Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era

Synthetic PET From CT Improves Diagnosis and Prognosis for Lung Cancer: Proof of Concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT

Diversity in CO2 concentrating mechanisms among chemolithoautotrophs from genera Hydrogenovibrio, Thiomicrorhabdus, and Thiomicrospira, ubiquitous in sulfidic habitats worldwide

Members of Hydrogenovibrio, Thiomicrospira and Thiomicrorhabdus fix carbon at hydrothermal vents, coastal sediments, hypersaline lakes, and other sulfidic habitats. The genome sequences of these ubiquitous and prolific chemolithoautotrophs suggest a surprising diversity of mechanisms for dissolved inorganic carbon (DIC) uptake and fixation; these mechanisms are verified here. Carboxysomes are apparent in transmission electron micrographs of most of these organisms; lack of carboxysomes in Thiomicrorhabdus sp. Milos T2 and Tmr. arctica, and an inability to grow under low DIC conditions by Thiomicrorhabdus sp. Milos T2 are consistent with an absence of carboxysome loci in their genomes. For the remaining organisms, potential DIC transporters from four evolutionarily distinct families (Tcr0853/0854, Chr, SbtA, SulP) are located downstream of carboxysome loci. Transporter genes collocated with carboxysome loci, as well as some homologs located elsewhere on the chromosomes, had elevated transcript levels under low DIC conditions, as assayed by qRT-PCR. DIC uptake was measureable via silicone oil centrifugation when a representative of each of the four types of transporter was expressed in Escherichia coli. Expression of these genes in carbonic anhydrase-deficient E. coli EDCM636 enabled it to grow under low DIC conditions, consistent with DIC transport by these proteins. The results from this study expand the range of DIC transporters within the SbtA and SulP transporter families, verify DIC uptake by transporters encoded by Tcr_0853 and Tcr_0854 and their homologs, and introduce DIC as a potential substrate for transporters from the Chr family. IMPORTANCE Autotrophic organisms take up and fix DIC, introducing carbon into the biological component of the global carbon cycle. The mechanisms for DIC uptake and fixation by autotrophic Bacteria and Archaea are likely to be diverse, but have only been well-characterized among "Cyanobacteria". Based on genome sequences, members of Hydrogenovibrio, Thiomicrospira and Thiomicrorhabdus have a variety of mechanisms for DIC uptake and fixation. We verified that most of these organisms are capable of growing under low DIC conditions, when they upregulate carboxysome loci and transporter genes collocated with these loci on their chromosomes. When these genes, which fall into four evolutionarily independent families of transporters, are expressed in E. coli, DIC transport is detected. This expansion in known DIC transporters across four families, from organisms from a variety of environments, provides insight into the ecophysiology of autotrophs, as well as a toolkit for engineering microorganisms for carbon-neutral biochemistries of industrial importance

Predicting Benefit From Immune Checkpoint Inhibitors in Patients With Non-Small-Cell Lung Cancer by CT-Based Ensemble Deep Learning: A Retrospective Study

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer

Infection Prevention and Control Guideline for Cystic Fibrosis: 2013 Update

The 2013 Infection Prevention and Control (IP&C) Guideline for Cystic Fibrosis (CF) was commissioned by the CF Foundation as an update of the 2003 Infection Control Guideline for CF. During the past decade, new knowledge and new challenges provided the following rationale to develop updated IP&C strategies for this unique population: 1. The need to integrate relevant recommendations from evidence-based guidelines published since 2003 into IP&C practices for CF . These included guidelines from the Centers for Disease Control and Prevention (CDC)/Healthcare Infection Control Practices Advisory Committee (HICPAC), the World Health Organization (WHO), and key professional societies, including the Infectious Diseases Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA). During the past decade, new evidence has led to a renewed emphasis on source containment of potential pathogens and the role played by the contaminated healthcare environment in the transmission of infectious agents. Furthermore, an increased understanding of the importance of the application of implementation science, monitoring adherence, and feedback principles has been shown to increase the effectiveness of IP&C guideline recommendations. 2. Experience with emerging pathogens in the non-CF population has expanded our understanding of droplet transmission of respiratory pathogens and can inform IP&C strategies for CF . These pathogens include severe acute respiratory syndrome coronavirus and the 2009 influenza A H1N1. Lessons learned about preventing transmission of methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant gram-negative pathogens in non-CF patient populations also can inform IP&C strategies for CF

