Next-generation sequencing is highly sensitive for the detection of beta-catenin mutations in desmoid-type fibromatoses

Desmoid-type fibromatoses are locally aggressive and frequently recurrent tumours, and an accurate diagnosis is essential for patient management. The majority of sporadic lesions harbour beta-catenin (CTNNB1) mutations. We used next-generation sequencing to detect CTNNB1 mutations and to compare the sensitivity and specificity of next-generation sequencing with currently employed mutation detection techniques: mutation-specific restriction enzyme digestion and polymerase chain reaction amplification. DNA was extracted from formalin-fixed paraffin-embedded needle biopsy or resection tissue sections from 144 patients with sporadic desmoid-type fibromatoses, four patients with syndrome-related desmoid-type fibromatoses and 11 morphological mimics. Two primer pairs were designed for CTNNB1 mutation hotspots. Using ≥10 ng of DNA, libraries were generated by Fluidigm and sequenced on the Ion Torrent Personal Genome Machine. Next-generation sequencing had a sensitivity of 92.36 % (133/144, 95 % CIs: 86.74 to 96.12 %) and a specificity of 100 % for the detection of CTNNB1 mutations in desmoid-type fibromatoses-like spindle cell lesions. All mutations detected by mutation-specific restriction enzyme digestion were identified by next-generation sequencing. Next-generation sequencing identified additional mutations in 11 tumours that were not detected by mutation-specific restriction enzyme digestion, two of which have not been previously described. Next-generation sequencing is highly sensitive for the detection of CTNNB1 mutations. This multiplex assay has the advantage of detecting additional mutations compared to those detected by mutation-specific restriction enzyme digestion (sensitivity 82.41 %). The technology requires minimal DNA and is time- and cost-efficient.

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.