Big Data and the Future of Radiology Informatics

Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach

Large numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels due to limitations of human annotations. The purpose of our study was to develop an automated labeling approach that improves the image classification model to distinguish normal knee images from those with abnormalities or prior arthroplasty. The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled data, further improving the image classification performance for knee radiographic diagnosis. We used BioBERT and EfficientNet as the feature extraction backbone of the labeler and imaging model, respectively. We developed our approach using 7382 patients and validated it on a separate set of 637 patients. The final image classification model, trained using both manually labeled and pseudo-labeled data, had the higher weighted average AUC (WA-AUC 0.903) value and higher AUC values among all classes (normal AUC 0.894; abnormal AUC 0.896, arthroplasty AUC 0.990) compared to the baseline model (WA-AUC = 0.857; normal AUC 0.842; abnormal AUC 0.848, arthroplasty AUC 0.987), trained using only manually labeled data. Statistical tests show that the improvement is significant on normal (p value < 0.002), abnormal (p value < 0.001), and WA-AUC (p value = 0.001). Our findings demonstrated that the proposed automated labeling approach significantly improves the performance of image classification for radiographic knee diagnosis, allowing for facilitating patient care and curation of large knee datasets.

Spatial mapping of tumor heterogeneity in whole-body PET-CT: a feasibility study

BACKGROUND

Tumor heterogeneity is recognized as a predictor of treatment response and patient outcome. Quantification of tumor heterogeneity across all scales may therefore provide critical insight that ultimately improves cancer management.

METHODS

An image registration-based framework for the study of tumor heterogeneity in whole-body images was evaluated on a dataset of 490 FDG-PET-CT images of lung cancer, lymphoma, and melanoma patients. Voxel-, lesion- and subject-level features were extracted from the subjects' segmented lesion masks and mapped to female and male template spaces for voxel-wise analysis. Resulting lesion feature maps of the three subsets of cancer patients were studied visually and quantitatively. Lesion volumes and lesion distances in subject spaces were compared with resulting properties in template space. The strength of the association between subject and template space for these properties was evaluated with Pearson's correlation coefficient.

RESULTS

Spatial heterogeneity in terms of lesion frequency distribution in the body, metabolic activity, and lesion volume was seen between the three subsets of cancer patients. Lesion feature maps showed anatomical locations with low versus high mean feature value among lesions sampled in space and also highlighted sites with high variation between lesions in each cancer subset. Spatial properties of the lesion masks in subject space correlated strongly with the same properties measured in template space (lesion volume, R = 0.986, p < 0.001; total metabolic volume, R = 0.988, p < 0.001; maximum within-patient lesion distance, R = 0.997, p < 0.001). Lesion volume and total metabolic volume increased on average from subject to template space (lesion volume, 3.1 ± 52 ml; total metabolic volume, 53.9 ± 229 ml). Pair-wise lesion distance decreased on average by 0.1 ± 1.6 cm and maximum within-patient lesion distance increased on average by 0.5 ± 2.1 cm from subject to template space.

CONCLUSIONS

Spatial tumor heterogeneity between subsets of interest in cancer cohorts can successfully be explored in whole-body PET-CT images within the proposed framework. Whole-body studies are, however, especially prone to suffer from regional variation in lesion frequency, and thus statistical power, due to the non-uniform distribution of lesions across a large field of view.

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

This study aims to show the feasibility and benefit of single queries in a research data warehouse combining data from a hospital's clinical and imaging systems. We used a comprehensive integration of a production picture archiving and communication system (PACS) with a clinical data warehouse (CDW) for research to create a system that allows data from both domains to be queried jointly with a single query. To achieve this, we mapped the DICOM information model to the extended entity-attribute-value (EAV) data model of a CDW, which allows data linkage and query constraints on multiple levels: the patient, the encounter, a document, and a group level. Accordingly, we have integrated DICOM metadata directly into CDW and linked it to existing clinical data. We included data collected in 2016 and 2017 from the Department of Internal Medicine in this analysis for two query inquiries from researchers targeting research about a disease and in radiology. We obtained quantitative information about the current availability of combinations of clinical and imaging data using a single multilevel query compiled for each query inquiry. We compared these multilevel query results to results that linked data at a single level, resulting in a quantitative representation of results that was up to 112% and 573% higher. An EAV data model can be extended to store data from clinical systems and PACS on multiple levels to enable combined querying with a single query to quickly display actual frequency data.

