Cancer Imaging Research Research Articles

Exploring the State of Cancer Imaging Research in Africa

IntroductionThe growing cancer burden in Africa demands urgent action. Medical imaging is crucial for cancer diagnosis and management and is an essential enabler of precision medicine. To understand the readiness for quantitative imaging analysis to support cancer management in Africa, we analyzed the utilization patterns of imaging modalities for cancer research across the continent. MethodsWe retrieved articles by systematically searching PubMed, using a combination of search terms {“Neoplasm”} AND {“Radiology” or “Diagnostic imaging” or “Radiography” or “Interventional Radiology” or “Radiotherapy” or “Radiation Oncology”} AND {Africa∗ or 54 African countries}. Articles describing cancer diagnosis or management in humans with the utilization of imaging were included. Exclusion criteria were review articles, non-English articles, publications before 2000, noncancer diagnoses, and studies conducted outside Africa. ResultsThe analysis of diagnostic imaging in Africa revealed a diverse utilization pattern across different cancer types and regions. The literature search identified 107 publications on cancer imaging in Africa. The studies were carried out in 19 African countries on 12 different cancer types with 6 imaging modalities identified. Most cancer imaging research studies used multiple imaging modalities. Ultrasound was the most used distinct imaging modality and MRI was the least frequently used. Most research studies originated from Nigeria, South Africa, and Egypt. ConclusionWe demonstrate substantial variability in the presence of imaging modalities, widespread utilization of ultrasonography, and limited availability of advanced imaging modalities for cancer research.

Machine learning in breast cancer imaging: a review on data, models and methods

ABSTRACT Medical imaging research has experienced significant growth over the past decade, particularly in the fields of computer vision and pattern recognition. Computational approaches have been proposed to address the challenges in breast cancer detection, classification and segmentation. However, recent advancements in computational technology such as machine learning (ML) methods have dramatically changed the landscape of breast cancer imaging research. This survey aims to provide a compilation of information for future breast cancer imaging researchers by 1) comprehensively examining how various ML techniques are being used to address the main challenges in breast cancer imaging; 2) providing an in-depth discussion and review of publicly available datasets for the development and evaluation of novel breast cancer detection, classification and segmentation approaches; and 3) outlining current evaluation metrics used by breast cancer imaging researchers. The insights and findings presented in this survey will serve as a valuable resource for researchers and clinicians interested in breast cancer imaging. By providing an overview of the current state-of-the-art techniques and highlighting areas for future research, we hope to facilitate the development of more accurate and effective breast cancer imaging ML techniques, and contribute to advancing our understanding and improving the diagnosis and treatment of breast cancer.

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

Imaging in translational cancer research.

This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool.

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool's aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance- Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development.

Mapping the cancer imaging research landscape: which cancers are more and which cancers are less frequently investigated?

ObjectiveTo investigate the proportion of published imaging studies relative to incidence and mortality rate per cancer type. MethodsFrom a random sample of 2500 articles published in 2019 by the top 25 imaging-related journals, we included cancer imaging studies. The publication-to-incidence and publication-to-mortality ratios (defined as the publication rate divided by the proportional incidence and mortality rate, respectively) were calculated per cancer type. Ratios >1 indicate a higher publication rate compared to the relative incidence or mortality rate of a specific cancer. Ratios <1 indicate a lower publication rate compared to the relative incidence or mortality rate of a specific cancer. Results620 original cancer imaging studies were included. Female breast cancer (20.2%), prostate cancer (13.0%), liver cancer (12.9%), lung cancer (8.8%), and cancers in the central nervous system (8.1%) comprised the top 5 of cancers investigated. Cancers in the central nervous system and liver had publication-to-incidence ratios >2, whereas nonmelanoma of the skin, leukemia, stomach cancer, and laryngeal cancer had publication-to-incidence ratios <0.2. Cancers in the prostate, central nervous system, female breast, and kidney had publication-to-mortality ratios >2, whereas esophageal cancer, stomach cancer, laryngeal cancer, and leukemia had publication-to-mortality ratios <0.2. ConclusionThis overview of published cancer imaging research may be informative and useful to all stakeholders in the field of cancer imaging. The potential causes of disproportionality between the publication rate vs. incidence and mortality rates of some cancer types are multifactorial and need to be further elucidated.

