An Automated Brain Metastasis Detection and Segmentation System from MRI with a Large Multi-Institutional Dataset

PURPOSE/OBJECTIVE(S)

Developments of automated systems for brain metastasis (BM) detection and segmentation from MRI for assisting early detection and stereotactic radiosurgery (SRS) have been reported but most based upon relatively small datasets from single institutes. This work aims to develop and evaluate a system using a large multi-institutional dataset, and to improve both identification of small/subtle BMs and segmentation accuracy of large BMs.

MATERIALS/METHODS

A 3D U-Net system was trained and evaluated to detect and segment intraparenchymal BMs with a size > 2mm using 1856 MRI volumes from 1791 patients treated with SRS from seven institutions (1539 volumes for training, 183 for validation, and 134 for testing). All patients had 3D post-Gd T1w MRI scans pre-SRS. Gross tumor volumes (GTVs) of BMs for SRS were curated by each institute first. Then, additional efforts were spent to create GTVs for the untreated and/or uncontoured BMs, including central reviews by two radiologists, to improve accuracy of ground truth. The training dataset was augmented with synthetic BMs of 3773 MRIs using a 3D generative pipeline. Our system consists of two U-Nets with one using small 3D patches dedicated for detecting small BMs and another using large 3D patches for segmenting large BMs, and a random-forest based fusion module for combining the two network outputs. The first U-Net was trained with 3D patches containing at least one BM < 0.1 cm3. For detection performance, we measured BM-level sensitivity and case-level false-positive (FP) rate. For segmentation performance, we measured BM-level Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95). We also stratified performances based upon BM sizes.

RESULTS

For 739 BMs in the 134 testing cases, the overall lesion-level sensitivity was 0.870 with an average case-level FP of 1.34±1.92 (95% CI: 1.02-1.67). The sensitivity was >0.969 for the BMs >0.1 cm3, but dropped to 0.755 for the BMs < 0.1 cm3 (Table 1). The average DSC and HD95 for all detected BMs were 0.786 and 1.35mm. The worse performance for BMs > 20 cm3 was caused by a case with 83 cm3 GTV and artifacts in the MRI volume.

CONCLUSION

We achieved excellent detection sensitivity and segmentation accuracy for BMs > 0.1 cm3, and promising performance for small BMs (<0.1cm3) with a controlled FP rate using a large multi-institutional dataset. Clinical utility for assisting early detection and SRS planning will be investigated. Table 1: Per-lesion detection and segmentation performance stratified by individual BM size. N is the number of BMs in each category.

Software Components and Product Variety in a Platform Ecosystem: A Dynamic Network Analysis of WordPress

Software components such as application programming interfaces (APIs) provided by external developers are vital to online digital platforms. Although APIs generally increase the variety of products according to anecdote, the precise relationship between the categories of APIs and product variety is not yet known. We find that APIs, regarding their use frequency, are categorized into three groups. The core is a group of frequently used APIs, whereas the periphery is a group of sparsely used APIs. In a large and mature platform ecosystem, an additional group of APIs, the regular core, mainly provided by third-party developers, emerges. APIs in the regular core are the main driver of product variety. However, we also find that the strength of this effect diminishes in a newly created product category when most of the new products are built by duplicating the usage of APIs from other products. A platform owner can stimulate developers’ creativity by acting as a bridge between digital product providers and third-party developers. It can collect functional needs from third-party developers and then share them with product providers. Therefore, the latter can build APIs that developers need.

Contrastive self-supervised learning from 100 million medical images with optional supervision

Purpose

Building accurate and robust artificial intelligence systems for medical image assessment requires the creation of large sets of annotated training examples. However, constructing such datasets is very costly due to the complex nature of annotation tasks, which often require expert knowledge (e.g., a radiologist). To counter this limitation, we propose a method to learn from medical images at scale in a self-supervised way.

Approach

Our approach, based on contrastive learning and online feature clustering, leverages training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasonography (US). We propose to use the learned features to guide model training in supervised and hybrid self-supervised/supervised regime on various downstream tasks.