Mosaic Origins of a Complex Chimeric Mitochondrial Gene in Silene vulgaris

Chimeric genes are significant sources of evolutionary innovation that are normally created when portions of two or more protein coding regions fuse to form a new open reading frame. In plant mitochondria astonishingly high numbers of different novel chimeric genes have been reported, where they are generated through processes of rearrangement and recombination. Nonetheless, because most studies do not find or report nucleotide variation within the same chimeric gene, evolution after the origination of these chimeric genes remains unstudied. Here we identify two alleles of a complex chimera in Silene vulgaris that are divergent in nucleotide sequence, genomic position relative to other mitochondrial genes, and expression patterns. Structural patterns suggest a history partially influenced by gene conversion between the chimeric gene and functional copies of subunit 1 of the mitochondrial ATP synthase gene (atp1). We identified small repeat structures within the chimeras that are likely recombination sites allowing generation of the chimera. These results establish the potential for chimeric gene divergence in different plant mitochondrial lineages within the same species. This result contrasts with the absence of diversity within mitochondrial chimeras found in crop species

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys

Purpose Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition. Methods A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers. Results The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58 ± 3.05 using the target prediction accuracy of ± 1 mm. Conclusion Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient

Prospective analysis of in vivo landmark point-based MRI geometric distortion in head and neck cancer patients scanned in immobilized radiation treatment position: Results of a prospective quality assurance protocol

Purpose: Uncertainties related to geometric distortion are a major obstacle for effectively utilizing MRI in radiation oncology. We aim to quantify the geometric distortion in patient images by comparing their in-treatment position MRIs with the corresponding planning CTs, using CT as the non-distorted gold standard. Methods: Twenty-one head and neck cancer patients were imaged with MRI as part of a prospective Institutional Review Board approved study. MR images were acquired with a T2 SE sequence (0.5 Ã 0.5 Ã 2.5 mm voxel size) in the same immobilization position as in the CTs. MRI to CT rigid registration was then done and geometric distortion comparison was assessed by measuring the corresponding anatomical landmarks on both the MRI and the CT images. Several landmark measurements were obtained including; skin to skin (STS), bone to bone, and soft tissue to soft tissue at specific levels in horizontal and vertical planes of both scans. Inter-observer variability was assessed and interclass correlation (ICC) was calculated. Results: A total of 430 landmark measurements were obtained. The median distortion for all landmarks in all scans was 1.06 mm (IQR 0.6â1.98). For each patient 48% of the measurements were done in the right-left direction and 52% were done in the anteroposterior direction. The measured geometric distortion was not statistically different in the right-left direction compared to the anteroposterior direction (1.5 ± 1.6 vs. 1.6 ± 1.7 mm, respectively, p = 0.4). The magnitude of distortion was higher in the STS peripheral landmarks compared to the more central landmarks (2.0 ± 1.9 vs. 1.2 ± 1.3 mm, p < 0.0001). The mean distortion measured by observer one was not significantly different compared to observer 2, 3, and 4 (1.05, 1.23, 1.06 and 1.05 mm, respectively, p = 0.4) with ICC = 0.84. Conclusion: MRI geometric distortions were quantified in radiotherapy planning applications with a clinically insignificant error of less than 2 mm compared to the gold standard CT. Keywords: MRI, CT, Geometric Distortion, Head and Neck Cancer, Radiation Treatment, Quality Assuranc