Imaging in Healthcare: A Glance at the Present and a Glimpse Into the Future

The utilization of artificial intelligence (AI) applications in medical imaging relies heavily on imaging informatics. That is a one-of-a-kind professional who works at the crossroads of clinical radiography, data science, and information technology. Imaging informaticians are becoming crucial players in expanding, assessing, and implementing AI in the medical setting. Teleradiology will continue to be a cost-effective healthcare facility that expands. Vendor neutral archive (VNA) isolates image presentation and storing systems, permitting platforms to develop quickly, and is a repository for organization-wide healthcare image data. Efforts are made to incorporate and integrate diagnostic facilities such as radiography and pathology to fulfill the needs and demands of targeted therapy. Developments in computer-aided medical object identification may alter the environment of patient services. Finally, interpreting and processing distinct complex healthcare data will create a data-rich context where evidence-based care and performance development may be driven.

Value-Based Healthcare and Radiology: How can Value be Measured?

The concept of value-based healthcare (VBH) emerges as a response to traditional models of healthcare system management. More specifically, in radiology, the transition from volume to value has been discussed by its main associations, having as the main concern regarding the role of the specialty in a more integrated healthcare context.

Through a qualitative study, this work aims to analyse and evaluate how this new concept can be implemented in radiology by identifying obstacles and mapping the technical and procedural improvements necessary for its correct implementation in the national context of healthcare provision. Through interviews with different elements of the healthcare sector (from doctors to industry partners and researchers), it was possible to draw a set of metrics for measuring the value of radiology, alongside the implementation of a VBH strategy.

As the main conclusion, the implementation of a strategic agenda for the creation of value in radiology at the national level should be based on the reduction of variability and the identification of best practices in terms of adequacy, quality, safety and efficiency, aiming to satisfy the needs of requesting doctors and patients.

Time Line of Occlusion for Intracranial Aneurysms Treated with the Pipeline Embolization Device

BACKGROUND

Rates of aneurysm occlusion following treatment with flow-diverting stents have been quantified at predefined time points in clinical trials, but data characterizing the continuous temporal progression of aneurysm occlusion are lacking. This study used real-world variability in timing of angiographic follow-up to characterize the time line of aneurysm occlusion following treatment with the Pipeline embolization device (PED).

METHODS

All aneurysms treated with a PED at our institution between 2011 and 2020 were screened. Nonsaccular or ruptured aneurysms were excluded. Aneurysm occlusion status and time since treatment were recorded for each follow-up angiogram. Aneurysm occlusion was characterized using Kaplan-Meier and Cox proportional hazards analysis after censoring at last follow-up or subsequent treatment.

RESULTS

There were 290 aneurysms in 222 patients analyzed. The median time of observed aneurysm occlusion was 7.5 months, and overall rate of aneurysm occlusion was 77.9%. Larger aneurysms demonstrated a longer median time to occlusion and lower rate of aneurysm occlusion (P = 0.029). There were no observed differences in the time line of occlusion for aneurysms treated with a single PED or multiple PEDs (P = 0.889) or without or with adjunctive coiling (P = 0.771).

CONCLUSIONS

Aneurysms treated with a PED had a median time to observed occlusion of 7.5 months. Occlusion of larger aneurysms occurred more slowly than occlusion of smaller aneurysms following flow diversion. The number of PEDs deployed or the use of adjunctive coiling did not affect the time line or likelihood of aneurysm occlusion. These findings may guide optimal timing of follow-up after treatment with a PED.

Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal

Background

Radiology is an essential tool in the management of a patient. The aim of this manuscript was to build structured report (SR) Mammography based in Breast Cancer.

Methods

A working team of 16 experts (group A) was composed to create a SR for Mammography Breast Cancer. A further working group of 4 experts (group B), blinded to the activities of the group A, was composed to assess the quality and clinical usefulness of the SR final draft. Modified Delphi process was used to assess level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency and to measure quality analysis according to the average inter-item correlation.