Results

We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT, and MR: (1) significant increase in accuracy compared to the state-of-the-art (e.g., area under the curve boost of 3% to 7% for detection of abnormalities from chest radiography scans and hemorrhage detection on brain CT); (2) acceleration of model convergence during training by up to 85% compared with using no pretraining (e.g., 83% when training a model for detection of brain metastases in MR scans); and (3) increase in robustness to various image augmentations, such as intensity variations, rotations or scaling reflective of data variation seen in the field.

Conclusions

The proposed approach enables large gains in accuracy and robustness on challenging image assessment problems. The improvement is significant compared with other state-of-the-art approaches trained on medical or vision images (e.g., ImageNet).

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans

Purpose

To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools.

Materials and Methods

This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; n = 25 946) and evaluation (n = 2947) and three "external" centers for calibration (n = 400) and evaluation (n = 16764). Internal centers contributed developmental data, whereas external centers did not. Deep neural networks predicted the presence of ICH and subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and/or epidural hemorrhage) and segmentations per case. Two ICH confidence scores are discussed: a calibrated classifier entropy score and a Dempster-Shafer score. Evaluation was completed by using receiver operating characteristic curve analysis and report turnaround time (RTAT) modeling on the evaluation set and on confidence score-defined subsets using bootstrapping.

Results

The areas under the receiver operating characteristic curve for ICH were 0.97 (0.97, 0.98) and 0.95 (0.94, 0.95) on internal and external center data, respectively. On 80% of the data stratified by calibrated classifier and Dempster-Shafer scores, the system improved the Youden indexes, increasing them from 0.84 to 0.93 (calibrated classifier) and from 0.84 to 0.92 (Dempster-Shafer) for internal centers and increasing them from 0.78 to 0.88 (calibrated classifier) and from 0.78 to 0.89 (Dempster-Shafer) for external centers (P < .001). Models estimated shorter RTAT for AI-prioritized worklists with confidence measures than for AI-prioritized worklists without confidence measures, shortening RTAT by 27% (calibrated classifier) and 27% (Dempster-Shafer) for internal centers and shortening RTAT by 25% (calibrated classifier) and 27% (Dempster-Shafer) for external centers (P < .001).

Conclusion

AI that provided statistical confidence measures for ICH detection on NCCT scans reliably detected and subtyped hemorrhages, identified high-confidence predictions, and improved worklist prioritization in simulation.Keywords: CT, Head/Neck, Hemorrhage, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

The Unknowability of Autonomous Tools and the Liminal Experience of Their Use

Recently companies are increasingly adopting intelligent technologies, such as autonomous tools and artificial intelligence, to assist complex knowledge works that are traditionally carried out by human experts. These tools can independently learn and execute novel actions. The input–output relationships of these tools, however, are unknowable to human experts. This calls for analysis of how humans may work differently while interacting with such tools. To this end, we conduct a comparative case study at one of the world’s largest semiconductor manufacturers. We investigate how chip designers interact with two families of design technologies: one following a traditional designer-centric approach in which the designer knows what outputs particular inputs to the tools will generate, and another relying on autonomous tools that continually surprise the user. Our inquiry reveals that, when using autonomous tools, designers can hardly understand the design generated by the tools and become more like laboratory experimentalists in that their primary job is experimenting with different inputs and assessing the quality of the finished design. Their interactions with the tools are marked by ambiguity, and the design is moved forward along multiple design trajectories in accordance with a multifarious temporality.

