Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction

Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The complex interactions between neoplastic cells and normal tissue, as well as the treatment-induced changes often encountered, make glioma tumor growth modeling challenging. In this paper, we present a novel end-to-end network capable of generating future tumor masks and realistic MRIs of how the tumor will look at any future time points for different treatment plans. Our approach is based on cutting-edge diffusion probabilistic models and deep-segmentation neural networks. We included sequential multi-parametric magnetic resonance images (MRI) and treatment information as conditioning inputs to guide the generative diffusion process. This allows for tumor growth estimates at any given time point. We trained the model using real-world postoperative longitudinal MRI data with glioma tumor growth trajectories represented as tumor segmentation maps over time. The model has demonstrated promising performance across a range of tasks, including the generation of high-quality synthetic MRIs with tumor masks, time-series tumor segmentations, and uncertainty estimates. Combined with the treatment-aware generated MRIs, the tumor growth predictions with uncertainty estimates can provide useful information for clinical decision-making.Comment: 13 pages, 10 figures, 2 tables, 2 agls, preprints in the IEEE trans. format for submission to IEEE-TM

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

IntroductionManagement of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences.MethodsWe adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives.ResultsThe 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively.Discussion/ConclusionOur results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio

Magnetic resonance imaging provides evidence of glymphatic drainage from human brain to cervical lymph nodes

Pre-clinical research in rodents provides evidence that the central nervous system (CNS) has functional lymphatic vessels. In-vivo observations in humans, however, are not demonstrated. We here show data on CNS lymphatic drainage to cervical lymph nodes in-vivo by magnetic resonance imaging (MRI) enhanced with an intrathecal contrast agent as a cerebrospinal fluid (CSF) tracer. Standardized MRI of the intracranial compartment and the neck were acquired before and up to 24–48 hours following intrathecal contrast agent administration in 19 individuals. Contrast enhancement was radiologically confirmed by signal changes in CSF nearby inferior frontal gyrus, brain parenchyma of inferior frontal gyrus, parahippocampal gyrus, thalamus and pons, and parenchyma of cervical lymph node, and with sagittal sinus and neck muscle serving as reference tissue for cranial and neck MRI acquisitions, respectively. Time series of changes in signal intensity shows that contrast enhancement within CSF precedes glymphatic enhancement and peaks at 4–6 hours following intrathecal injection. Cervical lymph node enhancement coincides in time with peak glymphatic enhancement, with peak after 24 hours. Our findings provide in-vivo evidence of CSF tracer drainage to cervical lymph nodes in humans. The time course of lymph node enhancement coincided with brain glymphatic enhancement rather than with CSF enhancement

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

Abstract Background Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans. Methods A Flangeless Esser PET Phantom with eight hollow spheres (4–25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data. Results In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki. Conclusion In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300–500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors. Trial registration Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO

Survival associations using perfusion and diffusion magnetic resonance imaging in patients with histologic and genetic defined diffuse glioma World Health Organization grades II and III

Objective: According to the new World Health Organization 2016 classification for tumors of the central nervous system, 1p/19q codeletion defines the genetic hallmark that differentiates oligodendrogliomas from diffuse astrocytomas. The aim of our study was to evaluate whether relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) histogram analysis can stratify survival in adult patients with genetic defined diffuse glioma grades II and III. Methods: Sixty-seven patients with untreated diffuse gliomas World Health Organization grades II and III and known 1p/19q codeletion status were included retrospectively and analyzed using ADC and rCBV maps based on whole-tumor volume histograms. Overall survival and progression-free survival (PFS) were analyzed by using Kaplan-Meier and Cox survival analyses adjusted for known survival predictors. Results: Significant longer PFS was associated with homogeneous rCBV distribution–higher rCBVpeak (median, 37 vs 26 months; hazard ratio [HR], 3.2; P = 0.02) in patients with astrocytomas, and heterogeneous rCBV distribution–lower rCBVpeak (median, 46 vs 37 months; HR, 5.3; P < 0.001) and higher rCBVmean (median, 44 vs 39 months; HR, 7.9; P = 0.003) in patients with oligodendrogliomas. Apparent diffusion coefficient parameters (ADCpeak, ADCmean) did not stratify PFS and overall survival. Conclusions: Tumors with heterogeneous perfusion signatures and high average values were associated with longer PFS in patients with oligodendrogliomas. On the contrary, heterogeneous perfusion distribution was associated with poor outcome in patients with diffuse astrocytomas

Impaired verbal learning is associated with larger caudate volumes in early onset schizophrenia spectrum disorders

Background Both brain structural abnormalities and neurocognitive impairments are core features of schizophrenia. We have previously reported enlargements in subcortical brain structure volumes and impairment of neurocognitive functioning as measured by the MATRICS Cognitive Consensus Battery (MCCB) in early onset schizophrenia spectrum disorders (EOS). To our knowledge, no previous study has investigated whether neurocognitive performance and volumetric abnormalities in subcortical brain structures are related in EOS. Methods Twenty-four patients with EOS and 33 healthy controls (HC) were included in the study. Relationships between the caudate nucleus, the lateral and fourth ventricles volumes and neurocognitive performance were investigated with multivariate linear regression analyses. Intracranial volume, age, antipsychotic medication and IQ were included as independent predictor-variables. Results The caudate volume was negatively correlated with verbal learning performance uniquely in the EOS group (r=-.454, p=.034). There were comparable positive correlations between the lateral ventricular volume and the processing speed, attention and reasoning and problem solving domains for both the EOS patients and the healthy controls. Antipsychotic medication was related to ventricular enlargements, but did not affect the brain structure-function relationship. Conclusion Enlargement of the caudate volume was related to poorer verbal learning performance in patients with EOS. Despite a 32% enlargement of the lateral ventricles in the EOS group, associations to processing speed, attention and reasoning and problem solving were similar for both the EOS and the HC groups

Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach

Purpose According to the revised World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) of 2016, oligodendrogliomas are now defined primarily by a specific molecular signature (presence of IDH mutation and 1p19q codeletion). The purpose of our study was to assess the value of dynamic susceptibility contrast MR imaging (DSC-MRI) and diffusion-weighted imaging (DWI) to characterize oligodendrogliomas and to distinguish them from astrocytomas. Methods Seventy-one adult patients with untreated WHO grade II and grade III diffuse infiltrating gliomas and known 1p/19q codeletion status were retrospectively identified and analyzed using relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) maps based on whole-tumor volume histograms. The Mann-Whitney U test and logistic regression were used to assess the ability of rCBV and ADC to differentiate between oligodendrogliomas and astrocytomas both independently, but also related to the WHO grade. Prediction performance was evaluated in leave-one-out cross-validation (LOOCV). Results Oligodendrogliomas showed significantly higher microvascularity (higher rCBVMean ≥ 0.80, p = 0.013) and higher vascular heterogeneity (lower rCBVPeak ≤ 0.044, p = 0.015) than astrocytomas. Diffuse gliomas with higher cellular density (lower ADCMean ≤ 1094 × 10−6 mm2/s, p = 0.009) were more likely to be oligodendrogliomas than astrocytomas. Histogram analysis of rCBV and ADC was able to differentiate between diffuse astrocytomas (WHO grade II) and anaplastic astrocytomas (WHO grade III). Conclusion Histogram-derived rCBV and ADC parameter may be used as biomarkers for identification of oligodendrogliomas and may help characterize diffuse gliomas based upon their genetic characteristics. Keywords Diffuse glioma Perfusion MRI Diffusion MR