Artificial Intellgence in the Era of Precision Oncological Imaging.

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

NCI Imaging Data Commons.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

Prostate Cancer Imaging Research: The Emerging Importance of Patient-Centered Outcomes.

Prostate cancer is the second most common cancer among American men. Because of an aging global population, the incidence of prostate cancer is increasing. Given that the 5-year survival rate of patients with metastatic prostate cancer is 30%, but approximately 90% in those with local or regional disease, accurate staging is necessary for prognostication and treatment decisions. However, mortality due to prostate cancer (31,620 new deaths estimated in 2019) is relatively low compared with its incidence (174,650 new cases estimated in 2019) because some prostate cancers are clinically insignificant [ 2 American Cancer SocietyFacts & figures 2019. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdfDate accessed: December 15, 2019 Google Scholar ]. Thus, although a highly sensitive method for prostate cancer detection is desirable to identify and treat early-stage disease, differentiation of clinically significant cancers from those that are clinically insignificant is needed to avoid overdiagnosis and overtreatment.

The ICTBioMed NCIP Hub: Cancer research in a science gateway consortium

ICTBioMed, the International Consortium for Technology in Biomedicine is a consortium for biomedical collaborative research and the consortium partners believe in decentralization and democratization of science. The consortium works on strategies to develop ready-to-use research initiatives bridging the gap between research, implementation and ready-to-use science. The consortium has identified real science use cases with the help of the collaborative partners to get access of shared data and methods using the principles of science gateways. ICTBioMed has set up an NCIP Hub instance based upon the HUBzero framework and started using it for community research activities. It is an environment for scientific collaboration where researchers can access and take advantage of a variety of resources shared by others. The NCIP Hub supports members with shared access to data, tools, and standards across the cancer research community. Cancer researchers, in principle, have access to a wide range of data and software tools but in practice, however, they make limited use of these resources. Barriers that restrict access include the need to install software locally, inconsistent user interfaces, security concerns and poor documentation. ICTBioMed is developing a cancer science gateway that will greatly simplify analysis in cancer imaging research by offering a single point of entry and a unified interface for multiple tools, databases, and image repositories widely used by the community. The consortium aims at borderless research with a common goal to solve real life biomedical problems by facilitating collaborative research.