The role of intraoperative frozen section analysis in joint arthroplasty with CD66b immunohistochemical staining

The preoperative diagnosis of infection during joint arthroplasty is important for clinical management. However, the evaluation of polymorphonuclear leukocytes (PMNs) during frozen section analysis is sometimes difficult due to frozen artifacts. In the present study, we sought to investigate the utility of intraoperative fresh frozen section (FFS) examination for diagnosis of infection and to evaluate whether the neutrophil-specific surface marker CD66b helps to improve the diagnostic accuracy of infection. A consecutive series of 65 original frozen sections at the time of resection arthroplasty was retrospectively reviewed compared with corresponding permanent sections. The presence of PMNs was determined using intraoperative FFS and permanent sections. Furthermore, CD66b staining was performed to identify PMNs clearly. The ratio of male to female patients was 21:42. The mean age was 70 years. Postoperatively, 25 of 65 cases were histologically diagnosed with infection (25/65; 39%). The sensitivity and specificity of intraoperative FFS relative to permanent section histology were 100% (25/25) and 95% (38/40), respectively. Among 40 patients without infection, two showed false-positive results during intraoperative FFS diagnosis (2/40, 5%). In addition, on CD66b staining, six cases (9%) experienced changes in results, which altered the sensitivity and specificity of intraoperative FFS compared with permanent histology only to 87% and 87%, respectively. In conclusion, the diagnostic performance of intraoperative FFS is high and comparable to yields of permanent section histology. Therefore, intraoperative FFS is highly suitable diagnostic method for detection of infection during joint arthroplasty. And CD66b immunostaining facilitates delicate identification of PMNs, especially in equivocal cases.

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing

We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing.

Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images

Purpose: We investigate the impact of various deep-learning-based methods for detecting and segmenting metastases with different lesion volume sizes on 3D brain MR images. Approach: A 2.5D U-Net and a 3D U-Net were selected. We also evaluated weak learner fusion of the prediction features generated by the 2.5D and the 3D networks. A 3D fully convolutional one-stage (FCOS) detector was selected as a representative of bounding-box regression-based detection methods. A total of 422 3D post-contrast T1-weighted scans from patients with brain metastases were used. Performances were analyzed based on lesion volume, total metastatic volume per patient, and number of lesions per patient. Results: The performance of detection of the 2.5D and 3D U-Net methods had recall of > 0.83 and precision of > 0.44 for lesion volume > 0.3    cm 3 but deteriorated as metastasis size decreased below 0.3    cm 3 to 0.58 to 0.74 in recall and 0.16 to 0.25 in precision. Compared the two U-Nets for detection capability, high precision was achieved by the 2.5D network, but high recall was achieved by the 3D network for all lesion sizes. The weak learner fusion achieved a balanced performance between the 2.5D and 3D U-Nets; particularly, it increased precision to 0.83 for lesion volumes of 0.1 to 0.3    cm 3 but decreased recall to 0.59. The 3D FCOS detector did not outperform the U-Net methods in detecting either the small or large metastases presumably because of the limited data size. Conclusions: Our study provides the performances of four deep learning methods in relationship to lesion size, total metastasis volume, and number of lesions per patient, providing insight into further development of the deep learning networks.

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.

Exploring the Contribution of Myelin Content in Normal Appearing White Matter to Cognitive Outcomes in Cerebral Small Vessel Disease

BACKGROUND

Myelin damage is a salient feature in cerebral small vessel disease (cSVD). Of note, myelin damage extends into the normal appearing white matter (NAWM). Currently, the specific role of myelin content in cognition is poorly understood.

OBJECTIVE

The objective of this exploratory study was to investigate the association between NAWM myelin and cognitive function in older adults with cSVD.

METHODS

This exploratory study included 55 participants with cSVD. NAWM myelin was measured using myelin water imaging and was quantified as myelin water fraction (MWF). Assessment of cognitive function included processing speed (Trail Making Test Part A), set shifting (Trail Making Test Part B minus A), working memory (Verbal Digit Span Backwards Test), and inhibition (Stroop Test). Multiple linear regression analyses assessed the contribution of NAWM MWF on cognitive outcomes controlling for age, education, and total white matter hyperintensity volume. The overall alpha was set at ≤0.05.

RESULTS

After accounting for age, education, and total white matter hyperintensity volume, lower NAWM MWF was significantly associated with slower processing speed (β  = -0.29, p = 0.037) and poorer working memory (β= 0.30, p = 0.048). NAWM MWF was not significantly associated with set shifting or inhibitory control (p > 0.132).

