Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

A current perspective on stereotactic body radiation therapy for pancreatic cancer.

Pancreatic cancer is a formidable malignancy with poor outcomes. The majority of patients are unable to undergo resection, which remains the only potentially curative treatment option. The management of locally advanced (unresectable) pancreatic cancer is controversial; however, treatment with either chemotherapy or chemoradiation is associated with high rates of local tumor progression and metastases development, resulting in low survival rates. An emerging local modality is stereotactic body radiation therapy (SBRT), which uses image-guided, conformal, high-dose radiation. SBRT has demonstrated promising local control rates and resultant quality of life with acceptable rates of toxicity. Over the past decade, increasing clinical experience and data have supported SBRT as a local treatment modality. Nevertheless, additional research is required to further evaluate the role of SBRT and improve upon the persistently poor outcomes associated with pancreatic cancer. This review discusses the existing clinical experience and technical implementation of SBRT for pancreatic cancer and highlights the directions for ongoing and future studies

Interrater Reliability in Toxicity Identification: Limitations of Current Standards.

PurposeThe National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 is the standard for oncology toxicity encoding and grading, despite limited validation. We assessed interrater reliability (IRR) in multireviewer toxicity identification.Methods and materialsTwo reviewers independently reviewed 100 randomly selected notes for weekly on-treatment visits during radiation therapy from the electronic health record. Discrepancies were adjudicated by a third reviewer for consensus. Term harmonization was performed to account for overlapping symptoms in CTCAE. IRR was assessed based on unweighted and weighted Cohen's kappa coefficients.ResultsBetween reviewers, the unweighted kappa was 0.68 (95% confidence interval, 0.65-0.71) and the weighted kappa was 0.59 (0.22-1.00). IRR was consistent between symptoms noted as present or absent with a kappa of 0.6 (0.66-0.71) and 0.6 (0.65-0.69), respectively.ConclusionsSignificant discordance suggests toxicity identification, particularly retrospectively, is a complex and error-prone task. Strategies to minimize IRR, including training and simplification of the CTCAE criteria, should be considered in trial design and future terminologies

An automated method for comparing motion artifacts in cine four-dimensional computed tomography images.

The aim of this study is to develop an automated method to objectively compare motion artifacts in two four-dimensional computed tomography (4D CT) image sets, and identify the one that would appear to human observers with fewer or smaller artifacts. Our proposed method is based on the difference of the normalized correlation coefficients between edge slices at couch transitions, which we hypothesize may be a suitable metric to identify motion artifacts. We evaluated our method using ten pairs of 4D CT image sets that showed subtle differences in artifacts between images in a pair, which were identifiable by human observers. One set of 4D CT images was sorted using breathing traces in which our clinically implemented 4D CT sorting software miscalculated the respiratory phase, which expectedly led to artifacts in the images. The other set of images consisted of the same images; however, these were sorted using the same breathing traces but with corrected phases. Next we calculated the normalized correlation coefficients between edge slices at all couch transitions for all respiratory phases in both image sets to evaluate for motion artifacts. For nine image set pairs, our method identified the 4D CT sets sorted using the breathing traces with the corrected respiratory phase to result in images with fewer or smaller artifacts, whereas for one image pair, no difference was noted. Two observers independently assessed the accuracy of our method. Both observers identified 9 image sets that were sorted using the breathing traces with corrected respiratory phase as having fewer or smaller artifacts. In summary, using the 4D CT data of ten pairs of 4D CT image sets, we have demonstrated proof of principle that our method is able to replicate the results of two human observers in identifying the image set with fewer or smaller artifacts

Feedback modulation of cholesterol metabolism by the lipid-responsive non-coding RNA LeXis.

Liver X receptors (LXRs) are transcriptional regulators of cellular and systemic cholesterol homeostasis. Under conditions of excess cholesterol, LXR activation induces the expression of several genes involved in cholesterol efflux, facilitates cholesterol esterification by promoting fatty acid synthesis, and inhibits cholesterol uptake by the low-density lipoprotein receptor. The fact that sterol content is maintained in a narrow range in most cell types and in the organism as a whole suggests that extensive crosstalk between regulatory pathways must exist. However, the molecular mechanisms that integrate LXRs with other lipid metabolic pathways are incompletely understood. Here we show that ligand activation of LXRs in mouse liver not only promotes cholesterol efflux, but also simultaneously inhibits cholesterol biosynthesis. We further identify the long non-coding RNA LeXis as a mediator of this effect. Hepatic LeXis expression is robustly induced in response to a Western diet (high in fat and cholesterol) or to pharmacological LXR activation. Raising or lowering LeXis levels in the liver affects the expression of genes involved in cholesterol biosynthesis and alters the cholesterol levels in the liver and plasma. LeXis interacts with and affects the DNA interactions of RALY, a heterogeneous ribonucleoprotein that acts as a transcriptional cofactor for cholesterol biosynthetic genes in the mouse liver. These findings outline a regulatory role for a non-coding RNA in lipid metabolism and advance our understanding of the mechanisms that coordinate sterol homeostasis

