The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis

Convolutional neural networks (CNNs) have shown promising performance in various 2D computer vision tasks due to availability of large amounts of 2D training data. Contrarily, medical imaging deals with 3D data and usually lacks the equivalent extent and diversity of data, for developing AI models. Transfer learning provides the means to use models trained for one application as a starting point to another application. In this work, we leverage 2D pre-trained models as a starting point in 3D medical applications by exploring the concept of Axial-Coronal-Sagittal (ACS) convolutions. We have incorporated ACS as an alternative of native 3D convolutions in the Generally Nuanced Deep Learning Framework (GaNDLF), providing various well-established and state-of-the-art network architectures with the availability of pre-trained encoders from 2D data. Results of our experimental evaluation on 3D MRI data of brain tumor patients for i) tumor segmentation and ii) radiogenomic classification, show model size reduction by ~22% and improvement in validation accuracy by ~33%. Our findings support the advantage of ACS convolutions in pre-trained 2D CNNs over 3D CNN without pre-training, for 3D segmentation and classification tasks, democratizing existing models trained in datasets of unprecedented size and showing promise in the field of healthcare.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

NIMG-25. OPTIMIZATION OF ARTIFICIAL INTELLIGENCE ALGORITHMS FOR LOW-RESOURCE/CLINICAL ENVIRONMENTS: FOCUS ON CLINICALLY-RELEVANT GLIOMA REGION DELINEATION

BACKGROUND

Diffuse astrocytic glioma are common and aggressive malignant primary brain tumors with grim prognosis. Artificial intelligence (AI) has shown promise across predictive, prognostic, and diagnostic neuro-oncology applications, towards improving patient management. However, clinical translation and deployment are hampered by AI models’ requirements for explicit acceleration cards, which are not typically considered in clinical environments. Here, we seek the execution of AI models in such clinical/low-resource environments, facilitated by mathematical optimizations rather than investing in acceleration cards, and focus on the use case of delineating clinically-relevant regions, namely the entire tumor burden (ETB) and tumor core (TC).

METHODS

We identified the BraTS20 retrospective cohort of 369 glioma cases, each described by 4 structural multi-parametric MRI (mpMRI) scans (T1,T1Gd,T2,T2-FLAIR). The Generally Nuanced Deep Learning Framework (GaNDLFv0.0.14/PyTorchv1.8.2) was used to train the original AI model (ResUNet), by randomly sampling 40 643 patches from each mpMRI scan. We then investigate the contributions of post-training mathematical optimizations. Quantitative performance evaluation of the original and optimized (GaNDLFv0.0.14/OpenVINOTM-INT8-v2022.1.0) models were based on 125 unseen hold-out cases (BraTS20 validation dataset), using the dice similarity coefficient (DSC), while profiling in a consumer-grade workstation, i.e., typical clinical hardware configuration.

RESULTS

Negligible delineation performance differences were observed between the original and optimized AI models, for both ETB (DSCoriginal/DSCoptimized= 0.877/0.876) and TC (DSCoriginal/DSCoptimized= 0.773/0.772). However, the optimized model yielded substantial improvements in latency (up to 5.4x faster inference) and 53% less memory footprint.

CONCLUSIONS

Post-training mathematical optimization of AI models yields substantial gains in latency and memory usage, while maintaining their accuracy. Although we focused on delineation, we anticipate these mathematical optimizations to be applicable in other AI models. Post-training optimization is a promising approach for deploying AI models on consumer-grade CPUs, and hence facilitating their translation in low-resource/clinical environments, potentially contributing to improved patient management, treatment decisions, and response assessment.

*author Siddhesh Thakur is an equal contributing first author.

OpenFL: the open federated learning library

Objective.Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets.Approach.Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks.Main results.In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases.Significance.The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced atgithub.com/intel/openfl.

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

Objective.De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria.Approach.Towards this end, this manuscript describes theFederatedTumorSegmentation (FeTS) tool, in terms of software architecture and functionality.Main results.The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data.Significance.Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced athttps://github.com/FETS-AI/Front-End.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1

Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Abstract 1929: Deep-learning-enabled volumetric breast density estimation with digital breast tomosynthesis

Background: It has been widely established that breast density is an independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from 2D images acquired with DBT and/or depend on the existence of raw DBT data which is rarely archived by clinical centers due to cost and storage constraints. This study aims to harness deep learning to develop a computational tool for VBD assessment based solely on 3D reconstructed, “for presentation” DBT images.

Methods: We retrospectively analyzed 1,080 negative DBT screening exams (09/20/2011 - 11/25/2016) from the Hospital of the University of Pennsylvania (mean age ± SD, 57 ± 11 years; mean BMI ± SD, 28.7 ± 7.1 kg/m2; racial makeup, 41.2% White, 54.2% Black, 4.6% Other), for which both 3D reconstructed and 2D raw DBT images (Selenia Dimensions, Hologic Inc) were available. All available standard views (left and right mediolateral-oblique and cranio-caudal views) were included for each exam, leading to 7,850 DBT views. Corresponding 3D reference-standard tissue segmentations were generated from a previously validated software that uses both 3D reconstructed slices and raw 2D DBT data to provide VBD metrics, shown to be strongly correlated with VBD measures from MRI image volumes. We based our deep learning algorithm on the U-Net architecture within the open-source Generally Nuanced Deep Learning Framework (GaNDLF) and created a 3-label image segmentation task (background, dense tissue, and fatty tissue). Our dataset was randomly split into training (70%), validation (15%) and test (15%) sets, while ensuring that all views of the same DBT exam were assigned to the same set. The performance of our deep learning algorithm against the corresponding reference-standard segmentations was measured in terms of Dice scores (DSC), with 0 signifying no overlap and 1 signifying perfect overlap, overall, as well as separately for each label.

Results: After training was complete, our deep learning algorithm achieved a DSC of 0.78 on the validation, as well as on the test set. Our method accurately segmented background from breast tissue (DSC = 0.94) and demonstrated moderate to high performance in segmenting dense and fatty tissue, respectively (DSC = 0.49 and 0.89).

Conclusion: Our preliminary analysis suggests that deep learning shows promise in the estimation of VBD using 3D DBT reconstructed, “for presentation” images. Future work involving transfer learning based on ground truth masks by clinical radiologists could further enhance this method’s performance. In view of rapid clinical conversion to DBT screening, such a tool has the potential to enable large retrospective epidemiologic and personalized risk assessment studies of breast density with DBT.

Citation Format: Vinayak S. Ahluwalia, Walter Mankowski, Sarthak Pati, Spyridon Bakas, Ari Brooks, Celine M. Vachon, Emily F. Conant, Aimilia Gastounioti, Despina Kontos. Deep-learning-enabled volumetric breast density estimation with digital breast tomosynthesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1929.

Artificial-intelligence-driven volumetric breast density estimation with digital breast tomosynthesis in a racially diverse screening cohort