Results

The final SR version was built by including n = 2 items in Personal Data, n = 4 items in Setting, n = 2 items in Comparison with previous breast examination, n = 19 items in Anamnesis and clinical context; n = 10 items in Technique; n = 1 item in Radiation dose; n = 5 items Parenchymal pattern; n = 28 items in Description of the finding; n = 12 items in Diagnostic categories and Report and n = 1 item in Conclusions. The overall mean score of the experts and the sum of score for structured report were 4.9 and 807 in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.82 in the second round. About the quality evaluation, the overall mean score of the experts was 3.3. The Cronbach’s alpha (Cα) correlation coefficient was 0.90.

Conclusions

Structured reporting improves the quality, clarity and reproducibility of reports across departments, cities, countries and internationally and will assist patient management and improve breast health care and facilitate research.

Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal

Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

Background: Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation. Results: The final SR version was built by including 16 items in the “Patient Clinical Data” section, 4 items in the “Clinical Evaluation” section, 8 items in the “Exam Technique” section, 22 items in the “Report” section, and 5 items in the “Conclusion” section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1–5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4–5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report. Conclusions: The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future

Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

Research on Application of Ultrasound Medical Imaging Technology in Big Data Mining of Regional Medical Imaging

Ultrasound medical imaging technology is one of the main methods of medical non-invasive diagnosis, and it is the focus of research in the medical field at home and abroad. Medical images have a large amount of data and contain a wealth of image feature information and rules, which need to be studied and understood. Therefore, the research of data mining technique for reading medical images has become a very important field in the interdisciplinary research of medical and computer science. The high resolution of medical images, the mass of data, and the complexity of image feature expressions make the research of data mining technology in medical images of great academic value and broad application prospects. At present, research on data mining for medical images has just started, and there are still many problems in the direct application of existing data mining methods. Researching and exploring the theoretical and practical problems of medical image data mining, such as data mining methods and algorithms suitable for medical image, which has significant and crucial value, and it is of great importance to help physicians in clinical diagnosis of medical images. This article introduces the background, definition and basic process of data mining technology, the characteristics of medical imaging data and the key techniques of medical image data mining. In view of the data mining research of human abdominal medical images is a completely new field, human abdominal imaging is the most complicated part of medical images. Solving the problem of abdominal imaging is of great value to the entire medical image. For regional medical image big data mining, we can use ultrasound images of the human abdomen. The clustering feature extraction algorithm and its implementation based on the approximate density structure of medical images proposed in this article, and innovative research results such as classification rule mining methods, are used to mine medical image data research, automatic diagnosis of clinical medical images, and early diagnosis of clinical medicine are of great significance.

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology

The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients. (1) SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. (2) SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. (3) Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Enabling automated pipelines, image analysis and big data methodology in cancer clinics requires thorough understanding of the data. Automated quality assurance steps could improve the efficiency and robustness of these methods by verifying possible data biases. In particular, in head and neck (H&N) computed-tomography (CT) images, dental artifacts (DA) obscure visualization of structures and the accuracy of Hounsfield units; a challenge for image analysis tasks, including radiomics, where poor image quality can lead to systemic biases. In this work we analyze the performance of three-dimensional convolutional neural networks (CNN) trained to classify DA statuses. 1538 patient images were scored by a single observer as DA positive or negative. Stratified five-fold cross validation was performed to train and test CNNs using various isotropic resampling grids (64³, 128³ and 256³), with CNN depths designed to produce 32³, 16³, and 8³ machine generated features. These parameters were selected to determine if more computationally efficient CNNs could be utilized to achieve the same performance. The area under the precision recall curve (PR-AUC) was used to assess CNN performance. The highest PR-AUC (0.92  ±  0.03) was achieved with a CNN depth  =  5, resampling grid  =  256. The CNN performance with 256³ resampling grid size is not significantly better than 64³ and 128³ after 20 epochs, which had PR-AUC  =  0.89  ±  0.03 (p -value  =  0.28) and 0.91  ±  0.02 (p -value  =  0.93) at depths of 3 and 4, respectively. Our experiments demonstrate the potential to automate specific quality assurance tasks required for unbiased and robust automated pipeline and image analysis research. Additionally, we determined that there is an opportunity to simplify CNNs with smaller resampling grids to make the process more amenable to very large datasets that will be available in the future.

