Search PubMed⌕ Search

PubMed · 9735136

Insulinoma.

Abstract

Symptoms most characteristically diagnostic of insulinoma are those of neuroglycopenia. The combination of hypoglycemia and endogenous hyperinsulinemia are pathognomonic of insulinoma. Several localization techniques are available, the choice of which best depends on the best expertise at individual institutions. Intraoperative ultrasonography is helpful in localization and defining related anatomy. Enucleation of these intrapancreatic tumors is preferred, but for body and tail lesions, distal pancreatic resection may be required. Because at least 90% are benign, long-term cure with complete resolution of preoperative symptoms is expected.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C S Grant. 1998. Insulinoma.. https://pubmed.ncbi.nlm.nih.gov/9735136/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