Methodological aspects of correlative, multimodal, multiparametric breast cancer imaging: from data acquisition to image processing for AI-based radioproteomics in a preclinical setting - Bartsch, Silvester J.; Brožová, Klára; Fürböck, Christoph; Friske, Joachim; Laimer-Gruber, Daniela; Helbich, Thomas H.; Hacker, Marcus; Kuntner, Claudia; Kratochwill, Klaus; Kenner, Lukas; Langs, Georg; Pinker, Katja; et al. (Frontiers in Biomaterials Science, 2024)
Preclinical high-field magnetic resonance imaging (MRI) systems offer a diverse array of MRI techniques, providing rich multiparametric MRI platforms for studying numerous biological parameters. Multiparametric MRI platforms prove particularly indispensable when investigating tumors exhibiting profound intratumoral heterogeneity, such as breast cancer. A thoughtful comprehension of the origins of intratumoral heterogeneity is imperative for the judicious assessment of forthcoming targeted therapies and treatment interventions. Furthermore, when data from mpMRI are complemented with data from other in vivo imaging modalities, such as positron emission tomography (PET), and correlated with data from ex vivo modalities such as matrix-assisted laser desorption imaging mass spectrometry (MALDI IMS), the in vivo parameters can be further elucidated at molecular levels and microscopic scales. Nevertheless, extracting meaningful scientific insights from such complex datasets necessitates the utilization of machine learning (ML) approaches to discern region-specific radiomic features. The development of correlative, multimodal imaging (CMI) workflows, such as one incorporating MRI, PET and MALDI IMS, is inherently challenging, given the many technological and methodological challenges related to multimodal data acquisition as well as limitations given by the physiology of the investigated laboratory mice. Standardization efforts in image acquisition and processing are required to increase reproducibility and translatability of CMI data. To address the challenges of developing standardized CMI workflows and stimulate dialogue regarding this area of need, we present a practical workflow to investigate tumor heterogeneity in breast cancer xenografts across various spatial scales. Our workflow entails simultaneous functional MRI and PET acquisitions in living mice, followed by correlation with post-imaging MALDI IMS and histologic data. Additionally, we propose data preprocessing steps for potential ML applications. We illustrate the feasibility of this workflow through two examples, showcasing its effectiveness in comparing in vivo and ex vivo images to evaluate tumor metabolism and hypoxia in mice bearing breast cancer xenografts.