e13538

Background: Breast density is considered a well-established breast cancer risk factor. As quasi-3D, digital breast tomosynthesis (DBT) becomes increasingly utilized for screening, there is an opportunity to routinely estimate volumetric breast density (VBD). However, current methods extrapolate VBD from 2D images acquired with DBT and/or depend on the existence of raw DBT data, which is rarely archived due to cost and storage constraints. Using a racially diverse screening cohort, this study evaluates the potential of deep learning for VBD assessment based solely on 3D reconstructed, “for presentation” DBT images. Methods: We retrospectively analyzed 1,080 negative DBT screening exams obtained between 2011 and 2016 from the Hospital of the University of Pennsylvania (racial makeup, 41.2% White, 54.2% Black, 4.6% Other; mean age ± SD, 57 ± 11 years; mean BMI ± SD, 28.7 ± 7.1 kg/m2), for which both 2D raw and 3D reconstructed DBT images (Selenia Dimensions, Hologic Inc) were available. Corresponding 3D reference-standard tissue segmentations were generated from previously validated software that uses both 3D reconstructed slices and raw 2D DBT data to provide VBD metrics, shown to be strongly correlated with VBD measures from MRI image volumes. We based our deep learning algorithm on the U-Net architecture within the open-source Generally Nuanced Deep Learning Framework (GaNDLF) and created a 3-label image segmentation task (background, dense tissue, and fatty tissue). Our dataset was randomly split into training (70%), validation (15%) and test (15%) sets. We report on the performance of our deep learning algorithm against corresponding reference-standard segmentations for a cranio-caudal (CC) view-only subset. We also stratify our results by the two main racial groups (White and Black). Our evaluation measure was the weighted Dice score (DSC), with 0 signifying no overlap and 1 signifying perfect overlap, overall and separately for each label. Results: Our deep learning algorithm achieved an overall DSC of 0.682 (STD = 0.136). It accurately segmented the three labels of background, fatty tissue, and dense tissue, with DSC scores of 0.995, 0.884, and 0.617, respectively. DSC for White and Black women were 0.688 (STD = 0.127) and 0.680 (STD = 0.146), respectively. Conclusions: Our preliminary analysis suggests that deep learning shows promise in the estimation of VBD using 3D DBT reconstructed, “for presentation” CC view images and does not demonstrate bias among racial groups. Future work involving optimization of performance in other breast views as well as transfer learning based on ground truth masks by clinical radiologists could further enhance this method. In view of rapid clinical conversion to DBT screening, such a tool has the potential to enable large retrospective epidemiological and personalized risk assessment studies of breast density with DBT.

Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.

Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments

Brain extraction is an indispensable step in neuro-imaging with a direct impact on downstream analyses. Most such methods have been developed for non-pathologically affected brains, and hence tend to suffer in performance when applied on brains with pathologies, e.g., gliomas, multiple sclerosis, traumatic brain injuries. Deep Learning (DL) methodologies for healthcare have shown promising results, but their clinical translation has been limited, primarily due to these methods suffering from i) high computational cost, and ii) specific hardware requirements, e.g., DL acceleration cards. In this study, we explore the potential of mathematical optimizations, towards making DL methods amenable to application in low resource environments. We focus on both the qualitative and quantitative evaluation of such optimizations on an existing DL brain extraction method, designed for pathologically-affected brains and agnostic to the input modality. We conduct direct optimizations and quantization of the trained model (i.e., prior to inference on new data). Our results yield substantial gains, in terms of speedup, latency, through-put, and reduction in memory usage, while the segmentation performance of the initial and the optimized models remains stable, i.e., as quantified by both the Dice Similarity Coefficient and the Hausdorff Distance. These findings support post-training optimizations as a promising approach for enabling the execution of advanced DL methodologies on plain commercial-grade CPUs, and hence contributing to their translation in limited- and low- resource clinical environments.

Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough?

Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.

Classification of Infection and Ischemia in Diabetic Foot Ulcers Using VGG Architectures

Diabetic foot ulceration (DFU) is a serious complication of diabetes, and a major challenge for healthcare systems around the world. Further infection and ischemia in DFU can significantly prolong treatment and often result in limb amputation, with more severe cases resulting in terminal illness. Thus, early identification and regular monitoring is necessary to improve care, and reduce the burden on healthcare systems. With that in mind, this study attempts to address the problem of infection and ischemia classification in diabetic food ulcers, in four distinct classes. We have evaluated a series of VGG architectures with different layers, following numerous training strategies, including k-fold cross validation, data pre-processing options, augmentation techniques, and weighted loss calculations. In favor of transparency and reproducibility, we make all the implementations available through the Generally Nuanced Deep Learning Framework (GaNDLF, github.com/CBICA/GaNDLF. Our best model was evaluated during the DFU Challenge 2021, and was ranked 2^nd, 5^th, and 7^th based on the macro-averaged AUC (area under the curve), macro-averaged F1 score, and macro-averaged recall metrics, respectively. Our findings support that current state-of-the-art architectures provide good results for the DFU image classification task, and further experimentation is required to study the effects of pre-processing and augmentation strategies.

