Dataset Information

Functional Connectivity Density for Radiation Encephalopathy Prediction in Nasopharyngeal Carcinoma.

ABSTRACT: The diagnostic efficiency of radiation encephalopathy (RE) remains heterogeneous, and prediction of RE is difficult at the pre-symptomatic stage. We aimed to analyze the whole-brain resting-state functional connectivity density (FCD) of individuals with pre-symptomatic RE using multivariate pattern analysis (MVPA) and explore its prediction efficiency. Resting data from NPC patients with nasopharyngeal carcinoma (NPC; consisting of 20 pre-symptomatic RE subjects and 26 non-RE controls) were collected in this study. We used MVPA to classify pre-symptomatic RE subjects from non-RE controls based on FCD maps. Classifier performances were evaluated by accuracy, sensitivity, specificity, and area under the characteristic operator curve. Permutation tests and leave-one-out cross-validation were applied for assessing classifier performance. MVPA was able to differentiate pre-symptomatic RE subjects from non-RE controls using global FCD as a feature, with a total accuracy of 89.13%. The temporal lobe as well as regions involved in the visual processing system, the somatosensory system, and the default mode network (DMN) revealed robust discrimination during classification. Our findings suggest a good classification efficiency of global FCD for the individual prediction of RE at a pre-symptomatic stage. Moreover, the discriminating regions may contribute to the underlying mechanisms of sensory and cognitive disturbances in RE.

SUBMITTER: Zhao LM

PROVIDER: S-EPMC8311791 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The natural course of radiation encephalopathy following nasopharyngeal carcinoma (NPC) radiotherapy remains poorly understood. The present study aimed to investigate the magnetic resonance imaging (MRI) characteristics and evolution of radiation encephalopathy. A series of 162 follow-up MRI examinations from 68 NPC patients with radiation encephalopathy in the temporal lobes were analyzed retrospectively. Each component of radiation encephalopathy was defined as follows: i) contrast enhanced lesions were enhanced lesions on contrast enhanced T1-weighted images (T1WI); ii) white matter lesions were lesions of homogeneous hyperintensity on T2-weighted images (T2WI) and hypointensity on T1WI; iii) cysts were round or oval well-defined lesions of hyperintensity on T2WI; iv) hemosiderin deposition were nodular or annular hypointense lesions with lower hypointense than normal white matter on both T1WI and T2WI; v) gray matter lesions were defined as disruption or erosion of hyperintensity in the cortex on T2WI. Contrast enhanced lesions, white matter lesions, gray matter lesions, cysts and hemosiderin deposition were detected in 105 (100.0%), 98 (93.3%), 94 (89.5%), 2 (1.7%) and 2 (1.7%) cases of the 105 initial diagnosed temporal lobe lesions. Contrast enhanced lesions were the most commonly observed, followed by white matter lesions, gray matter lesions, temporal lobe atrophy, cysts and hemosiderin deposition. In addition, 12 new lesions were identified during the follow-up, 4 of which presented as solid enhanced nodular lesions. Importantly, in 11 of the 117 (9.4%) affected temporal lobes, solid enhanced nodular lesions were observed to be the only initial abnormalities to occur. For those enhanced nodular lesions measuring <0.8 cm, no necrosis could be detected. On the contrary, all the contrast enhanced lesions measuring >2.0 cm exhibited a necrotic core. To the best of our knowledge, the present study revealed for the first time solid enhanced nodular lesions as the earliest MRI abnormalities of radiation encephalopathy following NPC radiotherapy.

Project description: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.

Dataset Information

Functional Connectivity Density for Radiation Encephalopathy Prediction in Nasopharyngeal Carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure