ESTRO 2024 - Abstract Book

S4530

Physics - Machine learning models and clinical applications

ESTRO 2024

Keywords: Glioma, Prognosis, Pseudoprogression

References:

[1] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. 2020.

[2] Baid U, Ghodasara S, Bilello M, et al. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv pre-print server. 2021.

[3] Menze BH, Jakab A, Bauer S, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging. 2015; 34(10):1993-2024.

[4] Bakas S, Akbari H, Sotiras A, et al. Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data. 2017; 4(1):170117.

[5] Bakas S, Sako C, Akbari H, et al. The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics. Scientific data. 2022; 9(1):453.

[6] Akbari, Hamed, et al. "Histopathology ‐ validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo ‐ progression in glioblastoma." Cancer 126.11 (2020): 2625-2636.

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