Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study.

BACKGROUND: Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability. METHODS: We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n = 88) including four centers. RESULTS: Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07). CONCLUSION: Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions.

Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans.

OBJECTIVES: Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop and validate a generative adversarial network for synthesizing missing sequences to allow for a robust automated segmentation. MATERIALS AND METHODS: Our model was trained on data from The Cancer Imaging Archive (n = 238 WHO II-IV gliomas) to synthesize either missing FLAIR, T2-weighted, T1-weighted (T1w), or contrast-enhanced T1w images from available sequences, using a novel tumor-targeting loss to improve synthesis of tumor areas. We validated performance in a test set from both the REMBRANDT repository and our local institution (n = 68 WHO II-IV gliomas), using qualitative image appearance metrics, but also segmentation performance with state-of-the-art segmentation models. Segmentation of synthetic images was compared with 2 commonly used strategies for handling missing input data, entering a blank mask or copying an existing sequence. RESULTS: Across tumor areas and missing sequences, synthetic images generally outperformed both conventional approaches, in particular when FLAIR was missing. Here, for edema and whole tumor segmentation, we improved the Dice score, a common metric for evaluation of segmentation performance, by 12% and 11%, respectively, over the best conventional method. No method was able to reliably replace missing contrast-enhanced T1w images. DISCUSSION: Replacing missing nonenhanced magnetic resonance sequences via synthetic images significantly improves segmentation quality over most conventional approaches. This model is freely available and facilitates more widespread use of automated segmentation in routine clinical use, where missing sequences are common.

CXCR4-Targeted PET Imaging of Central Nervous System B-Cell Lymphoma.

C-X-C chemokine receptor 4 (CXCR4) is a transmembrane chemokine receptor involved in growth, survival, and dissemination of cancer, including aggressive B-cell lymphoma. MRI is the standard imaging technology for central nervous system (CNS) involvement of B-cell lymphoma and provides high sensitivity but moderate specificity. Therefore, novel molecular and functional imaging strategies are urgently required. Methods: In this proof-of-concept study, 11 patients with lymphoma of the CNS (8 primary and 3 secondary involvement) were imaged with the CXCR4-directed PET tracer 68Ga-pentixafor. To evaluate the predictive value of this imaging modality, treatment response, as determined by MRI, was correlated with quantification of CXCR4 expression by 68Ga-pentixafor PET in vivo before initiation of treatment in 7 of 11 patients. Results:68Ga-pentixafor PET showed excellent contrast with the surrounding brain parenchyma in all patients with active disease. Furthermore, initial CXCR4 uptake determined by PET correlated with subsequent treatment response as assessed by MRI. Conclusion:68Ga-pentixafor PET represents a novel diagnostic tool for CNS lymphoma with potential implications for theranostic approaches as well as response and risk assessment.

c-Rel gain in B cells drives germinal center reactions and autoantibody production.

Single-nucleotide polymorphisms and locus amplification link the NF-κB transcription factor c-Rel to human autoimmune diseases and B cell lymphomas, respectively. However, the functional consequences of enhanced c-Rel levels remain enigmatic. Here, we overexpressed c-Rel specifically in mouse B cells from BAC-transgenic gene loci and demonstrate that c-Rel protein levels linearly dictated expansion of germinal center B (GCB) cells and isotype-switched plasma cells. c-Rel expression in B cells of otherwise c-Rel-deficient mice fully rescued terminal B cell differentiation, underscoring its critical B cell-intrinsic roles. Unexpectedly, in GCB cells transcription-independent regulation produced the highest c-Rel protein levels among B cell subsets. In c-Rel-overexpressing GCB cells this caused enhanced nuclear translocation, a profoundly altered transcriptional program, and increased proliferation. Finally, we provide a link between c-Rel gain and autoimmunity by showing that c-Rel overexpression in B cells caused autoantibody production and renal immune complex deposition.

Towards quantitative imaging biomarkers of tumor dissemination: A multi-scale parametric modeling of multiple myeloma.

The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for personalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longitudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk-assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease requiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model parameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for analyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.