Evidence of the benefits, advantages and potentialities of the structured radiological report: An integrative review

The structured report is a new trend for the preparation and manipulation of radiological examination reports. The structuring of the radiological report data can bring many benefits and advantages over other existing methodologies. Research and studies about the structured radiological report are highly relevant in clinical and academic subjects, improving medical practice, reducing unobserved problems by radiologists, improving reporting practices and medical diagnoses. Exposing the benefits, advantages and potential of the structured radiological report is important in encouraging the acceptance and implementation of this method by radiology professionals who are still somewhat resistant. The present review highlights the factors that contribute to the consolidation of adopting the structured radiology report methodology, addressing a variety of studies focused on the structuring of the radiological report. This integrative review of the literature is proposed by searching publications and journals databases (CAPES - Coordination of Improvement of Higher-Level Personnel, SciELO - Scientific Electronic Library Online, and PubMed - Publisher Medline) to develop a complete and unified understanding of the subject, so that it becomes a major part of evidence-based initiatives.

Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

Identifying patients with neuronal intranuclear inclusion disease in Singapore using characteristic diffusion-weighted MR images

PURPOSE

Adult-onset neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder described mainly in the Japanese population, with characteristic DWI abnormalities at the junction between gray and white matter. We identify possible cases of NIID in the picture archive and communication system (PACS) of a tertiary neurological referral hospital in Singapore and describe their radiological features.

METHODS

The neuroradiology imaging database was reviewed using keyword search of radiological reports to identify patients who had "subcortical U fibre" abnormalities on DWI. MRI were retrospectively reviewed, and those fulfilling inclusion criteria were invited for skin biopsy to detect nuclear inclusions by light and electron microscopy.

RESULTS

Twelve Chinese patients (nine female; median age 70.5 years) were enrolled. Seven patients were being assessed for dementia and five for other neurological indications. In all patients, DWI showed distinctive subcortical high signal with increased average apparent diffusion coefficient (ADC), involving frontal, parietal, and temporal more than occipital lobes; the corpus callosum and external capsule were affected in some patients. On T2-weighted images, cerebral and cerebellar atrophy and white matter hyperintensity of Fazekas grade 2 and above were seen in all patients. Three patients underwent skin biopsy; all were positive for intranuclear hyaline inclusion bodies on either p62 staining or electron microscopy, which are pathognomonic for NIID.

CONCLUSION

Previously undiagnosed patients with NIID can be identified by searching for abnormalities at the junction between gray and white matter on DWI in PACS and subsequently confirmed by skin biopsy. Radiologists should recognize the distinctive neuroimaging pattern of this dementing disease.

DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.

The Challenges of Diagnostic Imaging in the Era of Big Data

The diagnostic imaging field has undergone considerable growth both in terms of technological development and market expansion; with the following increasing production of a considerable amount of data that potentially fully poses diagnostic imaging in the Big data in the context of healthcare. Nevertheless, the mere production of a large amount of data does not automatically permit the real exploitation of their intrinsic value. Therefore, it is necessary to develop digital platforms and applications that favor the correct and advantageous management of diagnostic images such as Big data. This work aims to frame the role of diagnostic imaging in this new scenario, emphasizing the open challenges in exploiting such intense data generation for decision making with Big data analytics.

Layer-based visualization and biomedical information exploration of multi-channel large histological data

BACKGROUND AND OBJECTIVE

Modern microscopes can acquire multi-channel large histological data from tissues of human beings or animals, which contain rich biomedical information for disease diagnosis and biological feature analysis. However, due to the large size, fuzzy tissue structure, and complicated multiple elements integrated in the image color space, it is still a challenge for current software systems to effectively calculate histological data, show the inner tissue structures and unveil hidden biomedical information. Therefore, we developed new algorithms and a software platform to address this issue.

METHODS

This paper presents a multi-channel biomedical data computing and visualization system that can efficiently process large 3D histological images acquired from high-resolution microscopes. A novelty of our system is that it can dynamically display a volume of interest and extract tissue information using a layer-based data navigation scheme. During the data exploring process, the actual resolution of the loaded data can be dynamically determined and updated, and data rendering is synchronized in four display windows at each data layer, where 2D textures are extracted from the imaging volume and mapped onto the displayed clipping planes in 3D space.

