysis using radiomics that can identify radio-resistant tumor sub-volumes from pre-treatment MRI scans could guide individualized radiation therapy by suggesting target volumes in which a higher dose of radiation is needed for better tumor control.…”

Application of radiomics and machine learning in head and neck cancers

“…Radiomics models are built based on handcrafted features, while deep learning learns the features automatically. In order to have better prediction, many researchers have proposed hybrid prediction models [95][96][97][98]. Another research group used the PyRadiomics platform, and extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment.…”

Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

“…Being able to predict not only the risk of recurrence but also whether it would be locoregional or a distant metastasis (DM) may enhance treatment decisions. As a result, Li et al (82) explored the predictive power of radiomics for the type of recurrence (in field, out of field, marginal). After selection of the influential subset of radiomic features, Kruskal Wallis test and ROC analysis were employed for each feature to assess its capability on in field recurrence prediction.…”