S01.23 Evolution of ELIC

The Early Lung Imaging Confederation (ELIC) Hub & Spoke Environment (H&SE) is a new globally distributed and open source lung cancer imaging database and computational analysis environment designed to significantly improve the cost, time, and quality of lung cancer imaging research. This IASLC led initiative and computational infrastructure project, when fully deployed, will allow clinical research groups (Spokes) to securely make their locally stored de-identified lung cancer imaging collections available for computational analysis by other research groups (Clients), all coordinated by a central IASLC managed server (Hub). Clinical sites will be able to make lung cancer imaging data available for specific types of computational analysis without transmitting the imaging data over national boundaries to other groups and losing control over how the data is used and further distributed. This allows lung cancer screening research groups to more easily make available datasets to large global lung cancer imaging research studies with far more control over data use. This federated data storage and analysis approach will allow the ELIC H&SE to scale to much larger data sizes than a traditional centralized database, one day allowing lung cancer imaging researchers to quickly and easily perform quantitative analysis on global lung cancer imaging studies with larger collections of high quality, standardized data than is attainable today. This is viewed as a critical next step for the development of next generation Artificial Intelligence algorithms for lung cancer imaging, which require large amounts of data for algorithm development and performance evaluation. In preparation for the 2018 IASLC WCLC meeting in Toronto, Canada, a proof-of-concept ELIC Hub and Spoke Environment was developed and set up using Amazon Web Services (AWS) cloud resources. A hub was set up on a Virginia AWS cloud instance and 10 spokes, each pre-populated with an identical set of 100 de-identified CT lung scans, were set up at 10 globally distributed AWS cloud locations including Mumbai, London, Frankfurt, Montreal, Sydney, Tokyo, Paris, Seoul, Sao Paulo, and Virginia (on a separate cloud instance). Two open source lung cancer imaging algorithms, one that automatically computes lung volume for a thoracic CT scan and another for volumetric measurement of small lung nodules, were made available for use by the 10 spoke instances. Live demonstrations of the proof-of-concept system were shown at the 2018 WCLC meeting including the ability to launch computational experiments and receive back quantitative results from the 10 globally distributed spokes. Figure 1 shows the global distribution of the hub and spokes for the 2018 WCLC ELIC proof of concept demonstrations. The live demonstrations showed that the ELIC H&SE could be used to select globally distributed datasets available on the spokes for analysis, run specific computational algorithms on those datasets, and have all of the results aggregated in real-time for viewing on the hub, as shown in Figure 2. The ELIC H&SE infrastructure is now undergoing further development in 2019 to bring it from a proof-of-concept demonstration to a functional globally distributed database and computational environment capable of performing useful quantitative lung cancer imaging studies. References to tools and resources for performing data de-identification and encryption are being added to support research groups that will be uploading lung imaging datasets and metadata into the ELIC H&SE. Standards for lung cancer screening data representation, starting with a lung cancer screening data dictionary developed by the VA-Partnership to increase Access to Lung Screening (VA-PALS) project, are also being added to ensure that global analyses can be performed with common terminology and data formats. In addition, the Radiological Society of North America’s Quantitative Imaging Biomarker Alliance (QIBA) small lung nodule conformance certification phantom, specifications, and methods are being used to help lung cancer screening sites prospectively collect, monitor, and optimize lung cancer imaging studies for high quality volume measurements. All of these resources and formats are planned to be reviewed with all ELIC stakeholders on a quarterly basis to receive feedback and refine the systems and methods. There are numerous functionality advantages for spokes that use local cloud computing resources including significantly improved security for both clients and spokes, improved computational efficiency through on-demand cloud resourcing, and continuously updated hardware and infrastructure. Additional software development is underway that will allow ELIC to achieve these advantages for cloud-based deployments. Live demonstrations are again planned for the 2019 IASLC WCLC meeting in Barcelona, Spain showing an early demonstration of a new iaslc-elic.org website capable of supporting both spokes and clients performing globally distributed lung cancer imaging research studies.Figure 2ELIC H&SE live demonstration screenshots showing the ability to view spoke status, select globally distributed datasets for analysis, and view a list of completed experiments (left) as well as drill down and view statistical experiment results including computationally generated images (right).View Large Image Figure ViewerDownload Hi-res image Download (PPT) Lung Cancer Imaging, Computed tomography, lung cancer screening

Reengineering Workflow for Curation of DICOM Datasets

Reusable, publicly available data is a pillar of open science and rapid advancement of cancer imaging research. Sharing data from completed research studies not only saves research dollars required to collect data, but also helps insure that studies are both replicable and reproducible. The Cancer Imaging Archive (TCIA) is a global shared repository for imaging data related to cancer. Insuring the consistency, scientific utility, and anonymity of data stored in TCIA is of utmost importance. As the rate of submission to TCIA has been increasing, both in volume and complexity of DICOM objects stored, the process of curation of collections has become a bottleneck in acquisition of data. In order to increase the rate of curation of image sets, improve the quality of the curation, and better track the provenance of changes made to submitted DICOM image sets, a custom set of tools was developed, using novel methods for the analysis of DICOM data sets. These tools are written in the programming language perl, use the open-source database PostgreSQL, make use of the perl DICOM routines in the open-source package Posda, and incorporate DICOM diagnostic tools from other open-source packages, such as dicom3tools. These tools are referred to as the “Posda Tools.” The Posda Tools are open source and available via git at https://github.com/UAMS-DBMI/PosdaTools. In this paper, we briefly describe the Posda Tools and discuss the novel methods employed by these tools to facilitate rapid analysis of DICOM data, including the following: (1) use a database schema which is more permissive, and differently normalized from traditional DICOM databases; (2) perform integrity checks automatically on a bulk basis; (3) apply revisions to DICOM datasets on an bulk basis, either through a web-based interface or via command line executable perl scripts; (4) all such edits are tracked in a revision tracker and may be rolled back; (5) a UI is provided to inspect the results of such edits, to verify that they are what was intended; (6) identification of DICOM Studies, Series, and SOP instances using “nicknames” which are persistent and have well-defined scope to make expression of reported DICOM errors easier to manage; and (7) rapidly identify potential duplicate DICOM datasets by pixel data is provided; this can be used, e.g., to identify submission subjects which may relate to the same individual, without identifying the individual.

Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group.

Purpose To evaluate interradiologist agreement on assessments of computed tomography (CT) imaging features of high-grade serous ovarian cancer (HGSOC), to assess their associations with time-to-disease progression (TTP) and HGSOC transcriptomic profiles (Classification of Ovarian Cancer [CLOVAR]), and to develop an imaging-based risk score system to predict TTP and CLOVAR profiles. Materials and Methods This study was a multireader, multi-institutional, institutional review board-approved, HIPAA-compliant retrospective analysis of 92 patients with HGSOC (median age, 61 years) with abdominopelvic CT before primary cytoreductive surgery available through the Cancer Imaging Archive. Eight radiologists from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group developed and independently recorded the following CT features: characteristics of primary ovarian mass(es), presence of definable mesenteric implants and infiltration, presence of other implants, presence and distribution of peritoneal spread, presence and size of pleural effusions and ascites, lymphadenopathy, and distant metastases. Interobserver agreement for CT features was assessed, as were univariate and multivariate associations with TTP and CLOVAR mesenchymal profile (worst prognosis). Results Interobserver agreement for some features was strong (eg, α = .78 for pleural effusion and ascites) but was lower for others (eg, α = .08 for intraparenchymal splenic metastases). Presence of peritoneal disease in the right upper quadrant (P = .0003), supradiaphragmatic lymphadenopathy (P = .0004), more peritoneal disease sites (P = .0006), and nonvisualization of a discrete ovarian mass (P = .0037) were associated with shorter TTP. More peritoneal disease sites (P = .0025) and presence of pouch of Douglas implants (P = .0045) were associated with CLOVAR mesenchymal profile. Combinations of imaging features contained predictive signal for TTP (concordance index = 0.658; P = .0006) and CLOVAR profile (mean squared deviation = 1.776; P = .0043). Conclusion These results provide some evidence of the clinical and biologic validity of these image features. Interobserver agreement is strong for some features, but could be improved for others. © RSNA, 2017 Online supplemental material is available for this article.

Development and Investigation of Ultrastable PbS/CdS/ZnS Quantum Dots for Near‐Infrared Tumor Imaging

Achieving bright, reliable, robust, and stable probes for in vivo imaging is becoming extremely urgent for the cancer imaging research community. To date very few works have reported on elucidating in the varied and chemically complex biological milieu. The authors report detailed investigations of the synthesis of near‐infrared, water dispersive, strongly luminescent, and highly stable PbS/CdS/ZnS core/shell/shell quantum dots (QDs). These QDs are extremely stable, they could keep their initial morphology, dispersion status, and photoluminescence (PL) in phosphate buffered saline buffer for as long as 14 months. The QDs also show excellent photostability and could keep ≈80% of their initial PL intensity after 1 h continuous, strong UV illumination. More interestingly, they show negligible toxicity to cultured cells even at high QDs concentration. Given these outstanding properties, the QDs are explored for in vivo, tumor imaging in mice. With one order of magnitude lower QD concentration (0.04 mg mL–1), significantly weaker laser intensity (0.04 W cm–2 vs ≈1 W cm–2), and considerably shorter signal integration time (≤1 ms vs hundreds of ms) as compared to the best reported rare earth doped nanoparticles, the QDs show high emission intensity even at injection depth of ≈2.5 mm.