RESULTS

To test the efficiency and scalability of this system, we performed extensive evaluations using several different hardware systems and large histological color datasets acquired from a CryoViz 3D digital system. The experimental results demonstrated that our system can deliver interactive data navigation speed and display detailed imaging information in real time, which is beyond the capability of commonly available biomedical data exploration software platforms.

CONCLUSION

Taking advantage of both CPU (central processing unit) main memory and GPU (graphics processing unit) graphics memory, the presented software platform can efficiently compute, process and visualize very large biomedical data and enhance data information. The performance of this system can satisfactorily address the challenges of navigating and interrogating volumetric multi-spectral large histological image at multiple resolution levels.

DICOM Metadata Analysis for Population Studies

This article reports an experimental study to determine how to use the stored Digital Imaging and Communication in Medicine (DICOM) metadata to perform population studies. As a case study, it was considered three types of medical imaging studies (i.e. routine head computed tomography, thorax computed radiography and thorax digital radiography) stored in the picture archiving and communication systems (PACS) of three healthcare institutions. The final sample consisted of DICOM metadata belonging to 1370360 images, corresponding to 109160 medical imaging studies performed on 72716 patients. The study followed a methodological approach that allows the identification of the number of patients with performed studies by age group and gender, as well as the average number of studies by patient, age group and gender in each one of the three healthcare institutions. The results show the relevance of the aggregation and analyses of DICOM metadata stored in heterogeneous PACS facilities.

Considerations for ethics review of big data health research: A scoping review

Big data trends in biomedical and health research enable large-scale and multi-dimensional aggregation and analysis of heterogeneous data sources, which could ultimately result in preventive, diagnostic and therapeutic benefit. The methodological novelty and computational complexity of big data health research raises novel challenges for ethics review. In this study, we conducted a scoping review of the literature using five databases to identify and map the major challenges of health-related big data for Ethics Review Committees (ERCs) or analogous institutional review boards. A total of 1093 publications were initially identified, 263 of which were included in the final synthesis after abstract and full-text screening performed independently by two researchers. Both a descriptive numerical summary and a thematic analysis were performed on the full-texts of all articles included in the synthesis. Our findings suggest that while big data trends in biomedicine hold the potential for advancing clinical research, improving prevention and optimizing healthcare delivery, yet several epistemic, scientific and normative challenges need careful consideration. These challenges have relevance for both the composition of ERCs and the evaluation criteria that should be employed by ERC members when assessing the methodological and ethical viability of health-related big data studies. Based on this analysis, we provide some preliminary recommendations on how ERCs could adaptively respond to those challenges. This exploration is designed to synthesize useful information for researchers, ERCs and relevant institutional bodies involved in the conduction and/or assessment of health-related big data research.

Quantitative radiomics: Validating image textural features for oncological PET in lung cancer

BACKGROUND AND PURPOSE

Radiomics textural features derived from PET imaging are of broad and current interest due to recent evidence of their prognostic value during cancer management. An inherent assumption is the link between these imaging features and the underlying tumoral phenotypic spatial heterogeneity. The purpose of this work was to validate this assumption for tumors within the lung through a comparison of image based textural features and the ground truth activity distribution from which the images were created. A second purpose was to assess the level at which PET imaging introduces spatial texture not present in the associated ground truth activity distribution.

MATERIALS AND METHODS

25 lung lesions were created using an anthropomorphic phantom. Ten of the lesions had a spherical shape with a uniform activity distribution. The remaining 15 had an irregular shape with a heterogeneous activity distribution. PET images were created for each lesion using Monte Carlo simulation. 79 textural features related to the gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, run length, and size zone matrices were derived from both the simulated PET images and ground truth activity maps. A comparison was made between the two datasets using statistical analysis.

RESULTS

For homogenous lesions, features extracted from the PET images were largely irrelevant to the underlying uniform activity distribution. Additionally, the majority of these features assumed substantial values implying that an extensive amount of spatial texture had been introduced into the final imaging data. For heterogeneous lesions, complex trends were observed in the deviation between features extracted from PET images and those extracted from the ground truth activity maps. Moreover, the extent of both the deviation and the associated dynamic range was seen to be greatly feature-dependent.

