Neonatal Diabetes due to a Mutation in the Distal PTF1A Enhancer: A Case Report and Literature Review

: Biallelic variants in the pancreas-specific transcription factor 1A (PTF1A) gene are a rare cause of permanent neonatal diabetes. We report a case of neonatal diabetes with unique clinical manifestations. The clinical diagnosis of the affected infant was confirmed by insufficient endocrine and exocrine pancreas activity; however, the pancreas was normal in imaging. Molecular analyses identified a novel homozygous single nucleotide variant (Chr10, g.23508441T > G), affecting a highly conserved nucleotide within a distal enhancer of the PTF1A gene. The literature review showed that most of these patients had IUGR and imaging evidence of pancreatic agenesis or hypoplasia. We suggest that pancreatic imaging and evaluation of exocrine pancreas function can help early confirmation of the diagnosis in patients with permanent neonatal diabetes.

Initiating pancreatic neuroendocrine tumour (pNET) screening in young MEN1 patients: results from the DutchMEN Study Group

Context Non-functioning pancreatic neuroendocrine tumours (NF-pNETs) are highly prevalent and constitute an important cause of mortality in patients with multiple endocrine neoplasia type 1 (MEN1). Still, the optimal age to initiate screening for pNETs is under debate. Objective To assess the age of occurrence of clinically relevant NF-pNETs in young MEN1 patients. Patients and setting Pancreatic imaging data of MEN1 patients were retrieved from the DutchMEN Study Group database. Design Interval-censored survival methods were used to describe age-related penetrance, compare survival curves, and develop a parametric model for estimating the risk of having clinically relevant NF-pNET at various ages. Intervention(s) Not applicable. Main outcome measure(s) The primary objective was to assess age at occurrence of clinically relevant NF-pNET (size ≥20 mm or rapid growth); secondary objectives were the age at occurrence of NF-pNET of any size and pNET-associated metastasized disease. Results Five of 350 patients developed clinically relevant NF-pNETs before age 18, two of which subsequently developed lymph node metastases. No differences in clinically relevant NF-pNET-free survival were found for sex, timeframe, and type of MEN1 diagnosis or genotype. The estimated ages (median, 95% CI) at a 1%, 2.5% and 5% risk of having developed a clinically relevant tumour are 9.5 (6.5 – 12.7), 13.5 (10.2 – 16.9) and 17.8 years (14.3 – 21.4) respectively. Conclusion Analyses from this population-based cohort indicate that start of surveillance for NF-pNETs with pancreatic imaging at age 13–14 is justified. The psychological and medical burden of screening at a young age should be considered.

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.