Clarke Jonassen (planttongue22)

RCTs provide the scientific basis upon which treatment decisions are made. To facilitate critical review, it is important that methods and results are reported transparently. The aim of this study was to explore transparency in surgical RCTs with respect to trial registration, disclosure of funding sources, declarations of investigator conflicts and data-sharing. This was a cross-sectional review of published surgical RCTs. Ten high-impact journals were searched systematically for RCTs published in years 2009, 2012, 2015 and 2018. Four domains of transparency were explored trial registration, disclosure of funding, disclosure of investigator conflicts, and a statement relating to data-sharing. Of 611 RCTs, 475 were eligible for analysis. Some 397 RCTs (83.6 per cent) were registered on a trial database, of which 190 (47·9 per cent) had been registered prospectively. Prospective registration increased over time (26 per cent in 2009, 33·0 per cent in 2012, 54 per cent in 2015, and 72·7 per cent in 2018). Funding disclosure was present in 55·0, 65·0, 69·4 and 75·4 per cent of manuscripts respectively. Conflict of interest disclosure was present in 49·5, 89·1, 94·6 and 98·3 per cent of manuscripts across the same time periods. Data-sharing statements were present in only 15 RCTs (3·2 per cent), 11 of which were published in 2018. Trial registration, disclosure of funding and disclosure of investigator conflicts in surgical RCTs have improved markedly over the past 10 years. Disclosure of data-sharing plans is exceptionally low. This may contribute to research waste and represents a target for improvement. Trial registration, disclosure of funding and disclosure of investigator conflicts in surgical RCTs have improved markedly over the past 10 years. Disclosure of data-sharing plans is exceptionally low. This may contribute to research waste and represents a target for improvement.Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we compared sCTs based on CBCTs and MRs for head and neck (H&N) cancer patients in terms of image quality and proton dose calculation accuracy. A dataset of 27 H&N-patients, treated with proton therapy (PT), containing planning CTs (pCTs), repeat CTs, CBCTs and MRs were used to train two neural networks to convert either CBCTs or MRs into sCTs. Image quality was quantified by calculating mean absolute error (MAE), mean error (ME) and Dice similarity coefficient (DSC) for bones. learn more The dose evaluation consisted of a systematic non-clinical analysis and a clinical recalculation of actually used proton treatment plans. Gamma analysis was performed for non-clinical and clinical treatment plans. For clinical treatment plans also dose to targets and organs at risk (OARs) and normal tissue complication probabilities (NTCP) were compared. CBCT-based sCTs resulted in higher image quality with an average MAE of 40 ± 4 HU and a DSC of 0.95, while for MR-based sCTs a MAE of 65 ± 4 HU and a DSC of 0.89 was observed. Also in clinical proton dose calculations, sCTCBCT achieved higher average gamma pass ratios (2%/2 mm criteria) than sCTMR (96.1% vs. 93.3%). Dose-volume histograms for selected OARs and NTCP-values showed a very small difference between sCTCBCT and sCTMR and a high agreement with the reference pCT. CBCT- and MR-based sCTs have the potential to enable accurate proton dose calculations valuable for daily adaptive PT. Significant image quality differences were observed but did not affect proton dose calculation accuracy in a similar manner. Especially the recalculation of clinical treatment plans showed high agreement with the pCT for both sCTCBCT and sCTMR. The VACTERL association (VACTERL) includes at