Women in Cancer Imaging and Image-directed Interventions: 2021


Book Description

We are delighted to present the inaugural Frontiers in Oncology "Women in Cancer Imaging and Image-directed Interventions” series of article collections. At present, less than 30% of researchers worldwide are women. Long-standing biases and gender stereotypes are discouraging girls and women away from science-related fields, and STEM research in particular. Science and gender equality are, however, essential to ensure sustainable development as highlighted by UNESCO. In order to change traditional mindsets, gender equality must be promoted, stereotypes defeated, and girls and women should be encouraged to pursue STEM careers.










Case Reports in Cancer Imaging and Image-directed Interventions : 2022


Book Description

This Research Topic aims to collect all the Case Reports submitted to the Cancer Imaging and Image-directed Interventions section. All the Case Reports submitted to this collection will be personally assessed by a senior Associate Editor before the beginning of the peer-review process. Please make sure your article adheres to the following guidelines before submitting it.




Methods in Cancer Imaging and Image-directed Interventions


Book Description

Frontiers in Oncology is delighted to present the Methods in series of article collections. Methods in Cancer Imaging and Image-directed Interventions will publish high-quality methodical studies on key topics in the field. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. The Methods in Cancer Imaging and Image-directed Interventions collection aims to highlight the latest experimental techniques and methods used to investigate fundamental questions in Cancer Imaging and Image-directed Interventions. Review Articles or Opinion Articles on methodologies or applications including the advantages and limitations of each are welcome. This Research Topic includes technologies and up-to-date methods which help aim to help advance science.







World Cancer Day 2021: A Retrospective


Book Description

Cancer accounts for millions of deaths every year, and the burden of this disease is striking - testing our families, health-care systems, economies, and our scientists. In recent years, the outstanding work of researchers and vast improvements in technology has led to remarkable strides in progress. We are now able to prevent at least one third of cancers and have adapted routine-screening techniques for early detection and effective treatment. Our ability to treat and manage this shape-shifting disease has also transformed, as we have developed sophisticated therapies and adopted more tailored approaches. As a result, survival rates are reaching new highs each year, and the outlook for those affected is improving. However, there are still areas that require our attention. Unfortunately, inequalities are well known in the field. In areas where resources are scarce and outreach is limited, cancer patients do not have access to educational programs, timely diagnosis and quality treatment. Significant knowledge-gaps also exist within cancer research, with many minority populations being underrepresented in clinical trials and underreported within the literature. Considering that scientific progress relies on the publication and dissemination of research, the lack of access to primary literature also falters, with many breakthroughs hidden behind paywalls. This not only affects clinicians and researchers, reinforcing a negative feedback-loop for researchers already struggling to obtain sufficient funding, but inhibits the next generation of curious students. Each year, February 4th marks World Cancer Day; a movement dedicated to channeling awareness, education, and unity into collective initiatives and global action against one of medicine’s toughest challenges. The theme of 2021, “I Am and I Will” was one of power, encouraging commitment and togetherness; a sentiment resonating in today’s turbulent world. In honor of this day, Frontiers in Oncology has invited a retrospective of articles from our Specialty Chief Editors, highlighting current, international challenges in their corresponding fields of oncology. Our goal is to empower continuous discussion between communities and across borders, drawing attention to the disparities faced in the field. Our achievements should be shared to maximize impact and facilitate opportunities worldwide. We know that cancer does not discriminate. So, neither should we. We also take this opportunity to thank the wider community for their continued efforts in allowing for accelerated scientific developments, and most importantly for working with us on our mission to make science open. Nicola Faramarzi, PhD On behalf of the Frontiers in Oncology Editorial Office










Artificial Intelligence in Digital Pathology Image Analysis


Book Description

Thanks to the development and deployment of whole-slide imaging technology in pathology, glass slides previously observed under a traditional microscope are now scanned and converted to digital images, which are more beneficial for remote access, portability, and ease of sharing to facilitate telepathology. More importantly, digitization of glass slides paves the way towards the wide use of artificial intelligence (AI) tools including machine/deep learning algorithms, resulting in improved diagnostic accuracy. In the past decade, a large number of studies have demonstrated the remarkable success of AI, particularly deep learning, in digital pathology, such as tumor region identification, metastasis detection, and patient prognosis. Differing from handcrafted feature-based approaches that take advantage of domain knowledge to delineate specific morphological measurements (e.g., nuclei shape and size and tissue texture) in the images as features for training, deep learning is a paradigm of feature learning entirely driven by the image data and/or labels. Herein, the use of deep learning in pathological diagnosis can not only handle increased workloads and expertise shortages but also obviate subjective diagnosis from pathologists. Yet there remain many scientific and technological challenges associated with the efficiency of deep learning algorithms for use in clinical practice. For example, deep learning requires a sufficient amount of training data for generalization and suffers from a lack of feature interpretability. The overarching goal of this special issue is to highlight novel research accomplishments and directions, related to advanced AI methodology development and applications in digital pathology.