Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI

Hoffmann, Emily; Gerwing, Mirjam; Niland, Stephan; Niehoff, Rolf; Masthoff, Max; Geyer, Christiane; Wachsmuth, Lydia; Wilken, Enrica; Höltke, Carsten; Heindel, Walter; Hoerr, Verena; Schinner, Regina; Berger, Philipp; Vogl, Thomas J.; Eble, Johannes A.; Maus, Bastian; Helfen, Anne; Wildgruber, Moritz; Faber, Cornelius

doi:10.1136/jitc-2022-006092

Cited by 10 publications

(27 citation statements)

References 49 publications

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“…We continue by presenting the different liver dMRI biophysical models examined in this article. All models account for restricted diffusion inside spherical cells and hindered diffusion in the extra-cellular space – a common modelling framework in body dMRI ( 6, 19, 22 ). Models can be grouped into two families (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The latest liver dMRI signal models account for two key signal compartments, in that they attempt to disentangle intra-cellular and extra-cellular water contributions (6,19,22,23).…”

Section: Discussionmentioning

confidence: 99%

“…We synthesised signals for a substrate made of packed spherical cells (Fig. S5), a common geometric tissue model used for body dMRI imaging (6,19,22,23), Fig. 4 shows excellent co-localisation between MRI slices and histology sections.…”

Section: Computer Simulations Confirm Model Selection From Actual Mri...mentioning

confidence: 99%

“…The most recent biophysical dMRI techniques describe the non-vascular liver tissue signal as the sum of contributions from intra-cellular and extra-cellular water (19)(20)(21). While these models provide promising readouts of tumour microenvironments (22), their practical use in real-world settings is made unfeasible by i) the high number of dMRI images (and hence long scan time) required for model fitting, and by ii) the availability of specialised dMRI acquisitions (23), beyond standard examinations that MRI manufactures make available in the scanner console. In this study we aim to tackle this unmet need by delivering a practical liver dMRI signal model that is truly feasible in hospital settings, i.e., on 1.5T or 3T systems, with scan time that does not exceed 15 minutes, and using vendor-provided dMRI sequences.…”

Section: Introductionmentioning

confidence: 99%

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Clinically feasible liver tumour cell size measurement through histology-informedin vivodiffusion MRI

Grussu,

Bernatowicz,

Palombo

et al. 2024

Preprint

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Innovative diffusion Magnetic Resonance Imaging (dMRI) models enable in vivo mapping of biologically meaningful properties such as cell size, potential biomarkers in cancer. However, while cancers frequently spread to the liver, models tailored for liver applications and easy to deploy in the clinic are still sought. We tackle this unmet need by delivering a practical and clinically viable liver dMRI modelling framework. Through direct comparison of candidate dMRI approaches in mouse models and cancer patients' data, we select a model of intra-cellular diffusion fitted to highly diffusion-weighted images, as it provides the strongest radiological-histological correlates. We demonstrate the potential application of the proposed model in cancer immunotherapy, stratifying the risk of progression based on baseline tumour cell size/density from dMRI. This result, heretofore unreported and not achievable with standard dMRI indices (e.g., apparent diffusion coefficient), suggests that our approach may become a useful tool for precision imaging in oncology.

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Section: Resultsmentioning

confidence: 99%

“…The latest liver dMRI signal models account for two key signal compartments, in that they attempt to disentangle intra-cellular and extra-cellular water contributions (6,19,22,23).…”

Section: Discussionmentioning

confidence: 99%

Section: Computer Simulations Confirm Model Selection From Actual Mri...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinically feasible liver tumour cell size measurement through histology-informedin vivodiffusion MRI

Grussu,

Bernatowicz,

Palombo

et al. 2024

Preprint

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“…Multiple sampling is unavailable in daily practice, which leads to a lack of spatial and temporal information. Considering that Treg interactions in TME have an immense impact on patients' outcomes, imaging of Treg infiltration is highly desired 18 . Several preclinical trials for in vivo TIL imaging were applied with radiotracers or nanoparticles, whose injection is not always practical 19 Alternatively, radiomics would allow the assessment of TME, spatial heterogeneity, and longitudinal evolution.…”

Section: Discussionmentioning

confidence: 99%

Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics

Jiang,

Wu,

Yang

et al. 2023

Cancer Medicine

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ObjectiveTo generate an image‐driven biomarker (Rad_score) to predict tumor‐infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC).MethodsOverall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients.ResultsLandmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622–0.867) and 0.733 (95% CI 0.535–0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657–0.885) and 0.724 (95% CI 0.522–0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1.ConclusionsHigh Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment.

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Advanced breast diffusion-weighted imaging: what are the next steps? A proposal from the EUSOBI International Breast Diffusion-weighted Imaging working group

Honda,

Sigmund,

Le Bihan

et al. 2024

Eur Radiol

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Objectives This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. Methods A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation. Results Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice. Conclusion The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment. Clinical relevance statement Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice. Key Points Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.

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Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI

Cited by 10 publications

References 49 publications

Clinically feasible liver tumour cell size measurement through histology-informedin vivodiffusion MRI

Clinically feasible liver tumour cell size measurement through histology-informedin vivodiffusion MRI

Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics

Advanced breast diffusion-weighted imaging: what are the next steps? A proposal from the EUSOBI International Breast Diffusion-weighted Imaging working group

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