NIMG-32. THE FEDERATED TUMOR SEGMENTATION (FETS) INITIATIVE: THE FIRST REAL-WORLD LARGE-SCALE DATA-PRIVATE COLLABORATION FOCUSING ON NEURO-ONCOLOGY

PURPOSE

Robustness and generalizability of artificial intelligent (AI) methods is reliant on the training data size and diversity, which are currently hindered in multi-institutional healthcare collaborations by data ownership and legal concerns. To address these, we introduce the Federated Tumor Segmentation (FeTS) Initiative, as an international consortium using federated learning (FL) for data-private multi-institutional collaborations, where AI models leverage data at participating institutions, without sharing data between them. The initial FeTS use-case focused on detecting brain tumor boundaries in MRI.

METHODS

The FeTS tool incorporates: 1) MRI pre-processing, including image registration and brain extraction; 2) automatic delineation of tumor sub-regions, by label fusion of pretrained top-performing BraTS methods; 3) tools for manual delineation refinements; 4) model training. 55 international institutions identified local retrospective cohorts of glioblastoma patients. Ground truth was generated using the first 3 FeTS functionality modes as mentioned earlier. Finally, the FL training mode comprises of i) an AI model trained on local data, ii) local model updates shared with an aggregator, which iii) combines updates from all collaborators to generate a consensus model, and iv) circulates the consensus model back to all collaborators for iterative performance improvements.

RESULTS

The first FeTS consensus model, from 23 institutions with data of 2,200 patients, showed an average improvement of 11.1% in the performance of the model on each collaborator’s validation data, when compared to a model trained on the publicly available BraTS data (n=231).

CONCLUSION

Our findings support that data increase alone would lead to AI performance improvements without any algorithmic development, hence indicating that the model performance would improve further when trained with all 55 collaborating institutions. FL enables AI model training with knowledge from data of geographically-distinct collaborators, without ever having to share any data, hence overcoming hurdles relating to legal, ownership, and technical concerns of data sharing.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Constraints and challenges in convalescent plasma collection amidst the Covid 19 pandemic- strategies and recommendations to overcome these

Background

COVID 19 is an acute respiratory disease caused by infection by the virus SARS-COV-2 and has been declared as a pandemic whose specific treatment is still not established. One of the options in the treatment is Convalescent plasma (CP) therapy when there is presence of required amount of neutralizing antibodies in the plasma of recovered COVID patients. Our objective was to analyze the challenges and the constraints encountered in motivation of COVID 19 recovered persons to come for the screening procedures and to convince the selected persons to come for Plasma donation voluntarily.

Material & methods

The present retrospective observational study was conducted for a period of five and half months. Out of 1515 number of persons contacted telephonically for Plasma donation, 1291 persons came for screening of whom 1028 persons were eligible for donation, 263 cases were deferred and 966 persons finally donated.

Results

Maximum number of acceptance cases were from males-(98.7%). Of the accepted cases, (41.73%) were from the 18-30 years’ age group. 33.94% were from blood group ‘O’ Rh D positive giving maximum contribution from any blood group. 38.3% of the accepted cases had resolution of all COVID symptoms within time period of 28-40 days. Maximum number of accepted individuals (39.75%) had suffered from multiple symptoms followed by 39.02% of asymptomatic persons. Highest number of Plasma donation was contributed by Odisha Government Police personnel (51.56%).

Discussion

In this global ongoing pandemic, the “Fear Factor of contracting the disease” has acted as a major challenge in motivating and convincing a COVID recovered patient for plasma donation. The challenge before the medical professionals was to motivate, educate and convince the potential donors and the society about the likely benefits of convalescent plasma. This could be finally overcome with the help of positive orientation through social and conventional media as well as mass appeal from government side on the benefits of plasma therapy in saving lives in the present pandemic.