CONCLUSION

The use of image based textural features as a surrogate for tumoral phenotypic spatial heterogeneity could not be clearly validated. The association between the two is complex and a significant amount of uncertainty exist due to the introduction of incidental texture during image acquisition and reconstruction.

Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain

Objectives

Radiomic features extracted from diverse MRI modalities have been investigated regarding their predictive and/or prognostic value in a variety of cancers. With the aid of a 3D realistic digital MRI phantom of the brain, the aim of this study was to examine the impact of pulse sequence parameter selection on MRI-based textural parameters of the brain.

Methods

MR images of the employed digital phantom were realized with SimuBloch, a simulation package made for fast generation of image sequences based on the Bloch equations. Pulse sequences being investigated consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IR-SE), and inversion recovery gradient echo (IR-GRE). Twenty-nine radiomic textural features related, respectively, to gray-level intensity histograms (GLIH), cooccurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for the obtained MR realizations, and differences were identified.

Results

It was found that radiomic features vary considerably among images generated by the five different T1-weighted pulse sequences, and the deviations from those measured on the T1 map vary among features, from a few percent to over 100%. Radiomic features extracted from T1-weighted spin-echo images with TR varying from 360 ms to 620 ms and TE = 3.4 ms showed coefficients of variation (CV) up to 45%, while up to 70%, for T2-weighted spin-echo images with TE varying over the range 60-120 ms and TR = 6400 ms.

Conclusion

Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial. It calls for caution in employing MRI-derived radiomic features especially when pooling imaging data from multiple institutions with intention of correlating with clinical endpoints.

Medical imaging data in the digital innovation age

As we reflect on decades of exponential advancements in electronic innovation, we can see the field of medical imaging eclipsed by a new digital landscape - one that is inexpensive, fast, and powerful. This new paradigm presents new opportunities to innovate in both research and clinical settings. In this article, we review the current role of data: the common perceptions around its valuation and the infrastructure currently in place for data-driven innovation. Looking forward, we consider what has already been achieved using modern data capacities, the opportunities we have for further expansion in this area, and the obstacles we will need to transcend.

Large datasets, logistics, sharing and workflow in screening

Cancer screening initiatives exist around the world for different malignancies, most frequently breast, colorectal, and cervical cancer. A number of cancer registries exist to collect relevant data, but while these data may include imaging findings, they rarely, if ever, include actual images. Additionally, the data submitted to the registry are usually correlated with eventual cancer diagnoses and patient outcomes, rather than used with the individual's future screenings. Developing screening programs that allow for images to be submitted to a central location in addition to patient meta data and used for comparison to future screening exams would be very valuable in increasing access to care and ensuring that individuals are effectively screened at appropriate intervals. It would also change the way imaging results and additional patient data are correlated to eventual outcomes. However, it introduces logistical challenges surrounding secure storage and transmission of data to subsequent screening sites. In addition, in the absence of standardized protocols for screening, comparing current and prior imaging, especially from different equipment, can be challenging. Implementing a large-scale screening program with an image-enriched screening registry-effectively, an image-enriched electronic screening record-also requires that incentives exist for screening sites, physicians, and patients to participate; to maximize coverage, participation may have to be supported by government agencies. Workflows will also have to be adjusted to support registry participation for all screening patients in an effort to create a large, robust data set that can be used for future screening efforts as well as research initiatives.center.

[Big data in imaging]

Until now, most major medical advancements have been achieved through hypothesis-driven research within the scope of clinical trials. However, due to a multitude of variables, only a certain number of research questions could be addressed during a single study, thus rendering these studies expensive and time consuming. Big data acquisition enables a new data-based approach in which large volumes of data can be used to investigate all variables, thus opening new horizons. Due to universal digitalization of the data as well as ever-improving hard- and software solutions, imaging would appear to be predestined for such analyses. Several small studies have already demonstrated that automated analysis algorithms and artificial intelligence can identify pathologies with high precision. Such automated systems would also seem well suited for rheumatology imaging, since a method for individualized risk stratification has long been sought for these patients. However, despite all the promising options, the heterogeneity of the data and highly complex regulations covering data protection in Germany would still render a big data solution for imaging difficult today. Overcoming these boundaries is challenging, but the enormous potential advances in clinical management and science render pursuit of this goal worthwhile.

