Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory

This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest (ROI) proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross-validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy

Full-dose PET Synthesis from Low-dose PET Using High-efficiency Diffusion Denoising Probabilistic Model

To reduce the risks associated with ionizing radiation, a reduction of radiation exposure in PET imaging is needed. However, this leads to a detrimental effect on image contrast and quantification. High-quality PET images synthesized from low-dose data offer a solution to reduce radiation exposure. We introduce a diffusion-model-based approach for estimating full-dose PET images from low-dose ones: the PET Consistency Model (PET-CM) yielding synthetic quality comparable to state-of-the-art diffusion-based synthesis models, but with greater efficiency. There are two steps: a forward process that adds Gaussian noise to a full dose PET image at multiple timesteps, and a reverse diffusion process that employs a PET Shifted-window Vision Transformer (PET-VIT) network to learn the denoising procedure conditioned on the corresponding low-dose PETs. In PET-CM, the reverse process learns a consistency function for direct denoising of Gaussian noise to a clean full-dose PET. We evaluated the PET-CM in generating full-dose images using only 1/8 and 1/4 of the standard PET dose. Comparing 1/8 dose to full-dose images, PET-CM demonstrated impressive performance with normalized mean absolute error (NMAE) of 1.233+/-0.131%, peak signal-to-noise ratio (PSNR) of 33.915+/-0.933dB, structural similarity index (SSIM) of 0.964+/-0.009, and normalized cross-correlation (NCC) of 0.968+/-0.011, with an average generation time of 62 seconds per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12x faster. In the 1/4 dose to full-dose image experiments, PET-CM is also competitive, achieving an NMAE 1.058+/-0.092%, PSNR of 35.548+/-0.805dB, SSIM of 0.978+/-0.005, and NCC 0.981+/-0.007 The results indicate promising low-dose PET image quality improvements for clinical applications

Weight management interventions in adults with intellectual disabilities and obesity: a systematic review of the evidence

o evaluate the clinical effectiveness of weight management interventions in adults with intellectual disabilities (ID) and obesity using recommendations from current clinical guidelines for the first line management of obesity in adults. Full papers on lifestyle modification interventions published between 1982 to 2011 were sought by searching the Medline, Embase, PsycINFO and CINAHL databases. Studies were evaluated based on 1) intervention components, 2) methodology, 3) attrition rate 4) reported weight loss and 5) duration of follow up. Twenty two studies met the inclusion criteria. The interventions were classified according to inclusion of the following components: behaviour change alone, behaviour change plus physical activity, dietary advice or physical activity alone, dietary plus physical activity advice and multi-component (all three components). The majority of the studies had the same methodological limitations: no sample size justification, small heterogeneous samples, no information on randomisation methodologies. Eight studies were classified as multi-component interventions, of which one study used a 600 kilocalorie (2510 kilojoule) daily energy deficit diet. Study durations were mostly below the duration recommended in clinical guidelines and varied widely. No study included an exercise program promoting 225–300 minutes or more of moderate intensity physical activity per week but the majority of the studies used the same behaviour change techniques. Three studies reported clinically significant weight loss (≥ 5%) at six months post intervention. Current data indicate weight management interventions in those with ID differ from recommended practice and further studies to examine the effectiveness of multi-component weight management interventions for adults with ID and obesity are justified

Radiation, Immune Checkpoint Blockade and the Abscopal Effect: A Critical Review on Timing, Dose and Fractionation

The combination of radiation and immunotherapy is currently an exciting avenue of pre-clinical and clinical investigation. The synergy between these two treatment modalities has the potential to expand the role of radiation from a purely local therapy, to a role in advanced and metastatic disease. Tumor regression outside of the irradiated field, known as the abscopal effect, is a recognized phenomenon mediated by lymphocytes and enhanced by checkpoint blockade. In this review, we summarize the known mechanistic data behind the immunostimulatory effects of radiation and how this is enhanced by immunotherapy. We also provide pre-clinical data supporting specific radiation timing and optimal dose/fractionation for induction of a robust anti-tumor immune response with or without checkpoint blockade. Importantly, these data are placed in a larger context of understanding T-cell exhaustion and the impact of immunotherapy on this phenotype. We also include relevant pre-clinical studies done in non-tumor systems. We discuss the published clinical trials and briefly summarize salient case reports evaluating the abscopal effect. Much of the data discussed here remains at the preliminary stage, and a number of interesting avenues of research remain under investigation

Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities

Firm-hosted commercial online communities, in which customers interact to solve each other's service problems, represent a fascinating context to study the motivations of collective action in the form of knowledge contribution to the community. We extend a model of social capital based on Wasko and Faraj (2005) to incorporate and contrast the direct impact of commitment to both the online community and the host firm, as well as reciprocity, on quality and quantity of knowledge contribution. In addition, we examine the moderating influence of three individual attributes that are particularly relevant to the firm-hosted community context: perceived informational value, sportsmanship, and online interaction propensity. We empirically test our framework using self-reported and objective data from 203 members of a firm-hosted technical support community. In addition to several interesting moderating effects, we find that a customer's online interaction propensity, commitment to the community, and the informational value s/he perceives in the community are the strongest drivers of knowledge contribution

The Evolution of Combinatorial Gene Regulation in Fungi

It is widely suspected that gene regulatory networks are highly plastic. The rapid turnover of transcription factor binding sites has been predicted on theoretical grounds and has been experimentally demonstrated in closely related species. We combined experimental approaches with comparative genomics to focus on the role of combinatorial control in the evolution of a large transcriptional circuit in the fungal lineage. Our study centers on Mcm1, a transcriptional regulator that, in combination with five cofactors, binds roughly 4% of the genes in Saccharomyces cerevisiae and regulates processes ranging from the cell-cycle to mating. In Kluyveromyces lactis and Candida albicans, two other hemiascomycetes, we find that the Mcm1 combinatorial circuits are substantially different. This massive rewiring of the Mcm1 circuitry has involved both substantial gain and loss of targets in ancient combinatorial circuits as well as the formation of new combinatorial interactions. We have dissected the gains and losses on the global level into subsets of functionally and temporally related changes. One particularly dramatic change is the acquisition of Mcm1 binding sites in close proximity to Rap1 binding sites at 70 ribosomal protein genes in the K. lactis lineage. Another intriguing and very recent gain occurs in the C. albicans lineage, where Mcm1 is found to bind in combination with the regulator Wor1 at many genes that function in processes associated with adaptation to the human host, including the white-opaque epigenetic switch. The large turnover of Mcm1 binding sites and the evolution of new Mcm1–cofactor interactions illuminate in sharp detail the rapid evolution of combinatorial transcription networks

Deciphering the origin and evolution of Hepatitis B viruses by means of a family of non-enveloped fish viruses

Hepatitis B viruses (HBVs), which are enveloped viruses with reverse-transcribed DNA genomes, constitute the family Hepadnaviridae. An outstanding feature of HBVs is their streamlined genome organization with extensive gene overlap. Remarkably, the ∼1,100 bp open reading frame (ORF) encoding the envelope proteins is fully nested within the ORF of the viral replicase P. Here, we report the discovery of a diversified family of fish viruses, designated nackednaviruses, which lack the envelope protein gene, but otherwise exhibit key characteristics of HBVs including genome replication via protein-primed reverse-transcription and utilization of structurally related capsids. Phylogenetic reconstruction indicates that these two virus families separated more than 400 million years ago before the rise of tetrapods. We show that HBVs are of ancient origin, descending from non-enveloped progenitors in fishes. Their envelope protein gene emerged de novo, leading to a major transition in viral lifestyle, followed by co-evolution with their hosts over geologic eras

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe