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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.

Background: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time. Here, we present our method that can predict the molecular subtype and grade, while simultaneously providing a delineation of the tumor., Methods: We developed a single multi-task convolutional neural network that uses the full 3D, structural, preoperative MRI scans to predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using a patient cohort containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes., Results: In the independent test set, we achieved an IDH-AUC of 0.90, an 1p/19q co-deletion AUC of 0.85, and a grade AUC of 0.81 (grade II/III/IV). For the tumor delineation, we achieved a mean whole tumor Dice score of 0.84., Conclusions: We developed a method that non-invasively predicts multiple, clinically relevant features of glioma. Evaluation in an independent dataset shows that the method achieves a high performance and that it generalizes well to the broader clinical population. This first-of-its-kind method opens the door to more generalizable, instead of hyper-specialized, AI methods., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Background: Extensive resections in low-grade glioma (LGG) are associated with improved overall survival (OS). However, World Health Organization (WHO) classification of gliomas has been completely revised and is now predominantly based on molecular criteria. This requires reevaluation of the impact of surgery in molecularly defined LGG subtypes., Methods: We included 228 adults who underwent surgery since 2003 for a supratentorial LGG. Pre- and postoperative tumor volumes were assessed with semiautomatic software on T2-weighted images. Targeted next-generation sequencing was used to classify samples according to current WHO classification. Impact of postoperative volume on OS, corrected for molecular profile, was assessed using a Cox proportional hazards model., Results: Median follow-up was 5.79 years. In 39 (17.1%) histopathologically classified gliomas, the subtype was revised after molecular analysis. Complete resection was achieved in 35 patients (15.4%), and in 54 patients (23.7%) only small residue (0.1-5.0 cm3) remained. In multivariable analysis, postoperative volume was associated with OS, with a hazard ratio of 1.01 (95% CI: 1.002-1.02; P = 0.016) per cm3 increase in volume. The impact of postoperative volume was particularly strong in isocitrate dehydrogenase (IDH) mutated astrocytoma patients, where even very small postoperative volumes (0.1-5.0 cm) already negatively affected OS., Conclusion: Our data provide the necessary reevaluation of the impact of surgery in molecularly defined LGG and support maximal resection as first-line treatment for molecularly defined LGG. Importantly, in IDH mutated astrocytoma, even small postoperative volumes have negative impact on OS, which argues for a second-look operation in this subtype to remove minor residues if safely possible., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)