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International research and collaboration has been a part of the National Cancer Institute’s (NCI) mission since its creation in 1937. Early on, efforts were limited to international exchange of information to ensure that US cancer patients could benefit from advances in other countries. As NCI’s research grant portfolio grew in the 1950s, it included a modest number of grants to foreign institutions, primarily in the United Kingdom and Europe. In the 1960s, the development of geographic pathology, which aimed to study cancer etiology through variations in cancer incidence and risk factors, led to an increase in NCI-funded international research, including research in low- and middle-income countries. In this paper, we review key international research programs, focusing particularly on the first 50 years of NCI history. The first NCI-led overseas research programs, established in the 1960s in Ghana and Uganda, generated influential research but also struggled with logistical challenges and political instability. The 1971 National Cancer Act was followed by the creation of a number of bilateral agreements with foreign governments, including China, Japan, and Russia, to support cooperation in technology and medicine. Although these agreements were broad without specific scientific goals, they provided an important mechanism for sustained collaborations in specific areas. With the creation of the NCI Center for Global Health in 2011, NCI’s global cancer research efforts gained sustained focus. Because the global cancer burden has evolved over time, increasingly impacting low- and middle-income countries, NCI’s role in global cancer research remains more important than ever.

Oral cavity cancer is a common cancer that can result in breathing, swallowing, drinking, eating problems as well as speech impairment, and there is high mortality for the advanced stage. Its diagnosis is confirmed through histopathology. It is of critical importance to determine the need for biopsy and identify the correct location. Deep learning has demonstrated great promise/success in several image-based medical screening/diagnostic applications. However, automated visual evaluation of oral cavity lesions has received limited attention in the literature. Since the disease can occur in different parts of the oral cavity, a first step is to identify the images of different anatomical sites. We automatically generate labels for six sites which will help in lesion detection in a subsequent analytical module. We apply a recently proposed network called ResNeSt that incorporates channel-wise attention with multi-path representation and demonstrate high performance on the test set. The average F1-score for all classes and accuracy are both 0.96. Moreover, we provide a detailed discussion on class activation maps obtained from both correct and incorrect predictions to analyze algorithm behavior. The highlighted regions in the class activation maps generally correlate considerably well with the region of interest perceived and expected by expert human observers. The insights and knowledge gained from the analysis are helpful in not only algorithm improvement, but also aiding the development of the other key components in the process of computer assisted oral cancer screening.

The NCI Center for Global Health convened the 9th Annual Symposium on Global Cancer Research as a virtual 2-day meeting alongside the Consortium of Universities for Global Health Annual Conference, March 10–11, 2021. The virtual format allowed for diverse and inclusive participation by over 400 attendees from 70 countries, 25+ speakers from 12 countries, and sharing of research conducted in 68 countries. The highly interactive 2-day program explored the science and complex considerations around resilience and equity in global cancer research and control. The Symposium convened individuals working in global oncology to discuss trends in global cancer research and control and map out collaborative efforts to move the field forward. The accepted scientific abstracts are published in this special supplement of AACR Cancer Epidemiology, Biomarkers, and Prevention.

According to the Pan American Health Organization, noncommunicable diseases, including cancer, are the leading causes of preventable and premature death in the Americas. Governments and health care systems in Latin America face numerous challenges as a result of increasing morbidity and mortality from cancer. Multiple international organizations have recognized the need for collaborative action on and technical support for cancer research and control in Latin America. The Center for Global Health at the US National Cancer Institute (NCI-CGH) is one entity among many that are working in the region and has sought to develop a strategy for working in Latin America that draws on and expands the collaborative potential of engaged, skilled, and diverse partners. NCI-CGH has worked toward developing and implementing initiatives in collaboration with global partners that share the common objectives of building a global cancer research community and translating research results into evidence-informed policy and practice. Both objectives are complementary and synergistic and are additionally supported by an overarching strategic framework that is focused on partnerships and science diplomacy. This work highlights the overall strategy for NCI-CGH engagement in Latin America through partnerships and diplomacy, and highlights selected collaborative efforts that are aimed at improving cancer outcomes in the region.