CONCLUSION

Myelin loss in NAWM may play a role in the evolution of impaired processing speed and working memory in people with cSVD. Future studies, with a longitudinal design and larger sample sizes, are needed to fully elucidate the role of myelin as a potential biomarker for cognitive function.

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology

INTRODUCTION

During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood.

METHODS

GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure.

RESULTS

In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively.

CONCLUSIONS

This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

PURPOSE

To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

MATERIALS AND METHODS

In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobewise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April, 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.

RESULTS

Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO(P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.

CONCLUSION

A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

Quantifying and leveraging predictive uncertainty for medical image assessment

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy. Finally, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

PURPOSE

To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

MATERIALS AND METHODS

In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.

RESULTS

Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO (P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.

CONCLUSION

A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

Brain Myelin Water Fraction and Diffusion Tensor Imaging Atlases for 9-10 Year-Old Children

BACKGROUND AND PURPOSE

Myelin water imaging (MWI) and diffusion tensor imaging (DTI) provide information about myelin and axon-related brain microstructure, which can be useful for investigating normal brain development and many childhood brain disorders. While pediatric DTI atlases exist, there are no pediatric MWI atlases available for the 9-10 years old age group. As myelination and structural development occurs throughout childhood and adolescence, studies of pediatric brain pathologies must use age-specific MWI and DTI healthy control data. We created atlases of myelin water fraction (MWF) and DTI metrics for healthy children aged 9-10 years for use as normative data in pediatric neuroimaging studies.

METHODS

3D-T₁ , DTI, and MWI scans were acquired from 20 healthy children (mean age: 9.6 years, range: 9.2-10.3 years, 4 females). ANTs and FSL registration were used to create quantitative MWF and DTI atlases. Region of interest (ROI) analysis in nine white matter regions was used to compare pediatric MWF with adult MWF values from a recent study and to investigate the correlation between pediatric MWF and DTI metrics.

RESULTS

Adults had significantly higher MWF than the pediatric cohort in seven of the nine white matter ROIs, but not in the genu of the corpus callosum or the cingulum. In the pediatric data, MWF correlated significantly with mean diffusivity, but not with axial diffusivity, radial diffusivity, or fractional anisotropy.

CONCLUSIONS

Normative MWF and DTI metrics from a group of 9-10 year old healthy children provide a resource for comparison to pathologies. The age-specific atlases are ready for use in pediatric neuroimaging research and can be accessed: https://sourceforge.net/projects/pediatric-mri-myelin-diffusion/.

Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS

OBJECTIVE

To summarize current and emerging imaging techniques that can be used to assess neuroprotection and repair in multiple sclerosis (MS), and to provide a consensus opinion on the potential utility of each technique in clinical trial settings.

METHODS

Clinicians and scientists with expertise in the use of MRI in MS convened in Toronto, Canada, in November 2016 at a North American Imaging in Multiple Sclerosis (NAIMS) Cooperative workshop meeting. The discussion was compiled into a manuscript and circulated to all NAIMS members in attendance. Edits and feedback were incorporated until all authors were in agreement.

RESULTS

A wide spectrum of imaging techniques and analysis methods in the context of specific study designs were discussed, with a focus on the utility and limitations of applying each technique to assess neuroprotection and repair. Techniques were discussed under specific themes, and included conventional imaging, magnetization transfer ratio, diffusion tensor imaging, susceptibility-weighted imaging, imaging cortical lesions, magnetic resonance spectroscopy, PET, advanced diffusion imaging, sodium imaging, multimodal techniques, imaging of special regions, statistical considerations, and study design.

CONCLUSIONS

Imaging biomarkers of neuroprotection and repair are an unmet need in MS. There are a number of promising techniques with different strengths and limitations, and selection of a specific technique will depend on a number of factors, notably the question the trial seeks to answer. Ongoing collaborative efforts will enable further refinement and improved methods to image the effect of novel therapeutic agents that exert benefit in MS predominately through neuroprotective and reparative mechanisms.

