Evaluating the predictive value of glioma growth models for low-grade glioma after tumor resection

• Published in: IEEE transactions on medical imaging 2024-01, Vol.43 (1), p.1-1
• Main Authors: van Garderen, Karin A., van der Voort, Sebastian R., Wijnenga, Maarten M.J., Incekara, Fatih, Alafandi, Ahmad, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J.P.E., van den Bent, Martin, French, Pim J., Smits, Marion, Klein, Stefan
• Format: Article
• Language: English
• Subjects: Anisotropy; Brain; Brain modeling; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain Neoplasms - surgery; Data models; Diffusion; Diffusion Tensor Imaging - methods; Glioma; Glioma - diagnostic imaging; Glioma - pathology; Glioma - surgery; Glioma cells; Growth model; Growth models; Humans; Imaging; Magnetic Resonance Imaging; Measurement; Medical imaging; Metastases; Performance prediction; Predictive models; Substantia alba; Tensors; Tumors; Validation
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2023.3298637

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of the spatial pattern that does not require a volume cut-off value. Using the AP metric, we evaluate diffusion-proliferation models informed by structural MRI and DTI, after tumor resection. We applied the models to a unique longitudinal dataset of 14 patients with low-grade glioma (LGG), who received no treatment after surgical resection, to predict the recurrent tumor shape after tumor resection. The diffusion models informed by structural MRI and DTI showed a small but significant increase in predictive performance with respect to homogeneous isotropic diffusion, and the DTI-informed model reached the best predictive performance. We conclude there is a significant improvement in the prediction of the recurrent tumor shape when using a DTI-informed anisotropic diffusion model with respect to istropic diffusion, and that the AP is a suitable metric to evaluate these models. All code and data used in this publication are made publicly available.