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Assessing the Efficacy of Radiotherapy for Brain Metastases in Advanced Non-Small Cell Lung Cancer through Raidomics Prediction

Author:Li Yang  Ai Kai  Cheng Yongjun  Gao Bo  

Session Type:Digital Poster  

Session Date:Wednesday, 08 May 2024  

Session Name:AI in Brain Tumor Prediction  

Program Number:3782  

Room Session:Exhibition Hall (Hall 403)  

Institution:Philips Healthcare  The Affiliated Hospital of Guzhou Medical Unversity  

Deep learning radiomics nomograms predict IDH genotype in glioma patients: a multicenter study

Author:Darui Li  Jing Zhang  Kai Ai  Wanjun Hu  Guangyao Liu  Laiyang Ma  Tiejun Gan  

Session Type:Digital Poster  

Session Date:Wednesday, 08 May 2024  

Session Name:AI in Brain Tumor Prediction  

Program Number:3781  

Room Session:Exhibition Hall (Hall 403)  

Institution:lanzhou university second hospital  Philips Healthcare  

Detection of Early-stage Primary Central Nervous System Lymphoma Manifesting Atypical Radiological Phenotype Using Multi-Task Neural Network

A fusion model based on preoperative MRI radiomics features can predict potential bone invasion of meningiomas

Identification of Glioblastoma Infiltrative Areas in Peritumoral Edema Based on Expert Interaction Framework

Machine learning based characterisation of glioma shows best performance with post-contrast T1 and diffusion imaging

Machine learning based MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma

Machine Learning for Preoperative Prediction of EGFR Mutation in Lung Cancer Brain Metastasis

Mathematical Modelling of Survival in Low Grade Gliomas at Malignant Transformation with XGBoost.

Author:Lily Tan  James Ruffle  Rees Jeremy  Michael Kosmin  Parashkev Nachev  Harpreet Hyare  

Session Type:Digital Poster  

Session Date:Wednesday, 08 May 2024  

Session Name:AI in Brain Tumor Prediction  

Program Number:3775  

Room Session:Exhibition Hall (Hall 403)  

Institution:UCL  

Quantitative Physiologic MRI Parameters Combined with Innovative Machine Learning to Distinguish Glioblastoma from Solitary Brain Metastases

Radiomics Features on Magnetic Resonance Images Can Predict C5aR1 Expression Levels and Prognosis in High-Grade Glioma.

Author:Zijun Wu  Yuan Yang  Yunfei Zha  

Session Type:Digital Poster  

Session Date:Wednesday, 08 May 2024  

Session Name:AI in Brain Tumor Prediction  

Program Number:3780  

Room Session:Exhibition Hall (Hall 403)  

Institution:Renmin Hospital of Wuhan University  

A Subregion-based RadioFusionOmics Model Discriminates between Grade 4 Astrocytoma and Glioblastoma on Multisequence MRI

Utilizing 2D UNet with Synthetic Attention for Enhanced Classification of IDH Mutations Based on Anatomical MRI in Gliomas