Whole-exome sequencing and immune profiling of early-stage lung adenocarcinoma with fully annotated clinical follow-up

Background

Lung adenocarcinomas (LUADs) lead to the majority of deaths attributable to lung cancer. We performed whole-exome sequencing (WES) and immune profiling analyses of a unique set of clinically annotated early-stage LUADs to better understand the pathogenesis of this disease and identify clinically relevant molecular markers.

Methods

We performed WES of 108 paired stage I-III LUADs and normal lung tissues using the Illumina HiSeq 2000 platform. Ten immune markers (PD-L1, PD-1, CD3, CD4, CD8, CD45ro, CD57, CD68, FOXP3 and Granzyme B) were profiled by imaging-based immunohistochemistry (IHC) in a subset of LUADs (n = 92). Associations among mutations, immune markers and clinicopathological variables were analyzed using ANOVA and Fisher's exact test. Cox proportional hazards regression models were used for multivariate analysis of clinical outcome.

Results

LUADs in this cohort exhibited an average of 243 coding mutations. We identified 28 genes with significant enrichment for mutation. SETD2-mutated LUADs exhibited relatively poor recurrence- free survival (RFS) and mutations in STK11 and ATM were associated with poor RFS among KRAS-mutant tumors. EGFR, KEAP1 and PIK3CA mutations were predictive of poor response to adjuvant therapy. Immune marker analysis revealed that LUADs in smokers and with relatively high mutation burdens exhibited increased levels of immune markers. Analysis of immunophenotypes revealed that LUADs with STK11 mutations exhibited relatively low levels of infiltrating CD4+/CD8+ T-cells indicative of a muted immune response. Tumoral PD-L1 was significantly elevated in TP53 mutant LUADs whereas PIK3CA mutant LUADs exhibited markedly down-regulated PD-L1 expression. LUADs with TP53 or KEAP1 mutations displayed relatively increased CD57 and Granzyme B levels indicative of augmented natural killer (NK) cell infiltration.

Conclusion(s)

Our study highlights molecular and immune phenotypes that warrant further analysis for their roles in clinical outcomes and personalized immune-based therapy of LUAD.

SATB2-associated syndrome presenting with Rett-like phenotypes

The SATB2-associated syndrome (SAS) was proposed recently, after the SATB2 gene was initially discovered to be associated with isolated cleft palate. This syndrome is characterized by intellectual disability with delayed speech development, facial dysmorphism, cleft or high-arched palate, and dentition problems. Here, we describe two novel SATB2 sequence variants in two unrelated patients presenting with Rett-like phenotypes. We performed trio-based whole-exome sequencing in a 17-month-old girl presenting with severe retardation and Rett-like phenotypes, which revealed a de novo missense variant in SATB2 (p.Glu396Gln). Moreover, targeted sequencing of the SATB2 gene was performed in a 2-year-old girl with severe psychomotor retardation, facial hypotonia, and cleft palate who also exhibited some features of Rett syndrome. A nonsense variant in SATB2 was identified in this patient (p.Arg459*). This study expanded the clinical and genetic spectrum of SAS. SATB2 variants should be considered in cases with psychomotor retardation alone or in any cases with Rett-like phenotypes, regardless of the typical features of SAS such as cleft palate.

Role of second-look ultrasound examinations for MR-detected lesions in patients with breast cancer

PURPOSE

To assess the clinical value of second-look ultrasound (US) examination for the evaluation of additional enhancing lesions detected on magnetic resonance (MR) imaging.

MATERIALS AND METHODS

Between May 2008 and February 2011, 794 consecutive patients with histologically confirmed breast cancer underwent breast MR imaging. We included 101 patients with 132 additional enhancing breast lesions detected on MR imaging who underwent second-look US.  The imaging features and lesion category according to the Breast Imaging and Reporting and Data System (BI-RADS) were assessed with MR and US imaging, respectively.

