The Influence of Fat Suppression Technique on Diffusion-weighted (DW) MRI in Lung Cancer

Purpose: To qualitatively and quantitatively investigate the effect of common vendor-related sequence variations in fat suppression techniques on the diagnostic performance of free-breathing DW protocols for lung imaging.Methods: 8 patients with malignant lung lesions were scanned in free breathing using two diffusion-weighted (DW) protocols with different fat suppression techniques: DWA used short-tau inversion recovery (STIR), and DWB used Spectral Adiabatic Inversion Recovery (SPAIR). Both techniques were obtained at two time points, between 1 hour and 1 week apart. Image quality was assessed using a 5-point scoring system. The number of lesions visible within lung, mediastinum and at thoracic inlet on the DW (b=800 s/mm2) images was compared. Signal-to-noise ratios (SNR) were calculated for lesions and para-spinal muscle. Repeatability of ADC values of the lesions was estimated for both protocols together and separately.Results: There was a signal void at the thoracic inlet in all patients with DWB but not with DWA. DWA images were rated significantly better than DWB images overall quality domains. (Cohens κ = 1). Although 8 more upper mediastinal/thoracic inlet lymph nodes were detected with DWA than DWB, this did not reach statistical significance (p = 0.23). Tumour ADC values were not significantly different between protocols (p=0.93), their ADC reproducibility was satisfactory (CoV=7.7%) and repeatability of each protocol separately was comparable (CoVDWA=3.7% (95% CI 2.5 7.1%) and CoVDWB=4.6% (95% CI 3.18.8%)).Conclusion: In a free-breathing DW-MRI protocol for lung, STIR fat suppression produced images of better diagnostic quality than SPAIR, while maintaining comparable SNR and providing repeatable quantitative ADC acceptable for use in a multicentre trial setting

Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study

BACKGROUND: The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability. METHODS: Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds. RESULTS: Classification performance was significant (p  0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROC > 0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage. CONCLUSIONS: Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance. TRIAL REGISTRATION: NCT03226886 (TRACERx Renal

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains

Giant magnons of string theory in the lambda background

The analogues of giant magnon configurations are studied on the string world sheet in the lambda background. This is a discrete deformation of the AdS(5)xS(5) background that preserves the integrability of the world sheet theory. Giant magnon solutions are generated using the dressing method and their dispersion relation is found. This reduces to the usual dyonic giant magnon dispersion relation in the appropriate limit and becomes relativistic in another limit where the lambda model becomes the generalized sine-Gordon theory of the Pohlmeyer reduction. The scattering of giant magnons is then shown in the semi-classical limit to be described by the quantum S-matrix that is a quantum group deformation of the conventional giant magnon S-matrix. It is further shown that in the small g limit, a sector of the S-matrix is related to the XXZ spin chain whose spectrum matches the spectrum of magnon bound states.Comment: 53 pages, 6 figures, final version to appear in JHE

The dynamics of inbreeding depression in the butterfly Bicyclus anynana (Butler)

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Development of training-related health care software by a team of clinical educators: their experience, from conception to piloting

Derfel ap Dafydd,1 Ruth Williamson,2 Philip Blunt,3 Dominic M Blunt4 1Department of Radiology, Royal Marsden NHS Foundation Trust, London, 2Imaging Department, Royal Bornemouth Hospital, Bornemouth, 3Savernake IT Ltd, Marlborough, 4Imaging Department, Imperial College Healthcare NHS Trust, London, UK Abstract: The difficulties of producing useful, bespoke, and affordable information technology systems for large health care organizations are well publicized, following several high-profile endeavors in the UK. This article describes the experience of a small group of clinical radiologists and their collaborators in producing an information technology system – from conception to piloting. This system, called Trainee Tracker, enables automated target date recalculation of trainee milestones, depending on their work patterns and other individual circumstances. It utilizes an automated email alert system to notify the educational supervisors and trainees of approaching and elapsed target dates, in order to identify trainees in difficulty early and address their training needs accordingly. The challenges and advantages, both common to and contrasting with larger-scale projects, are also considered. The benefits of the development team’s “agile” approach to software development and the lessons learned will be of interest to medical educators, particularly those with expertise in e-portfolios and other training-related software. Keywords: training, appraisal, ARCP, Annual Review of Clinical Progression, portfolio, traine

Detection and staging of radio-recurrent prostate cancer using multiparametric MRI.

OBJECTIVE: We determined the sensitivity and specificity of multiparametric magnetic resonance imaging (MP-MRI) in detection of locally recurrent prostate cancer and extra prostatic extension in the post-radical radiotherapy setting. Histopathological reference standard was whole-mount prostatectomy specimens. We also assessed for any added value of the dynamic contrast enhancement (DCE) sequence in detection and staging of local recurrence. METHODS: This was a single centre retrospective study. Participants were selected from a database of males treated with salvage prostatectomy for locally recurrent prostate cancer following radiotherapy. All underwent pre-operative prostate-specific antigen assay, positron emission tomography CT, MP-MRI and transperineal template prostate mapping biopsy prior to salvage prostatectomy. MP-MRI performance was assessed using both Prostate Imaging-Reporting and Data System v. 2 and a modified scoring system for the post-treatment setting. RESULTS: 24 patients were enrolled. Using Prostate Imaging-Reporting and Data System v. 2, sensitivity, specificity, positive predictive value and negative predictive value was 64%, 94%, 98% and 36%. MP-MRI under staged recurrent cancer in 63%. A modified scoring system in which DCE was used as a co-dominant sequence resulted in improved diagnostic sensitivity (61%-76%) following subgroup analysis. CONCLUSION: Our results show MP-MRI has moderate sensitivity (64%) and high specificity (94%) in detecting radio-recurrent intraprostatic disease, though disease tends to be under quantified and under staged. Greater emphasis on dynamic contrast images in overall scoring can improve diagnostic sensitivity. ADVANCES IN KNOWLEDGE: MP-MRI tends to under quantify and under stage radio-recurrent prostate cancer. DCE has a potentially augmented role in detecting recurrent tumour compared with the de novo setting. This has relevance in the event of any future modified MP-MRI scoring system for the irradiated gland