European journal of radiology · 2026
Preoperative prediction of progesterone receptor expression in meningiomas based on diffusion weighted imaging habitat analysis and Transformer-Based deep learning.
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Gui Yuan, Diao Jiaying, Zhang Fang, Hu Wei, Su Ling, Liu Mengjia, Lin Zhiping, Zhang Jing
Аннотация
OBJECTIVE: This study aims to develop and validate a model based on diffusion-weighted imaging (DWI) habitat analysis and Transformer-based deep learning (DL) for preoperative prediction of progesterone receptor (PR) expression in meningiomas.
MATERIALS AND METHODS: This study retrospectively collected data from patients with meningiomas confirmed by surgical pathology from two centers during the period from January 2014 to April 2024. Habitat analysis was used for the non-invasive quantitative measurement of intratumoral heterogeneity (ITH). Ecological diversity features were extracted from the manually segmented tumor volume of interest (VOI), the optimal features were retained using intraclass and interclass correlation coefficients (ICCs), Mann-Whitney U test, support vector machine recursive feature elimination and 5-fold cross-validation, and a random forest (RF) was used to construct the ITH model, which outputs the predicted probability of PR expression in meningiomas. In addition, VOI images were fed into a Transformer-based DL model to generate predicted probabilities. Finally, the predicted probabilities from the ITH and DL models were combined, and a combined model was constructed using logistic regression. Clinical and imaging features were screened via univariate logistic regression and stepwise logistic regression, and a clinical model was constructed using multivariable logistic regression. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and validated in an independent external test set.
RESULTS: Ultimately, 13 habitat features and 4 clinical and imaging features were retained as highly correlated with meningioma PR expression. In both the training and testing sets, the combined model demonstrated the best predictive performance, with AUC values of 0.910 (95 % CI: 0.866, 0.947) and 0.902 (95 % CI: 0.831, 0.957), respectively, and it significantly outperformed the clinical model.
CONCLUSION: The combined model constructed by combining DWI-based ITH and Transformer-based DL demonstrated good predictive performance in preoperative prediction of PR expression in meningiomas.
MATERIALS AND METHODS: This study retrospectively collected data from patients with meningiomas confirmed by surgical pathology from two centers during the period from January 2014 to April 2024. Habitat analysis was used for the non-invasive quantitative measurement of intratumoral heterogeneity (ITH). Ecological diversity features were extracted from the manually segmented tumor volume of interest (VOI), the optimal features were retained using intraclass and interclass correlation coefficients (ICCs), Mann-Whitney U test, support vector machine recursive feature elimination and 5-fold cross-validation, and a random forest (RF) was used to construct the ITH model, which outputs the predicted probability of PR expression in meningiomas. In addition, VOI images were fed into a Transformer-based DL model to generate predicted probabilities. Finally, the predicted probabilities from the ITH and DL models were combined, and a combined model was constructed using logistic regression. Clinical and imaging features were screened via univariate logistic regression and stepwise logistic regression, and a clinical model was constructed using multivariable logistic regression. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and validated in an independent external test set.
RESULTS: Ultimately, 13 habitat features and 4 clinical and imaging features were retained as highly correlated with meningioma PR expression. In both the training and testing sets, the combined model demonstrated the best predictive performance, with AUC values of 0.910 (95 % CI: 0.866, 0.947) and 0.902 (95 % CI: 0.831, 0.957), respectively, and it significantly outperformed the clinical model.
CONCLUSION: The combined model constructed by combining DWI-based ITH and Transformer-based DL demonstrated good predictive performance in preoperative prediction of PR expression in meningiomas.