How AI-based techniques for brain tumour diagnosis can address data variability.

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Abstract

Data variability in medical imaging of tumors refers to the inconsistencies in Magnetic Resonance Imaging and Computational Tomography scans caused by heterogeneous tumor biology, varying scanner hardware, and subjective human interpretation. This variability creates significant challenges for accurate diagnoses, treatment tracking, and AI model development. Although the variability in medical imaging data for diagnostic and prognostic purposes is a challenge for AI methods applied to other types of tumors as well, it is particularly problematic when the analysis involves gliomas and glioblastomas. Most gliomas are infiltrative and glioblastomas are highly infiltrative tumors with poorly defined margins. They exhibit significant regional variations, including cystic necrosis, localized hemorrhages, and complex microvascular proliferation. Addressing this variability requires a combination of algorithmic innovation, standardized datasets, and a leading-edge computational pipeline including traditional and AI-based data modelling methodologies to ensure diagnostic and prognostic models are robust. In this article, we will identify some AI methods that show promise in the analysis of brain tumor images and that must overcome the challenges posed by data availability. We will also offer some insights into how these challenges can be overcome and propose a possible computational pipeline that utilizes them.

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