Impact of ComBat and a Multi-Model approach to deal with multi-scanner and missing MRI data in a small cohort study. Application to H3K27M mutation prediction in patients with DIPG.

Abstract

Radiomics was proposed to identify tumor phenotypes non-invasively from quantitative imaging features. Calculating a large amount of information on images, allows the development of reliable classification models. In multi-modal imaging protocols, the question arises of adding an imaging modality to improve model performance. In addition, in the implementation of clinical protocols, some modalities are not acquired or are of insufficient quality and cannot be reliably taken into account. Furthermore, multi-scanner studies generate some variability in the acquisition and data. Some methodological solutions using ComBat and a multi-model approach were tested to take these two issues into account. It was applied to a cohort of 88 patients with Diffuse Intrinsic Pontine Glioma (DIPG). Sixteen models using radiomic features computed using 0, 1, 2, 3 or 4 MRI modalities were proposed. Based on Leave-One-Out Cross-Validation, F1 weighted scores ranged from 0.66 to 0.85. A model of majority voting using the prediction of all the models available for one given patient was finally applied, reducing drastically the number of unclassified patients.Clinical relevance- In case of patients with DIPG, the prediction of H3 mutation is of prime importance in case of inconclusive biopsy or in the absence of it. It could suggest orientations for new chemotherapy drugs associated with the radiation therapy.