The steady increase in cancer incidence and mortality in the Asia-Pacific presents a growing challenge to countries in the region. National cancer control planning provides key strategies for countries to decrease their cancer burden and address risk factors in the context of their populations and health systems. Evidence-guided approaches allow countries to target resources towards highly-needed and effective cancer programs, while partnerships can serve to diversify sources of support, increase coverage and sustain services. Many countries struggle to develop and implement national cancer control plans (NCCPs) due to barriers in coordinating diverse stakeholders, limited funding and absence of evidence to guide program and health infrastructure improvements. The Cancer Control Leadership Forum (CCLF) program aims to address critical hurdles in cancer control planning by: (1) convening cancer control leaders from diverse sectors; (2) raising awareness and capacity of participating country representatives to develop and implement NCCPs; (3) facilitating development of country action-plans; and (4) providing technical assistance. Two years after participating in their first CCLF, eight countries from Southeast Asia and the Pacific, represented by multiple cancer control leaders from each country, participated in an Asia-Pacific Leadership Forum Phase II (APLF2) in 2016. The APLF2 facilitated valuable experience-exchange amongst regional and international experts, and explored areas of common interest, including: improving cancer registries, strengthening training, coordinating stakeholders, maximizing resources and overcoming implementation challenges. These priorities guide on-going cancer control efforts and serve to inform the international community of trends, needs and challenges in cancer control in Southeast Asia and the Pacific.

As the burden of non-communicable diseases such as cancer continues to rise in low- and middle-income countries (LMICs), it is essential to identify and invest in promising solutions for cancer control and treatment. Point-of-care technologies (POCTs) have played critical roles in curbing infectious disease epidemics in both high- and low-income settings, and their successes can serve as a model for transforming cancer care in LMICs, where access to traditional clinical resources is often limited. The versatility, cost-effectiveness, and simplicity of POCTs warrant attention for their potential to revolutionize cancer detection, diagnosis, and treatment. This paper reviews the landscape of affordable POCTs for cancer care in LMICs with a focus on imaging tools, in vitro diagnostics, and treatment technologies and aspires to encourage innovation and further investment in this space., This review explores the current landscape of point-of care (POC) tools available for cancer detection, diagnosis, and treatment in resource-limited settings. With a concerted effort to support international collaborations in technology development, fund promising POC technology concepts for translation in low-resource settings, and train the next generation of scientists in resource-appropriate design, the engineering research community can be a vital part of delivering quality cancer care to all patients.

Point-of-care (POC) technologies have proved valuable in cancer detection, diagnosis, monitoring, and treatment in the developed world, and have shown promise in low-and-middle-income countries (LMIC) as well. Despite this promise, the unique design constraints presented in low-resource settings, coupled with the variety of country-specific regulatory and institutional dynamics, have made it difficult for investigators to translate successful POC cancer interventions to the LMIC markets. In response to this need, the National Cancer Institute has partnered with the National Institute of Biomedical Imaging and Bioengineering to create the National Institutes of Health Affordable Cancer Technologies (ACTs) program. This program seeks to simplify the pathway to market by funding multidisciplinary investigative teams to adapt and validate the existing technologies for cancer detection, diagnosis, and treatment in LMIC settings. The various projects under ACTs range from microfluidic cancer diagnostic tools to novel treatment devices, each geared for successful clinical adaptation to LMIC settings. Via progression through this program, each POC innovation will be uniquely leveraged for successful clinical translation to LMICs in a way not before seen in this arena., This paper describes the purpose, formation, and early results of the National Institutes of Health Affordable Cancer Technologies Program, which aims to improve access to resource-appropriate technologies for cancer detection, diagnosis, monitoring and treatment in low- and middle-income countries.

Betel quid and areca nut are known risk factors for many oral and oesophageal cancers, and their use is highly prevalent in the Asia-Pacific region. Additionally, betel quid and areca nut are associated with health effects on the cardiovascular, nervous, gastrointestinal, metabolic, respiratory, and reproductive systems. Unlike tobacco, for which the WHO Framework Convention on Tobacco Control provides evidence-based policies for reducing tobacco use, no global policy exists for the control of betel quid and areca nut use. Multidisciplinary research is needed to address this neglected global public health emergency and to mobilise efforts to control betel quid and areca nut use. In addition, future research is needed to advance our understanding of the basic biology, mechanisms, and epidemiology of betel quid and areca nut use, to advance possible prevention and cessation programmes for betel quid and areca nut users, and to design evidence-based screening and early diagnosis programmes to address the growing burden of cancers that are associated with use.

This paper introduces a variant of the Demon's algorithm with an anisotropic smoothing term designed for the registration of surfaces with significant conformal differences. Because of the chosen implicit surface representation, the deformation field at each iteration can be easily defined with respect to the evolving surface. Anisotropic smoothing is performed preferentially in the tangent plane to the surface, preserving local, complex deformations. This method is applied to the registration of consecutive neonatal cortical surfaces from MR images acquired at 30 and 40 week gestational ages. Our method preserves the emergence of small, new folds, while greatly reducing noise in the resulting deformation field. © 2013 IEEE.

