Work

Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs

Year: 2024

Type: book-chapter

Abstract: INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15-18 months. Therefore, it is helpful to be able to assess a patient's OS to tailor treatment more specifically to the course of the disease. Automated analysis of routinely generated MRI sequences (FLAIR, T1, T1CE, and T2) using deep learning-based image classification has the potential to enable accurate OS predictions. METHODS: In this work, a method was developed and evaluated that classifies the OS into three classes - "short", "medium" and "long". For this purpose, the four MRI sequences of a person were corrected using bias-field correction and merged into one image. The pipeline was realized by a bagging model using 5-fold cross-validation and the ResNet50 architecture. RESULTS: The best model was able to achieve an F1-score of 0.51 and an accuracy of 0.67. In addition, this work enabled a largely clear differentiation of the "short" and "long" classes, which offers high clinical significance as decision support. CONCLUSION: Automated analysis of MRI scans using deep learning-based image classification has the potential to enable accurate OS prediction in glioblastomas.

Source: Studies in health technology and informatics

DOI: 10.3233/shti240878

Authors: Katharina Ott, Santiago Cepeda, Dennis Hartmann, Frank Kramer, Dominik Müller

Institutions: University of Augsburg, Hospital Universitario Río Hortega, University Hospital Augsburg

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4402279030

FWCI: 15.45

Cites:

Cited by: 6

Related to: 10