Impact of 18F-Choline PET scan acquisition time on delineation of GTV in prostate cancer [Poster Abstract]

Background: Dose painting radiotherapy requires accurate outlining of primary tumour volumes in the prostate. T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) is the best imaging method for defining the gross tumour volume (GTV). Choline positron emission tomography (PET) is currently a controversial tracer. The image acquisition differs significantly in published studies. Many used early static imaging. One study found that 18F-choline PET/CT with late image acquisition has superior accuracy to T2W MR and functional MR alone1. We investigate whether increasing 18F-Choline PET scan acquisition time from 60 (PET-60) to 90 (PET-90) minutes improves GTV TVD. Methods. Analysis was performed on 9 18F-Choline PET scans. Patients were injected with 370MBq of activity. Three clinicians (C1, C2 and C3) independently and without reference to each other contoured GTVs on each of the T2W-MRI, PET-60 and PET-90 scans at differing times. Scans were registered by a clinician using rigid co-registration. The treating clinicians MRI contour was used as a reference contour. The resulting PET and MRI GTVs were transferred to the PET-60 and PET-90 scans after image registration. The Dice Similarity Coefficient (DSC), Specificity (Sp) and Sensitivity (S) were calculated from contour mask voxel analysis. Results. Table 1 shows the mean and range DSC, S and Sp scores on MRI, PET-60 and PET-90 for C1, C2 and C3 in comparison to the treating clinicians contour on MRI (C1). A 2 sampled T-test (P < 0.01) showed, no significant difference in the Sp, S and DSC between GTVs on PET-60 and PET-90 scans. Further to this, as shown in Figure 1, variability in GTV delineation is significant between observers in a singular case as well as across imaging modalities. Conclusion. Compared to MRI delineated GTVs, 18F-Choline PET GTVs are significantly different. This study found however that increasing the PET scan acquisition time from 60 to 90 minutes did not improve the performance of GTV TVD in comparison to MRI delineated GTV

Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy

Biological tumour volume (GTVPET) delineation on 18F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTVPET. Automatic segmentation algorithms applied to 18F-FDG PET (PET-AS) imaging have been used for GTVPET delineation on 18F-FDG PET imaging acquired before ICT. However, their role has not been investigated in 18F-FDG PET imaging acquired after ICT. In this study we investigate PET-AS techniques, including ATLAAS a machine learned method, for accurate delineation of the GTVPET after ICT. Twenty patients were enrolled onto a prospective phase I study (FiGaRO). PET/CT imaging was acquired at baseline and 3 weeks following 1 cycle of induction chemotherapy. The GTVPET was manually delineated by a nuclear medicine physician and clinical oncologist. The resulting GTVPET was used as the reference contour. The ATLAAS original statistical model was expanded to include images of reduced metabolic activity and the ATLAAS algorithm was re-trained on the new reference dataset. Estimated GTVPET contours were derived using sixteen PET-AS methods and compared to the GTVPET using the Dice Similarity Coefficient (DSC). The mean DSC for ATLAAS, 60% Peak Thresholding (PT60), Adaptive Thresholding (AT) and Watershed Thresholding (WT) was 0.72, 0.61, 0.63 and 0.60 respectively. The GTVPET generated by ATLAAS compared favourably with manually delineated volumes and in comparison, to other PET-AS methods, was more accurate for GTVPET delineation after ICT. ATLAAS would be a feasible method to reduce inter-observer variability in multi-centre trials

Ubiquitination screen using protein microarrays for comprehensive identification of Rsp5 substrates in yeast

Ubiquitin-protein ligases (E3s) are responsible for target recognition and regulate stability, localization or function of their substrates. However, the substrates of most E3 enzymes remain unknown. Here, we describe the development of a novel proteomic in vitro ubiquitination screen using a protein microarray platform that can be utilized for the discovery of substrates for E3 ligases on a global scale. Using the yeast E3 Rsp5 as a test system to identify its substrates on a yeast protein microarray that covers most of the yeast (Saccharomyces cerevisiae) proteome, we identified numerous known and novel ubiquitinated substrates of this E3 ligase. Our enzymatic approach was complemented by a parallel protein microarray protein interaction study. Examination of the substrates identified in the analysis combined with phage display screening allowed exploration of binding mechanisms and substrate specificity of Rsp5. The development of a platform for global discovery of E3 substrates is invaluable for understanding the cellular pathways in which they participate, and could be utilized for the identification of drug targets

Systematic evidence on migrating and extractable food contact chemicals: Most chemicals detected in food contact materials are not listed for use

Food packaging is important for today’s globalized food system, but food contact materials (FCMs) can also be a source of hazardous chemicals migrating into foodstuffs. Assessing the impacts of FCMs on human health requires a comprehensive identification of the chemicals they contain, the food contact chemicals (FCCs). We systematically compiled the “database on migrating and extractable food contact chemicals” (FCCmigex) using information from 1210 studies. We found that to date 2881 FCCs have been detected, in a total of six FCM groups (Plastics, Paper & Board, Metal, Multi-materials, Glass & Ceramic, and Other FCMs). 65% of these detected FCCs were previously not known to be used in FCMs. Conversely, of the more than 12’000 FCCs known to be used, only 1013 are included in the FCCmigex database. Plastic is the most studied FCM with 1975 FCCs detected. Our findings expand the universe of known FCCs to 14,153 chemicals. This knowledge contributes to developing non-hazardous FCMs that lead to safer food and support a circular economy

2dFLenS and KiDS: determining source redshift distributions with cross-correlations

We develop a statistical estimator to infer the redshift probability distribution of a photometric sample of galaxies from its angular cross-correlation in redshift bins with an overlapping spectroscopic sample. This estimator is a minimum-variance weighted quadratic function of the data: a quadratic estimator. This extends and modifies the methodology presented by McQuinn & White. The derived source redshift distribution is degenerate with the source galaxy bias, which must be constrained via additional assumptions. We apply this estimator to constrain source galaxy redshift distributions in the Kilo-Degree imaging survey through cross-correlation with the spectroscopic 2-degree Field Lensing Survey, presenting results first as a binned step-wise distribution in the range z < 0.8, and then building a continuous distribution using a Gaussian process model. We demonstrate the robustness of our methodology using mock catalogues constructed from N-body simulations, and comparisons with other techniques for inferring the redshift distribution

Erratum: Author Correction: Identification of genes required for eye development by high-throughput screening of mouse knockouts.

[This corrects the article DOI: 10.1038/s42003-018-0226-0.]