9 Cancer kills more people worldwide than HIV/AIDS, tuberculosis, and malaria combined, and lowand middle-incomecountries (LMICs) bear themajority of this burden.While success indetection, diagnosis, and treatment hasbeen reported in LMICs through theuse of low-cost, point-of-care (POC) technologies, this areahasbeen largely overlookedby themedical device industry and venture capital communities, as low-cost solutions offer less financial incentive for investment. The program presented here aims to simplify the pathway tomarket by funding investigation teams to adapt and validate existing technologies in low-resource settings. This program specifically supports the translation of these technologies, prioritizing patient outcomes in a manner not typically seen. This program, currently in its second year, will soon support 15 technologies for cancer detection, diagnosis, and treatment (e.g., in vitro assays, imaging devices, ablation devices). It is anticipated that by year seven of the program, at least nine projects will have progressed through optimization, clinical validation, and business planning for dissemination and commercialization. Each project consists of an adaptation phase (two years: $500k total costs/year) and validation phase (three years: $1M total costs/year). Projects are selected through the National Institutes of Health peer review process by a special emphasis panel briefed on the goals of the program. Projects are competitively vetted for Phase II funding based on completion of Phase I milestones. All teams contain expertise in engineering, oncology, and business/manufacturing, uniquely leveraging their devices for success in translation. The seven preliminarily funded projects are making strides in optimization, and the projects range from anLED-based photodynamic therapy device for oral cancer to POC tests for Hepatitis C viral antigen level and viral load detection. This program is funded by theNational Cancer Institute and theNational Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health. AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST: No COIs from the authors. Michael Gwede,Pushpa Tandon,Miguel Ossandon,Houston Baker,Rao Divi, Lokesh Agrawal, andPaul C. Pearlman, National Cancer Institute, National Institute of Health, Department of Health and Human Services, United States of America; and Vinay Pai and Tiffani Lash,National Institute ofBiomedical ImagingandBioengineering,National Institute ofHealth,Department ofHealthand Human Services, United States of America Corresponding author:Michael Gwede,National Cancer Institute, 9609Medical CenterDr, Rockville,MD20850; 240-276-7634; Michael.gwede@nih.gov DOI: 10.1200/JGO.2016.004499 Michael Gwede Pushpa Tandon Vinay Pai Miguel Ossandon Houston Baker Tiffani Lash Rao Divi Lokesh Agrawal Paul C. Pearlman 17s jgo.ascopubs.org JGO – Journal of Global Oncology © 2016 by American Society of Clinical Oncology Licensed under the Creative Commons Attribution 4.0 License

This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.

Optical breast imaging offers the possibility of noninvasive, low cost, and high sensitivity imaging of breast cancers. Poor spatial resolution and a lack of anatomical landmarks in optical images of the breast make interpretation difficult and motivate registration and fusion of these data with subsequent optical images and other breast imaging modalities. Methods used for registration and fusion of optical breast images are reviewed. Imaging concerns relevant to the registration problem are first highlighted, followed by a focus on both monomodal and multimodal registration of optical breast imaging. Where relevant, methods pertaining to other imaging modalities or imaged anatomies are presented. The multimodal registration discussion concerns digital x-ray mammography, ultrasound, magnetic resonance imaging, and positron emission tomography. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).

Cancer kills more people worldwide than HIV/AIDS, tuberculosis and malaria combined, and low-and-middle income countries (LMICs) bear the majority of this burden. Success in detection, diagnosis and treatment has been reported in LMICs through the use of low-cost point-of-care (POC) technologies, and the program presented offers a unique pathway to this POC market by funding multidisciplinary investigative teams to adapt and clinically validate existing technologies for cancer detection, diagnosis and treatment in low-resource settings. Each project consists of an adaptation phase (2 years: $500k total costs/year) and validation phase (3 years: $1M total costs/year). Projects are selected through NIH peer review process by a carefully-selected special emphasis panel briefed on the goals of the program. Projects are competitively vetted for Phase II funding based on completion of Phase I milestones. The program currently supports seven technologies for cancer detection, diagnosis and treatment, each of which is progressing towards experimental and clinical validation. The first project is an LED-based photodynamic therapy device for oral cancer, that has similar efficacy in vivo and ex vivo as existing laser phototherapy. Another supported project is an automated high resolution microendoscope for cervical cancer detection, with an impressive histological concordance in detecting CIN2/3 (90%+ for CIN3). Two cervical cancer cryotherapy projects are funded: a cryopen, that can achieve an approximately 4.0 mm depth of necrosis (>90% of disease) for cervical cancer treatment, and an efficient cryopop device that consumes less than 10% of CO2, compared to commercially-available devices and exhibits comparable therapeutic efficacy in ballistic gel studies. The program is also supporting two POC tests, a HPV test and a Hepatitis C viral antigen level and viral load detection. Additionally, a breast cancer triaging device/algorithm, with 95% sensitivity and capabilities to reduce false positive detection rate by 40%, is also being supported. Each project has its own detailed outline for Phase I and Phase II studies, which will be highlighting in our presentation. The program is in the process of adding another six projects, and it is anticipated that by year seven of the program, at least nine projects will have progressed through optimization, clinical validation, and business planning for commercialization and field/clinic dissemination. Through these process, we will uniquely accelerate these technologies for success in clinical translation. Citation Format: Michael Gwede, Paul Pearlman, Pushpa Tandon, Miguel Ossandon, Lokesh Agrawal, Houston Baker, Vinay Pai, Tiffani Lash. The program for cancer detection, diagnosis, and treatment technologies for global health: Translating affordable, minimally invasive point-of-care technologies to less-resourced settings. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1428.