Machine Learning in Radiology: Applications Beyond Image Interpretation

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.

A peek into the future of radiology using big data applications

Big data is extremely large amount of data which is available in the radiology department. Big data is identified by four Vs – Volume, Velocity, Variety, and Veracity. By applying different algorithmic tools and converting raw data to transformed data in such large datasets, there is a possibility of understanding and using radiology data for gaining new knowledge and insights. Big data analytics consists of 6Cs – Connection, Cloud, Cyber, Content, Community, and Customization. The global technological prowess and per-capita capacity to save digital information has roughly doubled every 40 months since the 1980's. By using big data, the planning and implementation of radiological procedures in radiology departments can be given a great boost. Potential applications of big data in the future are scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, virtual quality assurance for the radiologist, etc. Targeted use of big data applications can be done for images by supporting the analytic process. Screening software tools designed on big data can be used to highlight a region of interest, such as subtle changes in parenchymal density, solitary pulmonary nodule, or focal hepatic lesions, by plotting its multidimensional anatomy. Following this, we can run more complex applications such as three-dimensional multi planar reconstructions (MPR), volumetric rendering (VR), and curved planar reconstruction, which consume higher system resources on targeted data subsets rather than querying the complete cross-sectional imaging dataset. This pre-emptive selection of dataset can substantially reduce the system requirements such as system memory, server load and provide prompt results. However, a word of caution, “big data should not become “dump data” due to inadequate and poor analysis and non-structured improperly stored data. In the near future, big data can ring in the era of personalized and individualized healthcare.

Trends in radiology and experimental research

European Radiology Experimental, the new journal launched by the European Society of Radiology, is placed in the context of three general and seven radiology-specific trends. After describing the impact of population aging, personalized/precision medicine, and information technology development, the article considers the following trends: the tension between subspecialties and the unity of the discipline; attention to patient safety; the challenge of reproducibility for quantitative imaging; standardized and structured reporting; search for higher levels of evidence in radiology (from diagnostic performance to patient outcome); the increasing relevance of interventional radiology; and continuous technological evolution. The new journal will publish not only studies on phantoms, cells, or animal models but also those describing development steps of imaging biomarkers or those exploring secondary end-points of large clinical trials. Moreover, consideration will be given to studies regarding: computer modelling and computer aided detection and diagnosis; contrast materials, tracers, and theranostics; advanced image analysis; optical, molecular, hybrid and fusion imaging; radiomics and radiogenomics; three-dimensional printing, information technology, image reconstruction and post-processing, big data analysis, teleradiology, clinical decision support systems; radiobiology; radioprotection; and physics in radiology. The journal aims to establish a forum for basic science, computer and information technology, radiology, and other medical subspecialties.

Image Sharing in Radiology-A Primer

By virtue of its information technology-oriented infrastructure, the specialty of radiology is uniquely positioned to be at the forefront of efforts to promote data sharing across the healthcare enterprise, including particularly image sharing. The potential benefits of image sharing for clinical, research, and educational applications in radiology are immense. In this work, our group-the Association of University Radiologists (AUR) Radiology Research Alliance Task Force on Image Sharing-reviews the benefits of implementing image sharing capability, introduces current image sharing platforms and details their unique requirements, and presents emerging platforms that may see greater adoption in the future. By understanding this complex ecosystem of image sharing solutions, radiologists can become important advocates for the successful implementation of these powerful image sharing resources.

Semi-automated detection of anterior cruciate ligament injury from MRI

BACKGROUND AND OBJECTIVES

A radiologist's work in detecting various injuries or pathologies from radiological scans can be tiresome, time consuming and prone to errors. The field of computer-aided diagnosis aims to reduce these factors by introducing a level of automation in the process. In this paper, we deal with the problem of detecting the presence of anterior cruciate ligament (ACL) injury in a human knee. We examine the possibility of aiding the diagnosis process by building a decision-support model for detecting the presence of milder ACL injuries (not requiring operative treatment) and complete ACL ruptures (requiring operative treatment) from sagittal plane magnetic resonance (MR) volumes of human knees.