Cancer Imaging at the Crossroads of Precision Medicine: Perspective From an Academic Imaging Department in a Comprehensive Cancer Center

The authors propose one possible vision for the transformative role that cancer imaging in an academic setting can play in the current era of personalized and precision medicine by sharing a conceptual model that is based on experience and lessons learned designing a multidisciplinary, integrated clinical and research practice at their institution. The authors’ practice and focus are disease-centric rather than imaging-centric. A “wall-less” infrastructure has been developed, with bidirectional integration of preclinical and clinical cancer imaging research platforms, enabling rapid translation of novel cancer drugs from discovery to clinical trial evaluation. The talents and expertise of medical professionals, scientists, and staff members have been coordinated in a horizontal and vertical fashion through the creation of Cancer Imaging Consultation Services and the “Adopt-a-Radiologist” campaign. Subspecialized imaging consultation services at the hub of an outpatient cancer center facilitate patient decision support and management at the point of care. The Adopt-a-Radiologist campaign has led to the creation of a novel generation of imaging clinician-scientists, fostered new collaborations, increased clinical and academic productivity, and improved employee satisfaction. Translational cancer research is supported, with a focus on early in vivo testing of novel cancer drugs, co-clinical trials, and longitudinal tumor imaging metrics through the imaging research core laboratory. Finally, a dedicated cancer imaging fellowship has been developed, promoting the future generation of cancer imaging specialists as multidisciplinary, multitalented professionals who are trained to effectively communicate with clinical colleagues and positively influence patient care.

Structural and Functional Roles of Collagen 1 Fibers in Breast Cancer Metastasis: Collagen 1 Fiber Density Increases in Lymph Node-Positive Breast Cancers

JHU ICMIC Program, Division of Cancer Imaging Research, The Russell H Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 208C Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205, USA Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA *Author for correspondence: zaver@mri.jhu.edu The role of collagen 1 (Col1) fibers in breast cancer metastasis is attracting increasing attention based on studies that have observed a clear association between these fibers, the metastatic process [1], hypoxia [2] and macromolecular transport [3]. Interactions between cancer cells and the surrounding extracellular matrix (ECM) seemingly potentiate the malignant phenotype [4,5]. The tumor ECM comprises of collagens, glycoproteins and proteoglycans [6]. Cancer cells actively secrete a host of proteolytic enzymes that continuously remodel the ECM [7]. Lysosomal cathepsins participate in cleaving fibrillar collagen [8], laminin [9] and fibronectin [10] intracellularly, and also following secretion. A major component of the ECM in tumors is Col1 [6,11,12] that is part of the fibrillar collagen family. Amongst the more than 20 types of collagen proteins, collagen type 1 is the most abundant in the human body and its main role is to provide strength, support and structure to tissues [13]. Mature Col1 fibers can reach up to 500 nm by 1 cm in size [14]. Col1 fiber formation is a multistep process that starts intracellularly and ends with complete mature Col1 fibers formed outside the cell. Inside the cell, pro-collagen 1 forms a triple helix derived from the Col1A1 and Col1A2 genes [15]. Pro-collagen 1 is secreted outside the cell and converted to tropocollagen, which in turn selfassembles and is crosslinked to form mature Col1 fibers. Malignant breast cancers are characterized by significantly higher Col1 fiber density and altered Col1 fiber architecture [12]. Deregulated and dense deposition of collagen fibers is often found surrounding the tumor, which promotes tumor progression and metastasis [12] and affects drug delivery in tumors [16,17]. Second harmonic generation (SHG) microscopy is increasingly being used to characterize Col1 fibers [16]. SHG microscopy does not require exogenous labeling and can intrinsically detect molecules like Col1 fibers that have a noncentrosymmetric molecular structure by the phase matching phenomena that gives rise to a second harmonic signal. SHG microscopy has the advantage of detecting Col1 fibers from fresh tissue sections, preserving 3D structural patterns, and can be potentially used in vivo with an intravital endoscope [18]. Novel computational algorithms, including Haralick feature analysis, are being applied to characterize Col1 fiber patterns to relate structural features to functional characteristics [1].