Abstract 9 Cancer kills more people worldwide than HIV/AIDS, tuberculosis, and malaria combined, and low- and middle-income countries (LMICs) bear the majority of this burden. While success in detection, diagnosis, and treatment has been reported in LMICs through the use of low-cost, point-of-care (POC) technologies, this area has been largely overlooked by the medical device industry and venture capital communities, as low-cost solutions offer less financial incentive for investment. The program presented here aims to simplify the pathway to market by funding investigation teams to adapt and validate existing technologies in low-resource settings. This program specifically supports the translation of these technologies, prioritizing patient outcomes in a manner not typically seen. This program, currently in its second year, will soon support 15 technologies for cancer detection, diagnosis, and treatment (e.g., in vitro assays, imaging devices, ablation devices). It is anticipated that by year seven of the program, at least nine projects will have progressed through optimization, clinical validation, and business planning for dissemination and commercialization. Each project consists of an adaptation phase (two years: $500k total costs/year) and validation phase (three years: $1M total costs/year). Projects are selected through the National Institutes of Health peer review process by a special emphasis panel briefed on the goals of the program. Projects are competitively vetted for Phase II funding based on completion of Phase I milestones. All teams contain expertise in engineering, oncology, and business/manufacturing, uniquely leveraging their devices for success in translation. The seven preliminarily funded projects are making strides in optimization, and the projects range from an LED-based photodynamic therapy device for oral cancer to POC tests for Hepatitis C viral antigen level and viral load detection. This program is funded by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST: No COIs from the authors.

To evaluate if clinical fluorescence imaging of IRDye800CW is feasible on our fluorescence optical mammography system by estimating detection limits assessed by breast-cancer-simulating phantom experiments. Phantoms (2.1 cm(3), 0.9 cm(3)) with IRDye800CW concentrations of 0.5 to 120 nM were suspended in a 550 cm(3) measurement cup containing 507 surface-mounted source and detector fibers. The cup was filled with optical matching fluid containing IRDye800CW concentrations of 0, 5, 10, or 20 nM. Tomographic fluorescence images were acquired by exciting IRDye800CW at 730 nm; wavelengths above 750 nm were filtered. Signal intensities were calculated over a volume of interest corresponding to the size and location of the phantom in the reconstructed images. Correlations (R(2)) were calculated, and detection limits with associated upper 95% prediction interval were estimated. Between-day reproducibility was assessed with intraclass correlation coefficients (ICC). Fluorescent intensities were strongly correlated with phantom IRDye800CW concentrations (R(2)0.983 to 0.999). IRDye800CW detection limits ranged from 0.14 to 2.46 nM (upper 95% prediction limit 4.63 to 18.63 nM). ICC ranged from 0.88 to 1.00. The estimated detection limits for IRDye800CW were in the low-nanomolar range. These results support the start of clinical trials to evaluate the fluorescence optical mammography system using IRDye800CW labeled breast cancer targeting ligands.

We present an approach for left ventricular segmentation of radio-frequency (RF) ultrasound sequences. Our method employs an independent identically distributed (iid) spatial model for RF voxel intensity and a conditional model relating a subsequent frame in the image sequence to the frame being segmented by means of a linear predictor that exploits spatio-temporal coherence in the data. The conditional model overcomes segmentation problems due to image inhomogeneity issues, while the spatial model overcomes a tendency of the conditional model to underestimate the blood pool. The method is validated by comparison with manual tracings, segmentations performed using Chan-Vese level sets, and by segmentations leveraging only the linear predictor.