Development and validation of a robust MRI-based nomogram incorporating radiomics and deep features for preoperative glioma grading: a multi-center study

Background: Gliomas, the most common primary brain tumors, are classified into low-grade glioma (LGG) and high-grade glioma (HGG) based on aggressiveness. Accurate preoperative differentiation is vital for effective treatment and prognosis, but traditional methods like biopsy have limitations, such as sampling errors and procedural risks. This study introduces a comprehensive model that combines radiomics features (RFs) and deep features (DFs) from magnetic resonance imaging (MRI) scans, integrating clinical factors with advanced imaging features to enhance diagnostic precision for preoperative glioma grading. Methods: In this retrospective multi-center study [2017-2022], 582 patients underwent preoperative contrast-enhanced T1-weighted (CE-T1w) and T2-weighted fluid-attenuated inversion recovery (T2w FLAIR) MRI. The dataset, divided into 407 training and 175 testing cases, included 340 LGGs and 242 HGGs. RFs and DFs were extracted from CE-T1w images, and radiomic scores (rad-score) and deep scores (deep-score) were calculated. Additionally, a clinical model based on demographics and MRI findings (CE-T1w and T2w FLAIR imaging) was developed. A nomogram model integrating rad-score, deep-score, and clinical factors was constructed using multivariate logistic regression analysis. Decision curve analysis (DCA) was employed to evaluate the nomogram's clinical utility in distinguishing between HGGs and LGGs. Results: The study included 582 patients (mean age: 52±14 years; 57.91% male). No significant differences in age or sex were found between the training and testing groups (P>0.05). For RFs, 73.02% of the 215 extracted features were selected based on inter-class correlation coefficients (ICCs), while for DFs, 38.27% of the 15,680 extracted features were selected. Optimal penalization coefficients lambda (λ) for RFs and DFs were determined using a five-fold cross-validation and minimal criteria process. The resulting receiver operating characteristic-area under the curve (ROC-AUC) values were 0.93 [95% confidence interval (CI): 0.91-0.94] for the training set and 0.91 (95% CI: 0.89-0.93) for the testing set. The Hosmer-Lemeshow test yielded P values of 0.619 and 0.547 for the training and testing sets, respectively, indicating satisfactory calibration. The nomogram demonstrated the highest net benefit (NB) up to a threshold of 0.7, followed by DFs and RFs. Conclusions: This study underscores the efficacy of integrating RFs and DFs alongside clinical data to accurately predict the pathological grading of HGGs and LGGs, offering a comprehensive approach for clinical decision-making.

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