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Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required., peer-reviewed

When the National Health Service (NHS) acquired a statutory duty of care for quality in 1998, clinical governance became a mandatory and intrinsic part of modern medicine. Defined as “a framework through which NHS organisations are accountable for continuously improving the quality of their services and safe-guarding high standards of care by creating an environment in which excellence in clinical care will flourish”,1 the vehicle for NHS consultants to enact clinical governance was supporting professional activity (SPA). All activities that underpin direct clinical care (DCC) are encouraged during SPA time, including professional development, research, audit, teaching, clinical management, appraisal, and job planning.2,3 Adequate time for SPAs alongside DCC is therefore crucial for NHS consultants to maintain excellence in clinical care.3 The recently published Royal College of Radiologists (RCR) Survey on Radiologists’ SPA4 has demonstrated three recurring themes, which are widely recognised to be growing concerns for our specialty.

The unprecedented demand for hospital services during the SARS-CoV-2 (COVID-19) pandemic has dramatically reduced the capability for dealing with non-acute health needs, including cancer care [1, 2]. To alleviate burden on health care systems, including imaging and laboratory services, curtailment of non-COVID-19-related research activity has been necessary [3]. Measures that reduce hospital visits have been adopted to limit risk of infection and death, which is critical in a cancer population whose age and immunocompromised status increases their risk [4]. Imaging, however, requires hospital visits and close contact with staff and equipment; both are sources of disease transmission. Equipment used to image COVID-19 cases may retain virus on its surface for days [5, 6] unless disinfected. The need for social distancing and for disinfecting equipment substantially slows imaging workflow and reduces throughput. This article discusses the specific impact of pandemics such as SARS-CoV-2 on imaging in oncological trials.

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and Resnet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively., Graphical abstract

International audience; State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging.This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now.Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.

Since the inception of the National Institute for Health Research (NIHR) in 2006, the landscape for the delivery of clinical research within the National Health Service (NHS) has been transformed. Clinical radiology has benefitted from funding opportunities for primary imaging research as well as improvements to the supporting research infrastructure to provide imaging for many clinical trials; however, in an increasingly challenging NHS environment, the NIHR and clinical radiology have to evolve an effective working partnership to ensure imaging research is sustainable and will make an impact. A number of initiatives have arisen from discussions between the NIHR, the Royal College of Radiologists (RCR), and stakeholders that will be discussed in this article. It is hoped that these initiatives will be embraced by the imaging community and create a more dynamic sustainable imaging workforce, driving and supporting research and innovation towards future sustainability.

Background: The BIRADS score for mammographic density does not currently indicate which breast has the higher density, and may be at higher risk of developing (or having) cancer. A focal density (‘developing’) is a region that is apparent on a recent mammogram, is present in both CC and MLO views, but can only be seen in one breast. Using focal density (FD) quantification, we assign a density score to each breast separately in order to assess breast asymmetry. Method: Our method, which substantially improves our previous method (RICE) [1], suppresses normal breast parenchyma in order to highlight ROIs (see Figure 1). It embodies the reasonable assumption that the neighborhood typically has a similar tissue density to that of the tissue encompassing the candidate masked tumor. In this preliminary study, the method was applied to 11 patient cases of very dense mammograms, each of which contained at least one cancer. Our method produces a density score for each individual breast and determines asymmetry as a percentage score. Results: Table 1 summaries our results. Except for a single instance (CL0081 – a marginal case), all asymmetric FD scores indicate the laterality of the cancer. All mammograms were pre-processed with Volpara breast density quantification software [2]. We note that in some cases (i.e. CL0037, CL0077, CL0090) where the volumetric density grade percentage VGD% is very similar for both the left and right breast, our method effectively produces distinct FD scores confirming to the laterality of cancer. Conclusion: We assessed asymmetric FD in bilateral mammograms using a method based on the actual composition of the breast. In initial experiments, our method shows a promising potential for quantifying breast asymmetry with respect to the laterality of cancer. It works very well in all BIRADS classes, including BIRADS-D.

Oligometastatic disease represents a clinical and anatomical manifestation between localised and polymetastatic disease. In prostate cancer, as with other cancers, recognition of oligometastatic disease enables focal, metastasis-directed therapies. These therapies potentially shorten or postpone the use of systemic treatment and can delay further metastatic progression, thus increasing overall survival. Metastasis-directed therapies require imaging methods that definitively recognise oligometastatic disease to validate their efficacy and reliably monitor response, particularly so that morbidity associated with inappropriately treating disease subsequently recognised as polymetastatic can be avoided. In this Review, we assess imaging methods used to identify metastatic prostate cancer at first diagnosis, at biochemical recurrence, or at the castration-resistant stage. Standard imaging methods recommended by guidelines have insufficient diagnostic accuracy for reliably diagnosing oligometastatic disease. Modern imaging methods that use PET-CT with tumour-specific radiotracers (choline or prostate-specific membrane antigen ligand), and increasingly whole-body MRI with diffusion-weighted imaging, allow earlier and more precise identification of metastases. The European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group suggests clinical algorithms to integrate modern imaging methods into the care pathway at the various stages of prostate cancer to identify oligometastatic disease. The EORTC proposes clinical trials that use modern imaging methods to evaluate the benefits of metastasis-directed therapies. ispartof: LANCET ONCOLOGY vol:19 issue:10 pages:E534-E545 ispartof: location:England status: published