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.

Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)

Background

Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm.

Methods

We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH-mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH, 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70-30% training-testing splits or 10-fold cross-validation within individual collections, (2) 70-30% training-testing splits within merged collections, and (3) training on one collection and testing on another.

Results

These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH, and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset.

Conclusions

Using machine learning algorithms, high accuracy was achieved in the prediction of IDH, 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses.

Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

PURPOSE

The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites.

ACQUISITION AND VALIDATION METHODS

From TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families.

DATA FORMAT AND USAGE NOTES

We make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis.

POTENTIAL APPLICATIONS

The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.

TMOD-09. GLIOBLASTOMA BIOPHYSICAL GROWTH ESTIMATION USING DEEP LEARNING-BASED REGRESSION

BACKGROUND

Glioblastomas are arguably the most aggressive, infiltrative, and heterogeneous adult brain tumor. Biophysical modeling of glioblastoma growth has shown its predictive value towards clinical endpoints, enabling more informed decision-making. However, the mathematically rigorous formulations of biophysical modeling come with a large computational footprint, hindering their application to clinical studies.

METHODS

We present a deep learning (DL)-based logistical regression model, to estimate in seconds glioblastoma biophysical growth, defined through three tumor-specific parameters: 1) diffusion coefficient of white matter (Dw), which describes how easily the tumor can infiltrate through the white matter, 2) mass-effect parameter (Mp), which defines the average tumor expansion, and 3) estimated time (T) in number of days that the tumor has been growing. Pre-operative multi-parametric MRI (mpMRI) structural scans (T1, T1-Gd, T1, T2-FLAIR) from 135 subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal T2-FLAIR signal envelope, for training three DL models for the three tumor-specific parameters. Each of our DL models consist of two sets of convolution layers followed by a single max-pooling layer, with a normalized root mean squared error as the minimization metric and evaluated using 10-fold cross validation. We train and validate the DL-based predictions against parameters derived from biophysical inversion models.

RESULTS

Pearson correlation coefficients between our DL-based estimations and the biophysical parameters were equal to 0.85 for Dw, 0.90 for Mp, and 0.94 for T.

CONCLUSION

This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation, paving the way towards their utilization in more clinical studies, while opening the door for leveraging advanced radiomic descriptors in future studies, as well as allowing for significantly faster parameter reconstruction compared to biophysical growth modeling approaches.

*denotes equal senior authorship.

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

PURPOSE

To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.

PATIENTS AND METHODS

We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort.

RESULTS

These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP.

CONCLUSION

Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Comparative analysis of PHAs production by Bacillus megaterium OUAT 016 under submerged and solid-state fermentation

In view of risk coupled with synthetic polymer waste, there is an imperative need to explore biodegradable polymer. On account of that, six PHAs producing bacteria were isolated from mangrove forest and affilated to the genera Bacillus & Pseudomonas from morpho-physiological characterizations. Among which the potent PHAs producer was identified as Bacillus megaterium OUAT 016 by 16S rDNA sequencing and in-silico analysis. This research addressed a comparative account on PHAs production by submerged and solid-state fermentation pertaining to different downstream processing. Here, we established higher PHAs production by solid-state fermentation through sonication and mono-solvent extraction. Using modified MSM media under optimized conditions, 49.5% & 57.7% of PHAs were produced in submerged and 34.1% & 62.0% in solid-state fermentation process. Extracted PHAs was identified as a valuable polymer PHB-co-PHV and its crystallinity & thermostability nature was validated by FTIR, ¹H NMR and XRD. The melting (Tm) and thermal degradation temperature (Td) of PHB-co-PHV was 166 °C and 273 °C as depicted from DTA. Moreover, FE-SEM and SPM surface imaging indicated biodegradable nature, while FACS assay confirmed cytocompatibility of PHB-co-PHV.

Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning

Skull-stripping is an essential pre-processing step in computational neuro-imaging directly impacting subsequent analyses. Existing skull-stripping methods have primarily targeted non-pathologicallyaffected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. Here we present a performance evaluation of publicly available implementations of established 3D Deep Learning architectures for semantic segmentation (namely DeepMedic, 3D U-Net, FCN), with a particular focus on identifying a skull-stripping approach that performs well on brain tumor scans, and also has a low computational footprint. We have identified a retrospective dataset of 1,796 mpMRI brain tumor scans, with corresponding manually-inspected and verified gold-standard brain tissue segmentations, acquired during standard clinical practice under varying acquisition protocols at the Hospital of the University of Pennsylvania. Our quantitative evaluation identified DeepMedic as the best performing method (Dice = 97.9, Hausdorf f 95 = 2.68). We release this pre-trained model through the Cancer Imaging Phenomics Toolkit (CaPTk) platform.

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Friction Stir Processing of Stainless Steel for Ascertaining Its Superlative Performance in Bioimplant Applications

Substrate-cell interactions for a bioimplant are driven by substrate's surface characteristics. In addition, the performance of an implant and resistance to degradation are primarily governed by its surface properties. A bioimplant typically degrades by wear and corrosion in the physiological environment, resulting in metallosis. Surface engineering strategies for limiting degradation of implants and enhancing their performance may reduce or eliminate the need for implant removal surgeries and the associated cost. In the current study, we tailored the surface properties of stainless steel using submerged friction stir processing (FSP), a severe plastic deformation technique. FSP resulted in significant microstructural refinement from 22 μm grain size for the as-received alloy to 0.8 μm grain size for the processed sample with increase in hardness by nearly 1.5 times. The wear and corrosion behavior of the processed alloy was evaluated in simulated body fluid. The processed sample demonstrated remarkable improvement in both wear and corrosion resistance, which is explained by surface strengthening and formation of a highly stable passive layer. The methylthiazol tetrazolium assay demonstrated that the processed sample is better in supporting cell attachment, proliferation with minimal toxicity, and hemolysis. The athrombogenic characteristic of the as-received and processed samples was evaluated by fibrinogen adsorption and platelet adhesion via the enzyme-linked immunosorbent assay and lactate dehydrogenase assay, respectively. The processed sample showed less platelet and fibrinogen adhesion compared with the as-received alloy, signifying its high thromboresistance. The current study suggests friction stir processing to be a versatile toolbox for enhancing the performance and reliability of currently used bioimplant materials.

Development of an international comorbidity education framework

CONTEXT

The increasing number of people living with multiple chronic conditions in addition to an index condition has become an international healthcare priority. Health education curricula have been developed alongside single condition frameworks in health service policy and practice and need redesigning to incorporate optimal management of multiple conditions.

AIM

Our aims were to evaluate current teaching and learning about comorbidity care amongst the global population of healthcare students from different disciplines and to develop an International Comorbidity Education Framework (ICEF) for incorporating comorbidity concepts into health education.

METHODS

We surveyed nursing, medical and pharmacy students from England, India, Italy and Sweden to evaluate their understanding of comorbidity care. A list of core comorbidity content was constructed by an international group of higher education academics and clinicians from the same disciplines, by searching current curricula and analysing clinical frameworks and the student survey data. This list was used to develop the International Comorbidity Education Framework.

RESULTS

The survey sample consisted of 917 students from England (42%), India (48%), Italy (8%) and Sweden (2%). The majority of students across all disciplines said that they lacked knowledge, training and confidence in comorbidity care and were unable to identify specific teaching on comorbidities. All student groups wanted further comorbidity training. The health education institution representatives found no specific references to comorbidity in current health education curricula. Current clinical frameworks were used to develop an agreed list of core comorbidity content and hence an International Comorbidity Education Framework.

CONCLUSIONS

Based on consultation with academics and clinicians and on student feedback we developed an International Comorbidity Education Framework to promote the integration of comorbidity concepts into current healthcare curricula.

Effects of overweight and obesity on motor and mental development in infants and toddlers

BACKGROUND

A consequence of childhood obesity may be poor developmental outcomes.

