Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Did RNA editing in plant organellar genomes originate under natural selection or through genetic drift?

<p>Abstract</p> <p>Background</p> <p>The C↔U substitution types of RNA editing have been observed frequently in organellar genomes of land plants. Although various attempts have been made to explain why such a seemingly inefficient genetic mechanism would have evolved, no satisfactory explanation exists in our view. In this study, we examined editing patterns in chloroplast genomes of the hornwort <it>Anthoceros formosae </it>and the fern <it>Adiantum capillus-veneris </it>and in mitochondrial genomes of the angiosperms <it>Arabidopsis thaliana</it>, <it>Beta vulgaris </it>and <it>Oryza sativa</it>, to gain an understanding of the question of how RNA editing originated.</p> <p>Results</p> <p>We found that 1) most editing sites were distributed at the 2^ndand 1^stcodon positions, 2) editing affected codons that resulted in larger hydrophobicity and molecular size changes much more frequently than those with little change involved, 3) editing uniformly increased protein hydrophobicity, 4) editing occurred more frequently in ancestrally T-rich sequences, which were more abundant in genes encoding membrane-bound proteins with many hydrophobic amino acids than in genes encoding soluble proteins, and 5) editing occurred most often in genes found to be under strong selective constraint.</p> <p>Conclusion</p> <p>These analyses show that editing mostly affects functionally important and evolutionarily conserved codon positions, codons and genes encoding membrane-bound proteins. In particular, abundance of RNA editing in plant organellar genomes may be associated with disproportionately large percentages of genes in these two genomes that encode membrane-bound proteins, which are rich in hydrophobic amino acids and selectively constrained. These data support a hypothesis that natural selection imposed by protein functional constraints has contributed to selective fixation of certain editing sites and maintenance of the editing activity in plant organelles over a period of more than four hundred millions years. The retention of genes encoding RNA editing activity may be driven by forces that shape nucleotide composition equilibrium in two organellar genomes of these plants. Nevertheless, the causes of lineage-specific occurrence of a large portion of RNA editing sites remain to be determined.</p> <p>Reviewers</p> <p>This article was reviewed by Michael Gray (nominated by Laurence Hurst), Kirsten Krause (nominated by Martin Lercher), and Jeffery Mower (nominated by David Ardell).</p

Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks

Physics-informed Neural Networks (PINNs) have recently gained popularity due to their effective approximation of partial differential equations (PDEs) using deep neural networks (DNNs). However, their out of domain behavior is not well understood, with previous work speculating that the presence of high frequency components in the solution function might be to blame for poor extrapolation performance. In this paper, we study the extrapolation behavior of PINNs on a representative set of PDEs of different types, including high-dimensional PDEs. We find that failure to extrapolate is not caused by high frequencies in the solution function, but rather by shifts in the support of the Fourier spectrum over time. We term these spectral shifts and quantify them by introducing a Weighted Wasserstein-Fourier distance (WWF). We show that the WWF can be used to predict PINN extrapolation performance, and that in the absence of significant spectral shifts, PINN predictions stay close to the true solution even in extrapolation. Finally, we propose a transfer learning-based strategy to mitigate the effects of larger spectral shifts, which decreases extrapolation errors by up to 82%

Design of a low profile array transducer in d15 mode for high angled shear wave generation

Shear wave inspection is generated from mode conversion of longitudinal waves, using a selected, angled wedge positioned between the transducer and the test specimen. However, in certain scenarios where access is restricted the combination of the transducer and the wedge can be too cumbersome for in situ deployment. In this work, a low profile, linear ultrasound array transducer is proposed to generate shear waves via direct coupling to the component surface precluding the requirement for a wedge. The array transducer was designed using finite element modelling, and a prototyped array was manufactured with 32 elements and operating frequency at ~2MHz. Preliminary imaging results has shown the shear wave beam generated can be operated at high angles up to 80 degrees

Spatial Heterogeneity of Soil and Vegetation Characteristics and Soil-Vegetation Relationships along an Ecotone in Southern Mu Us Sandy Land, China

Spatial pattern analysis is an essential component of spatial heterogeneity studies on soil properties and vegetation characteristics. It was conducted in several studies for both soil and vegetation characteristics (Strand et al., 2007; Dick and Gilliam, 2007; Zuo et al., 2010). This study aims to examine the changes in the spatial heterogeneity of soil properties at different soil layers, the spatial heterogeneity of soil and vegetation characteristics along an ecotone, and soil-vegetation relationships along the ecotone in a critical area of desertification

Converting Treatment Plans From Helical Tomotherapy to L-Shape Linac: Clinical Workflow and Dosimetric Evaluation.

This work evaluated a commercial fallback planning workflow designed to provide cross-platform treatment planning and delivery. A total of 27 helical tomotherapy intensity-modulated radiotherapy plans covering 4 anatomical sites were selected, including 7 brain, 5 unilateral head and neck, 5 bilateral head and neck, 5 pelvis, and 5 prostate cases. All helical tomotherapy plans were converted to 7-field/9-field intensity-modulated radiotherapy and volumetric-modulated radiotherapy plans through fallback dose-mimicking algorithm using a 6-MV beam model. The planning target volume (PTV) coverage ( D1, D99, and homogeneity index) and organs at risk dose constraints were evaluated and compared. Overall, all 3 techniques resulted in relatively inferior target dose coverage compared to helical tomotherapy plans, with higher homogeneity index and maximum dose. The organs at risk dose ratio of fallback to helical tomotherapy plans covered a wide spectrum, from 0.87 to 1.11 on average for all sites, with fallback plans being superior for brain, pelvis, and prostate sites. The quality of fallback plans depends on the delivery technique, field numbers, and angles, as well as user selection of structures for organs at risk. In actual clinical scenario, fallback plans would typically be needed for 1 to 5 fractions of a treatment course in the event of machine breakdown. Our results suggested that &lt;1% dose variance can be introduced in target coverage and/or organs at risk from fallback plans. The presented clinical workflow showed that the fallback plan generation typically takes 10 to 20 minutes per case. Fallback planning provides an expeditious and effective strategy for transferring patients cross platforms, and minimizing the untold risk of a patient missing treatment(s)