AI Article Synopsis
- A study was conducted to improve the differentiation between tumor recurrence and pseudoprogression (PsP) in high-grade glioma patients after surgery, focusing on radiomics analysis of tumor and surrounding areas.
- The researchers extracted a total of 1316 features from MRI and ADC maps, using advanced algorithms to select optimal features for creating predictive models.
- The combined analysis of intratumoral and peritumoral features showed significant effectiveness, achieving higher diagnostic performance compared to single-model approaches, with strong AUC values indicating accuracy in distinguishing between recurrence and PsP.
Article Abstract
Background: Distinguishing between tumor recurrence and pseudoprogression (PsP) in high-grade glioma postoperatively is challenging. This study aims to enhance this differentiation using a combination of intratumoral and peritumoral radiomics.
Purpose: To assess the effectiveness of intratumoral and peritumoral radiomics in improving the differentiation between high-grade glioma recurrence and pseudoprogression after surgery.
Material And Methods: A total of 109 cases were randomly divided into training and validation sets, with 1316 features extracted from intratumoral and peritumoral volumes of interest (VOIs) on conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps. Feature selection was performed using the mRMR algorithm, resulting in intratumoral (100 features), peritumoral (100 features), and combined (200 features) subsets. Optimal features were then selected using PCC and RFE algorithms and modeled using LR, SVM, and LDA classifiers. Diagnostic performance was compared using area under the receiver operating characteristic curve (AUC), evaluated in the validation set. A nomogram was established using radscores from intratumoral, peritumoral, and combined models.
Results: The combined model, utilizing 14 optimal features (8 peritumoral, 6 intratumoral) and LR as the best classifier, outperformed the single intratumoral and peritumoral models. In the training set, the AUC values for the combined model, intratumoral model, and peritumoral model were 0.938, 0.921, and 0.847, respectively; in the validation set, the AUC values were 0.841, 0.755, and 0.705. The nomogram model demonstrated AUCs of 0.960 (training set) and 0.850 (validation set).
Conclusion: The combination of intratumoral and peritumoral radiomics is effective in distinguishing high-grade glioma recurrence from pseudoprogression after surgery.
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| http://dx.doi.org/10.1177/02841851241283781 | DOI Listing |
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