Global Trend in Breast Cancer Imaging Research 1992–2012: Bibliometric Study

AJR:202, March 2014 Heading (MeSH) terms, which are standardized labels assigned by the National Library of Medicine for the purpose of identifying articles by their subject matter. Country of origin, methodology, specialty of the first author, and funding sources (for U.S. articles) were recorded. Growth was analyzed by using linear and nonlinear regression. We included a total of 9984 articles from 1141 distinct journals. The five leading journals by number of published studies are the American Journal of Roentgenology (490), Radiology (464), Medical Physics (260), European Radiology (221), and Academic Radiology (221). The number of publications from leading nations included the United States (41%, 4102 articles), the United Kingdom (8.0%, 795 articles), Italy (5.1%, 507 articles), Germany (4.8%, 479 articles), and Japan (4.7%, 469 articles). Growth globally and in the United States followed a linear pattern Global Trend in Breast Cancer Imaging Research 1992–2012: Bibliometric Study

The Three NITRC's: Software, Data and Cloud Computing for Brain Science and Cancer Imaging Research

The Three NITRC's: Software, Data and Cloud Computing for Brain Science and Cancer Imaging Research

The future of imaging colorectal cancer

Dr Gina Brown is the NHS theme lead in cancer imaging research at The Royal Marsden, Surrey, UK. She holds honorary positions with The Institute of Cancer Research and Imperial College London. Dr Brown also has long-established research links with the Pelican Cancer Foundation in Basingstoke, a charity committed to developing and teaching improvements in cancer surgery and providing research funding for various projects throughout the UK and Europe. As a cancer imaging representative on the National Cancer Research Institute Colorectal Clinical Studies Group and Subgroups, Dr Brown’s role has been to ensure that high-quality imaging is embedded into national colorectal clinical trial protocols to introduce best practice to routine clinical care. As a result of this, innovative trials such as Aristotle, FOXTROT study, multicenter Royal Marsden-led EXPERT-C and the Deferral of Surgery Trial have all incorporated high-quality imagery end points. Dr Brown has published over 100 peer-reviewed papers in gastrointestinal cancer imaging and provided educational material for many national and international courses. She has authored chapters in major educational text books and edited and coauthored Contemporary Issues in Cancer Imaging: Colorectal Cancer.

Advertising Campaign Puts Face on Prostate Cancer

Meet Prosty the Spokesgland. The plucky prostate recently debuted on Capitol Hill, starring in a television advertisement designed to rally support for research into effective imaging tests for prostate cancer. “ Women have the mammogram, ” said Faina Shtern, M.D., president and chief executive offi cer of the nonprofi t AdMeTech Foundation, which organized the October press event. “ Men want to know, ‘ Where is our man-o-gram? ’ ” Last year AdMeTech, which Shtern founded in 2000, decided to focus on drawing philanthropy and government funding dedicated to prostate cancer imaging research. The foundation received Department of Defense grants in 2000 and 2002, which it used to fund research into molecular tags that could highlight incipient prostate tumors. “ We want to convince Congress that funding in this area is vital, ” said Shtern, the former chief of the diagnostic imaging research branch at the National Cancer Institute. The Prosty campaign personifi es the problem as Shtern and her coalition see it. After the gland gets poked by a gloved fi nger and pierced by biopsy needles, he spins around and points to a throbbing red pustule on his backside. “ Hey, you missed a cancer! ” he protests. Shtern’s coalition also wants to reduce the number of biopsies that men undergo. “ The number of unnecessary biopsies is staggering, ” she said. Bill Nelson, M.D., Ph.D., professor of oncology at Johns Hopkins University in Baltimore, said that molecular tagging — designing molecules that latch onto prostate cancer cells for various scanners to read — is the most promising prostate imaging technology under development. But he cautioned that an effective test is years away. In other words, Prosty might have a job for a while. “ I didn’t want this campaign to be some boring doctor talking in front of some plant in his boring offi ce, ” said Stephen Nemeth, the Hollywood producer who created Prosty. “ I wanted to shake things up, get men talking about their prostates. ” Shtern said that the 30second Prosty advertisement will begin airing next year. The video and other campaign materials are available online at http://www.manogram.org .