METHODS

Histogram of oriented gradient (HOG) descriptors and gist descriptors are extracted from manually selected rectangular regions of interest enveloping the wider cruciate ligament area. Performance of two machine-learning models is explored, coupled with both feature extraction methods: support vector machine (SVM) and random forests model. Model generalisation properties were determined by performing multiple iterations of stratified 10-fold cross validation whilst observing the area under the curve (AUC) score.

RESULTS

Sagittal plane knee joint MR data was retrospectively gathered at the Clinical Hospital Centre Rijeka, Croatia, from 2007 until 2014. Type of ACL injury was established in a double-blind fashion by comparing the retrospectively set diagnosis against the prospective opinion of another radiologist. After clean up, the resulting dataset consisted of 917 usable labelled exam sequences of left or right knees. Experimental results suggest that a linear-kernel SVM learned from HOG descriptors has the best generalisation properties among the experimental models compared, having an area under the curve of 0.894 for the injury-detection problem and 0.943 for the complete-rupture-detection problem.

CONCLUSIONS

Although the problem of performing semi-automated ACL-injury diagnosis by observing knee-joint MR volumes alone is a difficult one, experimental results suggest potential clinical application of computer-aided decision making, both for detecting milder injuries and detecting complete ruptures.

Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images

Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

Advancing Precision Nuclear Medicine and Molecular Imaging for Lymphoma

PET with fluorodeoxyglucose F 18 (¹⁸F FDG-PET) is a meaningful biomarker for the detection, targeted biopsy, and treatment of lymphoma. This article reviews the evolution of ¹⁸F FDG-PET as a putative biomarker for lymphoma and addresses the current capabilities, challenges, and opportunities to enable precision medicine practices for lymphoma. Precision nuclear medicine is driven by new imaging technologies and methodologies to more accurately detect malignant disease. Although quantitative assessment of response is limited, such technologies will enable a more precise metabolic mapping with much higher definition image detail and thus may make it a robust and valid quantitative response assessment methodology.

Big data in oncologic imaging

Cancer is a complex disease and unfortunately understanding how the components of the cancer system work does not help understand the behavior of the system as a whole. In the words of the Greek philosopher Aristotle "the whole is greater than the sum of parts." To date, thanks to improved information technology infrastructures, it is possible to store data from each single cancer patient, including clinical data, medical images, laboratory tests, and pathological and genomic information. Indeed, medical archive storage constitutes approximately one-third of total global storage demand and a large part of the data are in the form of medical images. The opportunity is now to draw insight on the whole to the benefit of each individual patient. In the oncologic patient, big data analysis is at the beginning but several useful applications can be envisaged including development of imaging biomarkers to predict disease outcome, assessing the risk of X-ray dose exposure or of renal damage following the administration of contrast agents, and tracking and optimizing patient workflow. The aim of this review is to present current evidence of how big data derived from medical images may impact on the diagnostic pathway of the oncologic patient.

Imaging Informatics: 25 Years of Progress

The science and applications of informatics in medical imaging have advanced dramatically in the past 25 years. This article provides a selective overview of key developments in medical imaging informatics. Advances in standards and technologies for compression and transmission of digital images have enabled Picture Archiving and Communications Systems (PACS) and teleradiology. Research in speech recognition, structured reporting, ontologies, and natural language processing has improved the ability to generate and analyze the reports of imaging procedures. Informatics has provided tools to address workflow and ergonomic issues engendered by the growing volume of medical image information. Research in computeraided detection and diagnosis of abnormalities in medical images has opened new avenues to improve patient care. The growing number of medical-imaging examinations and their large volumes of information create a natural platform for "big data" analytics, particularly when joined with high-dimensional genomic data. Radiogenomics investigates relationships between a disease's genetic and gene-expression characteristics and its imaging phenotype; this emerging field promises to help us better understand disease biology, prognosis, and treatment options. The next 25 years offer remarkable opportunities for informatics and medical imaging together to lead to further advances in both disciplines and to improve health.

Precision Imaging: more descriptive, predictive and integrative imaging

Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.