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Abstract #3395

Prediction of meningioma brain invasion based on preoperative MRI noninvasive deep transfer learning radiomics model

Dong Yuan1,2,3, Jiajia Zhang1,2, Tianyi Ma1,2, Shengjie Diao1,2, Xianchang Zhang4, and Tong Han1
1Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China, 2Graduate School, Tianjin Medical University, Tianjin, China, 3Department of Radiology, Nayong Xinli Hospital, Bijie,Guizhou, China, 4MR Research Collaboration, Siemens Healthineers Ltd, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software

Motivation: Meningioma brain invasion is a negative prognostic factor closely associated with recurrence, but distinct imaging features on pathology are lacking.

Goal(s): We aimed to uncover the biological behavioral characteristics of meningioma brain invasion embedded in tumor images.

Approach: We applied a deep migratory learning strategy to construct a 2.5D radiomics model using preoperatively contrast-enhanced T1-weighted images to deeply excavate the biological behavioral characteristics of meningioma brain invasion embedded in tumor images.

Results: The combined model incorporating clinical and deep-learning radiomics had the best prediction efficacy for meningioma brain invasion and showed clinical applicability for accurate preoperative predictions.

Impact: Our model, which combines clinical and deep-transfer learning radiomics features, demonstrates high efficacy in predicting brain invasion in meningiomas and may contribute to improved prognoses for patients.

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Keywords