Abstract
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 3-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.
Highlights
Due to the high proximity of the primary nasopharyngeal carcinoma (NPC) tumor to the surrounding critical organs and metastatic neck lymph nodes, NPC is rarely treated surgically; radiation therapy (RT) remains the mainstay of NPC treatment [1]
The objective of our study was to identify tumoral radiomic features using multi-parametric MR images, which are capable of predicting the Adaptive radiotherapy (ART) eligibility for NPC patients
We successfully revealed the predictive capability of MRI-based radiomics in ART eligibility using our dataset
Summary
Due to the high proximity of the primary NPC tumor to the surrounding critical organs (spinal cord, brainstem, parotid glands) and metastatic neck lymph nodes, NPC is rarely treated surgically; radiation therapy (RT) remains the mainstay of NPC treatment [1]. Patients having significant BW loss tends to accompany with reduced skin separation at various levels of cervical spine and neck [10], causing positional variability due to possible head movement inside the thermoplastic cast Such variations would leave the issue of whether the contour deviations induced significant dose deviations in target or organs at risk. The choice to ART can be resource intensive and time-consuming for repeat imaging, re-contouring, re-planning, and analyzing dosimetric impacts between previous and new treatment plans, adding significant clinical burden and cost of patient care to an oncology center. 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. We aimed to determine tumoral biomarkers using pre-treatment MR images for predicting ART eligibility in NPC patients prior to the start of treatment
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