Blood vessel anastomosis is spatially regulated by Flt1 during angiogenesis

Blood vessel formation is essential for vertebrate development and is primarily achieved by angiogenesis – endothelial cell sprouting from pre-existing vessels. Vessel networks expand when sprouts form new connections, a process whose regulation is poorly understood. Here, we show that vessel anastomosis is spatially regulated by Flt1 (VEGFR1), a VEGFA receptor that acts as a decoy receptor. In vivo, expanding vessel networks favor interactions with Flt1 mutant mouse endothelial cells. Live imaging in human endothelial cells in vitro revealed that stable connections are preceded by transient contacts from extending sprouts, suggesting sampling of potential target sites, and lowered Flt1 levels reduced transient contacts and increased VEGFA signaling. Endothelial cells at target sites with reduced Flt1 and/or elevated protrusive activity were more likely to form stable connections with incoming sprouts. Target cells with reduced membrane-localized Flt1 (mFlt1), but not soluble Flt1, recapitulated the bias towards stable connections, suggesting that relative mFlt1 expression spatially influences the selection of stable connections. Thus, sprout anastomosis parameters are regulated by VEGFA signaling, and stable connections are spatially regulated by endothelial cell-intrinsic modulation of mFlt1, suggesting new ways to manipulate vessel network formation

Flt-1 (VEGFR-1) coordinates discrete stages of blood vessel formation

In developing blood vessel networks, the overall level of vessel branching often correlates with angiogenic sprout initiations, but in some pathological situations, increased sprout initiations paradoxically lead to reduced vessel branching and impaired vascular function. We examine the hypothesis that defects in the discrete stages of angiogenesis can uniquely contribute to vessel branching outcomes

Toward improving the diagnosis of glioma

Glioma is a heterogeneous and incurable neoplastic mass derived from the glial cells in the brain. The clinical course of a glial tumor can range from slow growing to highly aggressive and it is driven by the genetic and epigenetic alterations within the neoplastic cells’ DNA. In order to diagnose both that a patient has a glioma and what kind of glioma it may be, it is necessary to acquire magnetic resonance images (MRI) of the brain. MRI not only provides unparalleled soft tissue contrast in the brain compared with other tomographic imaging techniques (e.g. CT), it is also incredibly flexible: in addition to structural anatomy of the lesion, it can probe the physiology (e.g. diffusion, perfusion) and metabolism of the brain. Together, these data guide neuroradiologists and neurooncologists to provide the optimal treatment plan for a patient with glioma. Even with the most talented clinicians and the highest-quality MR acquisitions, there still exist issues along the trajectory of diagnosing a glioma. In this dissertation, I harnessed the incredibly rich biological information within the pixels of MRI with modern advances in data science and computer vision to address three of the most urgent problems along the diagnostic pipeline: 1) Can we identify a patient’s glioma subtype prior to surgical intervention?; 2) Can we create an automatic MR dashboard for clinicians to monitor patient disease over time?; and 3) When a patient appears to recur, is it a true glioma or the effect of radiation therapy

Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning

Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 weighted, T1 post-contrast, T2 weighted, T2-weighted FLAIR, proton-density weighted. Prior studies have investigated using imaging-based methods and DICOM metadata-based methods with success on cohorts of patients acquired as part of a clinical trial. This study compares the performance of these methods on heterogeneous clinical datasets acquired with many different scanners from many institutions. RF and CNN models were trained on metadata and pixel data, respectively. A combined RF model incorporated CNN logits from the pixel-based model together with metadata. Four cohorts were used for model development and evaluation: MS research (n = 11,106 series), MS clinical (n = 3244 series), glioma research (n = 612 series, test/validation only), and ADNI PTSD (n = 477 series, training only). Together, these cohorts represent a broad range of acquisition contexts (scanners, sequences, institutions) and subject pathologies. Pixel-based CNN and combined models achieved accuracies between 97 and 98% on the clinical MS cohort. Validation/test accuracies with the glioma cohort were 99.7% (metadata only) and 98.4 (CNN). Accurate and generalizable classification of MRI acquisition contrast types was demonstrated. Such methods are important for enabling automated data selection in high-throughput and big-data image analysis applications

Topographic clinical insights from deep learning-based geographic atrophy progression prediction

To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate. Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experiments investigated the contribution of imaging features to the performance of the algorithms. Three FAF image regions were defined relative to GA: Lesion, Rim, and Background. For No Lesion, No Rim, and No Background datasets, a single region of interest was removed at a time. For Lesion, Rim, and Background Shuffled datasets, individual region pixels were randomly shuffled. For Lesion, Rim, and Background Mask datasets, masks of the regions were used. A Convex Hull dataset was generated to evaluate the importance of lesion size. Squared Pearson correlation (r2) was used to compare the predictive performance of ablated datasets relative to the benchmark. The Rim region influenced r2 more than the other two regions in all experiments, indicating the most relevant contribution of this region to the performance of the algorithms. In addition, similar performance was observed for all regions when pixels were shuffled or only a mask was used, indicating intensity information was not independently informative without textural context. These ablation experiments enabled topographic clinical insights on FAF images from a DL-based GA progression prediction algorithm. Results from this study may lead to new insights on GA progression prediction

Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment

Flt-1 (VEGFR-1) coordinates discrete stages of blood vessel formation

AIMS: In developing blood vessel networks, the overall level of vessel branching often correlates with angiogenic sprout initiations, but in some pathological situations, increased sprout initiations paradoxically lead to reduced vessel branching and impaired vascular function. We examine the hypothesis that defects in the discrete stages of angiogenesis can uniquely contribute to vessel branching outcomes. METHODS AND RESULTS: Time-lapse movies of mammalian blood vessel development were used to define and quantify the dynamics of angiogenic sprouting. We characterized the formation of new functional conduits by classifying discrete sequential stages—sprout initiation, extension, connection, and stability—that are differentially affected by manipulation of vascular endothelial growth factor-A (VEGF-A) signalling via genetic loss of the receptor flt-1 (vegfr1). In mouse embryonic stem cell-derived vessels genetically lacking flt-1, overall branching is significantly decreased while sprout initiations are significantly increased. Flt-1(−/−) mutant sprouts are less likely to retract, and they form increased numbers of connections with other vessels. However, loss of flt-1 also leads to vessel collapse, which reduces the number of new stable conduits. Computational simulations predict that loss of flt-1 results in ectopic Flk-1 signalling in connecting sprouts post-fusion, causing protrusion of cell processes into avascular gaps and collapse of branches. Thus, defects in stabilization of new vessel connections offset increased sprout initiations and connectivity in flt-1(−/−) vascular networks, with an overall outcome of reduced numbers of new conduits. CONCLUSIONS: These results show that VEGF-A signalling has stage-specific effects on vascular morphogenesis, and that understanding these effects on dynamic stages of angiogenesis and how they integrate to expand a vessel network may suggest new therapeutic strategies

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging

BackgroundDiagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.MethodsOur dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models.ResultsThe best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging.ConclusionTraining a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups