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

Dissertatio historica de initiis monarchiae Babyloniorum, quam, cum cons. ampliss. Colleg. Philos. in Reg. Acad. Upsal. sub praesidio ... Jacobi Arrhenii ... publico examini modeste subjicit Petrus Hagberg Gestr. In audit. Gustav. maj. ad d. 25. Maji. Anni MDCCV.

International audienceBackground : The incidence of childhood type 1 diabetes (T1D) incidence is rising in many countries, supposedlybecause of changing environmental factors, which are yet largely unknown. The purpose of the study was tounravel environmental markers associated with T1D. Methods : Cases were children with T1D from the French Isis-Diab cohort. Controls were schoolmates or friends ofthe patients. Parents were asked to fill a 845-item questionnaire investigating the child’s environment before diagnosis.The analysis took into account the matching between cases and controls. A second analysis used propensity scoremethods. Results : We found a negative association of several lifestyle variables, gastroenteritis episodes, dental hygiene, hazelnutcocoa spread consumption, wasp and bee stings with T1D, consumption of vegetables from a farm and death of a petby old age. Conclusions : The found statistical association of new environmental markers with T1D calls for replication in othercohorts and investigation of new environmental areas