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This is a Quantitative Study study.

Machine learning based MRI radiomics model in predicting postoperative severe poor outcomes after resection of meningioma.

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Novel radiomics model predicts severe poor outcomes in meningioma patients, highlighting enhanced clinical decision-making.

The novel model predicts severe poor outcomes after meningioma resection, improving early risk identification.
Using radiomics features from tumor regions, the model achieved AUC values of 0.88 and 0.87 in training and validation sets, respectively.
Review of 148 patients with meningiomas formed the basis for building the predictive model using machine learning techniques.
This approach could significantly enhance postoperative management and outcomes for patients undergoing meningioma resection.

Tan et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Novel radiomics model predicts severe poor outcomes in meningioma patients, highlighting enhanced clinical decision-making.

The novel model predicts severe poor outcomes after meningioma resection, improving early risk identification.
Using radiomics features from tumor regions, the model achieved AUC values of 0.88 and 0.87 in training and validation sets, respectively.
Review of 148 patients with meningiomas formed the basis for building the predictive model using machine learning techniques.
This approach could significantly enhance postoperative management and outcomes for patients undergoing meningioma resection.

Tan et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark