Abstract #0868

Deep Learning Based Multi-Scale Approach for Precision Medicine and Quantitative Imaging in Glioblastoma

Anum Masood¹, Usman Naseem², Junaid Rashid³, Euijoon Ahn², Mehmood Nawaz⁴, and Mehwish Nasim⁵

¹Radiology, Harvard Medical School, Boston Children's Hospital, Boston, MA, United States, ²James Cook University, James Cook University, Townsville, Australia, ³Department of Data Science, Sejong University, Seoul, Korea, Republic of, ⁴Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ⁵School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Australia

Synopsis

Keywords: Diagnosis/Prediction, PET/MR, Glioblastoma, WSI

Motivation: Glioblastoma (GBM) is a fast-growing invasive brain tumor that presents unique treatment challenges. Early diagnosis requires manual segmentation using MRI and histopathological image analysis.

Goal(s): Our proposed model can facilitate medical personnel in an efficient and accurate diagnosis of glioblastoma.

Approach: We present a multiscale multilevel approach based on deep learning for precision medicine and quantitative imaging in GBM capturing image feature and providing wide-ranging contextual information.

Results: Our method predicted the overall survival of GMB patients with an average accuracy of 88.63% and 91.7% DSC (Unet: 84% DSC; Swin Transformer: 87% DSC) on BraTS 2020.

Impact: Our model surpasses state-of-the-art methods in Glioblastoma (GBM) segmentation and predicts patient survival with 88.63% accuracy. This research work assists in precise and efficient diagnoses of GBM, potentially contributing to early disease detection and treatment strategies.

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