Next-generation imaging biomarkers in neuro-oncology using artificial intelligence: overcoming key challenges towards clinically applicable AI

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Over the past decades, tremendous progress has been made in improving cancer patient treatment and outcomes. Brain tumors, which are the focus of this proposal, are however still associated with a predominantly poor prognosis due to their enormous heterogeneity and underlying complex biology. However, hope lies in individualized and molecularly targeted treatment approaches. In this context, it has become critical to develop accurate and broadly applicable biomarkers to assess the efficacy of these novel therapies. Magnetic resonance imaging (MRI) is of particular importance in this regard, and recent advances in the field of artificial intelligence (AI) have demonstrated remarkable progress in the quantitative analysis of radiological image data. In this project, we will build on and further expand our key developments from the first funding period of SPP-2177, with the goal of closing the gap towards implementing clinically applicable AI in the field of brain tumor imaging. By leveraging a large-scale multimodal data resource in neuro-oncology (with longitudinal multiparametric MRI, molecular and clinical data) with more than 5000 patients from previously conducted prospective multicenter clinical studies in neuro-oncology, the following key-objectives will be address within our project: (1) to further improve the performance, generalizability and clinical utility of our previously developed state-of-the-art AI models for brain tumor segmentation model for quantitative tumor response assessment through implementation of temporally consistent, uncertainty aware and continual learning models; (2) to implement novel privacy-preserving AI techniques which will allow to overcome the need of data-sharing between institutions when using multi-institutional data for the development of AI models based on diverse population samples; and (3) to implement interpretable predictions of AI models and thereby addressing the “black-box” nature of AI based classification models. In summary, the anticipated developments will not only address the key challenges towards achieving clinically applicable AI in the field of brain tumor imaging, but also serve as a blueprint for the meaningful application of AI in the field of radiology.