The present meta-analysis supports the efficacy of a low FODMAP diet in the treatment of functional gastrointestinal symptoms. Further research should ensure studies include dietary adherence, and more studies looking at greater number of patients and long-term adherence to a low FODMAP diet need to be conducted.

Background: PET scans using FDG and somatostatin receptor imaging agents have both been used to study neuroendocrine tumours. Most reports have documented the sensitivity and specificity of each radiopharmaceutical independently, and even suggested the superiority of one over the other for different grades of disease.Aim: The aim of this work was to develop a grading scheme that describes the joint results of both the FDG and somatostatin receptor imaging PET scans in staging subjects with neuroendocrine tumours in a single combined parameter. The grading scheme that has been developed is referred to as the NETPET grade.Methods: This is a retrospective study which assessed subjects who had both FDG and somatostatin receptor PET imaging at our institution within 31 days of each other. The NETPET grade was assigned by experienced nuclear medicine physicians and compared with other clinical data such as WHO grade and overall survival.Results: In the period 2011-2015 we were able to recruit 62 subjects with histologically proven metastatic neuroendocrine tumour for review. The NETPET grade incorporating both the FDG and somatostatin receptor imaging results was significantly correlated with overall survival by univariate analysis (p=0.0018), whereas in this cohort the WHO grade at the time of diagnosis did not correlate with survival.Conclusions: The NETPET grade has promise as a prognostic imaging biomarker in neuroendocrine tumours. It permits the capturing of the complexity of dual radiotracer imaging in a single parameter which describes the subjects' disease and is readily amenable to use in patient management and further research.

Purpose CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. Methods The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA‐SPECT, and 4 sheep imaged with Xenon‐CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B‐splines, Free‐form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel‐wise Spearman coefficient rS, and Dice similarity coefficients evaluated for low function lung (DSClow) and high function lung (DSChigh). Results A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant‐submitted DIR motion fields using the in‐house software, VESPIR. The rS and DSC results reveal a high degree of inter‐algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross‐modality correlations used a biomechanical model‐based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27–0.73) for rS, 0.52 (0.36–0.67) for DSClow, and 0.45 (0.28–0.62) for DSChigh. All other algorithms exhibited at least one negative rS value, and/or one DSC value less than 0.5. Conclusions The VAMPIRE Challenge results demonstrate that the cross‐modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a “gold standard,” highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic ce...