Symplectic lattice gauge theories on Grid: approaching the conformal window

Symplectic gauge theories coupled to matter fields lead to symmetry enhancement phenomena that have potential applications in such diverse contexts as composite Higgs, top partial compositeness, strongly interacting dark matter, and dilaton-Higgs models. These theories are also interesting on theoretical grounds, for example in reference to the approach to the large-N limit. A particularly compelling research aim is the determination of the extent of the conformal window in gauge theories with symplectic groups coupled to matter, for different groups and for field content consisting of fermions transforming in different representations. Such determination would have far-reaching implications, but requires overcoming huge technical challenges. Numerical studies based on lattice field theory can provide the quantitative information necessary to this endeavour. We developed new software to implement symplectic groups in the Monte Carlo algorithms within the Grid framework. In this paper, we focus most of our attention on the Sp(4) lattice gauge theory coupled to four (Wilson-Dirac) fermions transforming in the 2-index antisymmetric representation, as a case study. We discuss an extensive catalogue of technical tests of the algorithms and present preliminary measurements to set the stage for future large-scale numerical investigations. We also include the scan of parameter space of all asymptotically free Sp(4) lattice gauge theories coupled to varying number of fermions transforming in the antisymmetric representation.Comment: 41 pages, 16 figure

Asymptomatic Plasmodium vivax infections induce robust IgG responses to multiple blood-stage proteins in a low-transmission region of western Thailand

BACKGROUND: Thailand is aiming to eliminate malaria by the year 2024. Plasmodium vivax has now become the dominant species causing malaria within the country, and a high proportion of infections are asymptomatic. A better understanding of antibody dynamics to P. vivax antigens in a low-transmission setting, where acquired immune responses are poorly characterized, will be pivotal for developing new strategies for elimination, such as improved surveillance methods and vaccines. The objective of this study was to characterize total IgG antibody levels to 11 key P. vivax proteins in a village of western Thailand. METHODS: Plasma samples from 546 volunteers enrolled in a cross-sectional survey conducted in 2012 in Kanchanaburi Province were utilized. Total IgG levels to 11 different proteins known or predicted to be involved in reticulocyte binding or invasion (ARP, GAMA, P41, P12, PVX_081550, and five members of the PvRBP family), as well as the leading pre-erythrocytic vaccine candidate (CSP) were measured using a multiplexed bead-based assay. Associations between IgG levels and infection status, age, and spatial location were explored. RESULTS: Individuals from a low-transmission region of western Thailand reacted to all 11 P. vivax recombinant proteins. Significantly greater IgG levels were observed in the presence of a current P. vivax infection, despite all infected individuals being asymptomatic. IgG levels were also higher in adults (18 years and older) than in children. For most of the proteins, higher IgG levels were observed in individuals living closer to the Myanmar border and further away from local health services. CONCLUSIONS: Robust IgG responses were observed to most proteins and IgG levels correlated with surrogates of exposure, suggesting these antigens may serve as potential biomarkers of exposure, immunity, or both

AltitudeOmics: Red Blood Cell metabolic adaptation to high altitude hypoxia

Red blood cells (RBCs) are key players in systemic oxygen transport. RBCs respond to in vitro hypoxia  through  the so-called  oxygen-dependent  metabolic  regulation,  which  involves  the competitive  binding  of  deoxyhemoglobin  and  glycolytic  enzymes  to  the  N-terminal  cytosolic domain  of  band  3.  This  mechanism  promotes  the  accumulation  of  2,3-DPG,  stabilizing  the deoxygenated state of hemoglobin, and cytosol acidification, triggering oxygen off-loading through the  Bohr  effect.  Despite  in  vitro  studies,  in  vivo adaptations  to  hypoxia  have  not  yet  been completely elucidated. Within  the  framework  of  the AltitudeOmics  study,  erythrocytes  were  collected  from  21 healthy volunteers at sea level, after exposure to high altitude (5260m) for 1, 7 and 16days, and following  reascent  after  7days  at 1525m.  UHPLC-MS  metabolomics  results  were  correlated  to physiological and athletic performance parameters. Immediate  metabolic  adaptations  were  noted as early as a few hours from ascending  to >5000m, and maintained for 16 days at high altitude.  Consistent with the mechanisms elucidated in vitro, hypoxia promoted glycolysis and deregulated the pentose phosphate pathway, as well purine catabolism, glutathione homeostasis, arginine/nitric oxide and sulphur/H2S metabolism. Metabolic adaptations were preserved one week after descent, consistently with improved physical performances in comparison to the first ascendance, suggesting a mechanism of metabolic memory