Understanding the Treatment Algorithm of Patients with Metastatic Pancreatic Neuroendocrine Neoplasms: A Single-Institution Retrospective Analysis Comparing Outcomes of Chemotherapy, Molecular Targeted Therapy, and Peptide Receptor Radionuclide Therapy in 255 Patients

Background The number of therapeutic options for patients with pancreatic neuroendocrine neoplasms (PNEN) has increased, but the optimal therapeutic algorithm has not been defined due to lack of randomised trials comparing different modalities. Methods We performed a retrospective study in patients with metastatic PNEN treated with ≥1 line of systemic therapy. The relationship between baseline characteristics, treatment type and time to treatment failure (TTF), time to progression (TTP) and overall survival (OS) was analysed using the Kaplan-Meier method. Univariate and multivariate analyses were performed using the Cox proportional hazards model. Results Two hundred and fifty-five patients with metastatic PNEN had 491 evaluable lines of therapy. Independent predictors of TTF included treatment type, Ki-67, tumour grade and chromogranin A. To reduce selection bias, a subgroup of 114 patients with grade 2 (G2) metastatic pancreatic neuroendocrine tumours (PNET) was analysed separately. These patients had received 234 lines of treatment (105 chemotherapy, 82 molecular targeted therapy, and 47 peptide receptor radionuclide therapy [PRRT]). In the G2 cohort, TTF and TTP were superior for PRRT compared with both chemotherapy and molecular targeted therapy. OS in the G2 cohort was also superior for those that had received PRRT compared with those that had not (median 84 vs 56 months; HR 0.55, 95%CI 0.31-0.98, p=0.04). Conclusions This study suggests that PRRT is associated with superior clinical outcomes relative to other systemic therapies for G2 metastatic PNET. Prospective studies are required to confirm these observations

Prostate cancer risk related to foods, food groups, macronutrients and micronutrients derived from the UK Dietary Cohort Consortium food diaries.

BACKGROUND/OBJECTIVES: The influence of dietary factors remains controversial for screen-detected prostate cancer and inconclusive for clinically detected disease. We aimed to examine these associations using prospectively collected food diaries. SUBJECTS/METHODS: A total of 1,717 prostate cancer cases in middle-aged and older UK men were pooled from four prospective cohorts with clinically detected disease (n=663), with routine data follow-up (means 6.6-13.3 years) and a case-control study with screen-detected disease (n=1054), nested in a randomised trial of prostate cancer treatments (ISCTRN 20141297). Multiple-day food diaries (records) completed by men prior to diagnosis were used to estimate intakes of 37 selected nutrients, food groups and items, including carbohydrate, fat, protein, dairy products, fish, meat, fruit and vegetables, energy, fibre, alcohol, lycopene and selenium. Cases were matched on age and diary date to at least one control within study (n=3528). Prostate cancer risk was calculated, using conditional logistic regression (adjusted for baseline covariates) and expressed as odds ratios in each quintile of intake (±95% confidence intervals). Prostate cancer risk was also investigated by localised or advanced stage and by cancer detection method. RESULTS: There were no strong associations between prostate cancer risk and 37 dietary factors. CONCLUSIONS: Prostate cancer risk, including by disease stage, was not strongly associated with dietary factors measured by food diaries in middle-aged and older UK men.Medical Research Council (Grant ID: MC_UU_12019/1), Medical Research Council Population Health Sciences Research Network, British Heart Foundation, Cancer Research UK (Grant ID: C8221/A19170), Department of Health, Food Standards Agency, Stroke Association, WCRF, National Institute for Health Research Health Technology Assessment Programme (Project IDs: 96/20/06, 96/20/99), National Cancer Research Institute (formed by Cancer Research UK, Medical Research Council, Department of Health)This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ejcn.2016.16

Multigroup Ethnic Identity Measure (MEIM) Expansion: Measuring Racial, Religious, and National Aspects of Sense of Ethnic Identity Within the United Kingdom

These studies examined the degree to which racial, religious, and national aspects of individuals' sense of ethnic identity stand as interrelated, yet distinct, constructs. Results of exploratory factor analyses in Study 1 (n = 272) revealed that a three-factor model specifying racial, religious, and national identities yielded optimal fit to correlational data from an expanded, 36-item version of the Multigroup Ethnic Identity Measure (MEIM; Roberts et al., 1999), although results left room for improvement in model fit. Subsequently, results of confirmatory factor analyses in Study 2 (n = 291) revealed that, after taking covariance among the items into account, a six-factor model specifying exploration and commitment dimensions within each of the racial, religious, and national identity constructs provided optimal fit. Implications for the utility of Goffman's (1963b) interactionist role theory and Erikson's (1968) ego psychology for understanding the full complexity of felt ethnic identity are discussed

Microscopic infrared mapping of chloromethylated polystyrene resin beads

In solid-phase combinatorial chemistry, analyses are performed using a wide range of analytical techniques ranging from gel-phase nuclear magnetic resonance (NMR) to colorimetric tests to elemental analysis. However, these techniques cannot be used to interrogate functional group distribution at the single-bead level. This paper explores the feasibility of using Fourier transform infrared (FTIR) microscopy to examine site distribution on chloromethylated polystyrene resin beads and to quantify the loading after coupling with 4-cyanophenol, an IR tagging agent. FT-IR microscopy also provides a unique opportunity to better understand the reactivity of highly cross-linked polymer beads under a range of chemical conditions

The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors.

Aim: The RAISE project assessed whether deep learning could improve early progression-free survival (PFS) prediction in patients with neuroendocrine tumors. Patients & methods: Deep learning models extracted features from CT scans from patients in CLARINET (NCT00353496) (n = 138/204). A Cox model assessed PFS prediction when combining deep learning with the sum of longest diameter ratio (SLDr) and logarithmically transformed CgA concentration (logCgA), versus SLDr and logCgA alone. Results: Deep learning models extracted features other than lesion shape to predict PFS at week 72. No increase in performance was achieved with deep learning versus SLDr and logCgA models alone. Conclusion: Deep learning models extracted relevant features to predict PFS, but did not improve early prediction based on SLDr and logCgA

Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors.

Aim: The RAISE project aimed to find a surrogate end point to predict treatment response early in patients with enteropancreatic neuroendocrine tumors (NET). Response heterogeneity, defined as the coexistence of responding and non-responding lesions, has been proposed as a predictive marker for progression-free survival (PFS) in patients with NETs. Patients & methods: Computerized tomography scans were analyzed from patients with multiple lesions in CLARINET (NCT00353496; n = 148/204). Cox regression analyses evaluated association between response heterogeneity, estimated using the standard deviation of the longest diameter ratio of target lesions, and NET progression. Results: Greater response heterogeneity at a given visit was associated with earlier progression thereafter: week 12 hazard ratio (HR; 95% confidence interval): 1.48 (1.20-1.82); p < 0.001; n = 148; week 36: 1.72 (1.32-2.24); p < 0.001; n = 108. HRs controlled for sum of longest diameter ratio: week 12: 1.28 (1.04-1.59); p = 0.020 and week 36: 1.81 (1.20-2.72); p = 0.005. Conclusion: Response heterogeneity independently predicts PFS in patients with enteropancreatic NETs. Further validation is required