OBJECTIVES

This study aimed to examine the relationship between weight and developmental delays in young children.

METHODS

We conducted a secondary analysis of the Early Childhood Longitudinal Study Birth Cohort data. Logistic regression models quantified the association between different weight statuses (normal weight <85th, overweight ≥90th, obese ≥95th percentile for weight) and delays in motor and mental development.

RESULTS

Children classified as overweight in both waves had higher percentages of delays in wave 2 (motor [7.5 vs. 6.2-6.4%], mental [8.6 vs. 5.9-6.7%]), as well as wave 1 and/or wave 2 (motor [14.8 vs. 10.9-13.0%], mental [11.9 vs. 9.0-10.1%]), compared with other children. This association was also found in children who were obese at both time points in wave 2 (motor delay [8.9 vs. 4.9-7.3%], mental delay [10.3 vs. 6.0-7.2%]), as well as wave 1 and/or wave 2 (motor delay [14.5 vs. 10.9-12.9%], mental delay [14.1 vs. 9.4-10.1%]). In the adjusted models, children classified as always obese were more likely to have a mental delay in wave 2 (adjusted odds ratio [aOR] 1.89, 95% confidence interval [CI]: 1.21-2.95) as well as wave 1 and/or wave 2 (aOR 1.56, 95% CI: 1.08-2.26). These children were also more likely to have motor delay (aOR 1.47, 95% CI: 1.02-2.13) in wave 1 and/or wave 2.

CONCLUSION

Overweight children are more likely than their normal-weight peers to have motor and mental developmental delays. Preventing obesity during infancy may facilitate reducing developmental delays in young children.

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6,7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative model based on a joint segmentation-registration framework is used to segment the brain scans into cancerous and healthy tissues. Secondly, a gradient boosting classification scheme is used to refine tumor segmentation based on information from multiple patients. We evaluated our approach in 218 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2016 challenge and report promising results. During the testing phase, the proposed approach was ranked among the top performing methods, after being additionally evaluated in 191 unseen cases.

Study of proteolysis in river buffalo mozzarella cheese using a proteomics approach

The guarantee of the origin and quality of raw material is essential for the protection and valorization of Campana buffalo mozzarella cheese. The risk of utilization of semifinished products and stored milk in substitution for fresh milk is increasing, due to the continuous desire to reduce production costs. A proteomics approach and electrophoresis survey of retail mozzarella cheeses indicated different rates of proteolysis in the production of dairy industries. The use of fresh milk and correct cheesemaking protocol yielded only γ-caseins, which are derived from β-casein by plasmin, and para-κ-casein, which is derived from κ-casein by chymosin. The detection of abnormal hydrolysis resulting in β- and αS1-casein fragments, identified by mass spectrometry, indicates the use of stored milk or stored and pressed curd, or the reuse of unsold mozzarella cheese, to produce mozzarella. The formation of γ-caseins and other fragments during a long storage of raw materials at room or refrigeration temperature was ascribed to plasmin (endogenous milk enzyme), whereas formation of αS1-casein fragments, mainly αS1-I(6P)- and αS1-I(7P)-casein during the storage of curd was ascribed to the action of chymosin (exogenous enzyme) from rennet. Sodium dodecyl sulfate-PAGE and alkaline urea-PAGE permitted us to evaluate the freshness of the raw materials used in the manufacturing of buffalo mozzarella cheese and to reveal possible inappropriate preservation.

Epidemiological study of canine parvovirus infection in and around Bhubaneswar, Odisha, India

Aim:

An epidemiological study of canine parvovirus infection in dogs in and around Bhubaneswar, Odisha was conducted between December 2012 to March 2013 and prevalence rate was studied on the basis of age, breed, and sex.

Materials and Methods:

A total of 71 fecal samples from suspected diarrheic dogs were collected in sterile phosphate buffer saline (10% W/V) and examined by polymerase chain reaction (PCR) for detection of canine parvo virus infection, followed by epidemiological study in relation to age, breed, and sex.