RESULTS

According to the BI-RADS system, 67 lesions (50.8 %) were classified as category 0, 33 lesions (25.0 %) as category 3, and 32 lesions (24.2 %) as category 4. Of the 67 indeterminate lesions on MR imaging, 34 (50.7 %) were demonstrated on second-look US. 11 of these 34 lesions showed suspicious sonographic features, including 1 lesion that showed malignancy (9.1 %, 1/11). Most of the suspicious lesions on MR imaging (26 of 32 BI-RADS category 4 lesions, 81.3 %) were demonstrated on second-look US, and 17 were malignant (65.4 %, 17/26). Of the 6 BI-RADS category 4 lesions without sonographic correlation, 1 was malignant (16.7 %, 1/6).

CONCLUSION

Second-look US examination was useful for evaluating MR-detected lesions in patients with breast cancer.

Non-local spatial regularization of MRI T2 relaxation images for myelin water quantification

Myelin is an essential component of nerve fibers and monitoring its health is important for studying diseases that attack myelin, such as multiple sclerosis (MS). The amount of water trapped within myelin, which is a surrogate for myelin content and integrity, can be measured in vivo using MRI relaxation techniques that acquire a series of images at multiple echo times to produce a T2 decay curve at each voxel. These curves are then analyzed, most commonly using non-negative least squares (NNLS) fitting, to produce T2 distributions from which water measurements are made. NNLS is unstable with respect to the noise and variations found in typical T2 relaxation images, making some form of regularization inevitable. The current methods of NNLS regularization for measuring myelin water have two key limitations: 1) they use strictly local neighborhood information to regularize each voxel, which limits their effectiveness for very noisy images, and 2) the neighbors of each voxel contribute to its regularization equally, which can over-smooth fine details. To overcome these limitations, we propose a new regularization algorithm in which local and non-local information is gathered and used adaptively for each voxel. Our results demonstrate that the proposed method provides more globally consistent myelin water measurements yet preserves fine structures. Our experiment with real patient data also shows that the algorithm improves the ability to distinguish two sample groups, one of MS patients and the other of healthy subjects.

Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning

Changes in brain morphology and white matter lesions are two hallmarks of multiple sclerosis (MS) pathology, but their variability beyond volumetrics is poorly characterized. To further our understanding of complex MS pathology, we aim to build a statistical model of brain images that can automatically discover spatial patterns of variability in brain morphology and lesion distribution. We propose building such a model using a deep belief network (DBN), a layered network whose parameters can be learned from training images. In contrast to other manifold learning algorithms, the DBN approach does not require a prebuilt proximity graph, which is particularly advantageous for modeling lesions, because their sparse and random nature makes defining a suitable distance measure between lesion images challenging. Our model consists of a morphology DBN, a lesion DBN, and a joint DBN that models concurring morphological and lesion patterns. Our results show that this model can automatically discover the classic patterns of MS pathology, as well as more subtle ones, and that the parameters computed have strong relationships to MS clinical scores.

The analgesic effect of remifentanil on prevention of withdrawal response associated with the injection of rocuronium in children: no evidence for a peripheral action

Remifentanil pre-treatment has been reported to decrease the incidence of rocuronium injection-associated withdrawal movement. This study was designed to explore the site of action of remifentanil for reducing withdrawal response during rocuronium injection in children. Ninety-six paediatric patients were randomly assigned to three groups. After induction of anaesthesia with 5 mg/kg 2.5% thiopental sodium, 2 ml saline (placebo group) or 0.5 microg/kg remifentanil (group R), was injected intravenously 1 min before 0.6 mg/kg rocuronium. In a third group (group R'), the venous retention of 0.5 microg/kg remifentanil was maintained for 30 s followed by tourniquet release and injection of 0.6 mg/kg rocuronium. Withdrawal response was graded using a four-point scale. The incidence of withdrawal movement after rocuronium administration was 94%, 22% and 81% in the placebo, R, and R' groups, respectively. This study demonstrated that the pre-treatment effect of remifentanil for reducing rocuronium-associated withdrawal response occurs mainly through a central action.