Results:

Of 71 samples analyzed, 29 (40.85%) were found to be positive by PCR assay. The infection was higher in Deshi/local breeds (34.48%), followed by German shepherd (17.24%), equal incidences in mixed and Labrador retriever (10.34%), Rottweiler and German spitz showed 6.90% each and finally lower incidences in four breeds (3.45%) such as Dalmatians, Nea politan mastiff, Pug and Great Dane. Age-wise prevalence study revealed the infection being more in the age group of 3-6 months (41.37%), followed by equal incidences of 27.59% in 1-3 months and 6-12 months age group, and a low incidence in age groups above 12 months (3.45%). The incidence was predominantly higher in males (86.21%) than females (13.79%).

Conclusions:

The epidemiological analysis revealed that the breed wise prevalence was found to be more in Deshi breeds as compared to others, age groups below 6 months were found to be more prone to parvovirus infection and males were mostly infected.

Spray-dried plasma and fresh frozen plasma modulate permeability and inflammation in vitro in vascular endothelial cells

BACKGROUND

After major traumatic injury, patients often require multiple transfusions of fresh frozen plasma (FFP) to correct coagulopathy and to reduce bleeding. A spray-dried plasma (SDP) product has several logistical benefits over FFP use in trauma patients with coagulopathy. These benefits include ease of transport, stability at room temperature, and rapid reconstitution for infusion. Our past work suggests that FFP promotes endothelial stability by inhibiting endothelial permeability.

STUDY DESIGN AND METHODS

The main goal of this project is to determine if solvent-detergent-treated SDP is equivalent to FFP in inhibiting vascular endothelial cell (EC) permeability and inflammation in vitro. Furthermore, this study aimed to determine if solvent-detergent treatment and spray drying of plasma alters the protective effects of FFP on EC function. The five groups tested in our studies are the following: 1) fresh frozen-thawed plasma (FFP); 2) solvent-detergent-treated FFP; 3) solvent-detergent-treated SDP; 4) lactated Ringer's solution; and 5) Hextend.

RESULTS

This study demonstrates that in vitro SDP and FFP equivalently inhibit vascular EC permeability, EC adherens junction breakdown, and endothelial white blood cell binding, an effect that is independent of changes in Vascular Cell Adhesion Molecule 1, Intracellular Adhesion Molecule 1, or E-selectin expression on ECs. Solvent-detergent treatment of FFP does not alter the protective effects of FFP on endothelial cell function in vitro.

CONCLUSION

These data suggest the equivalence of FFP and SDP on modulation of endothelial function and inflammation in vitro.

Overview of the diagnosis of cytomegalovirus infection

Cytomegalovirus (CMV) is recognized as the most common congenital viral infection in humans and an important cause of morbidity and mortality in immunocompromised hosts. This recognition of the clinical importance of invasive CMV disease in the setting of immunodeficiency and in children with congenital CMV infection has led to the development of new diagnostic procedures for the rapid identification of CMV. Diagnosis of acute maternal CMV infection by the presence of immunoglobulin (Ig)M and low-avidity IgG requires confirmation of fetal infection, which is typically performed using polymerase chain reaction (PCR) assays for CMV on amniotic fluid. Viral culture of the urine and saliva obtained within the first two weeks of life continues to be the gold standard for diagnosis of congenitally-infected infants. PCR assays of dried blood spots from newborns have been shown to lack sufficient sensitivity for the identification of most neonates with congenital CMV infection for universal screening purposes. However, saliva PCR assays are currently being assessed as a useful screening method for congenital CMV infection. In the immunocompromised host, newer rapid diagnostic assays, such as phosphoprotein 65 antigenemia and CMV real-time PCR of blood or plasma have allowed for preemptive treatment, reducing morbidity and mortality. However, lack of standardized real-time PCR protocols hinders the comparison of data from different centers and the development of uniform guidelines for the management of invasive CMV infections in immunocompromised individuals.