Validation of the needle targeting accuracy of a MRI/TRUS- image-guided system for transperineal prostate cancer biopsy

Multi-parametric magnetic resonance imaging (mp-MRI) has become an increasingly important method for detecting and treating prostate cancer. Transrectal ultrasound (TRUS) is the most commonly used method for guiding prostate needle biopsy and remains the gold standard for diagnosis of prostate cancer. MRI-to-TRUS image reg- istration is an important technology for enabling computer-assisted targeting of the majority of prostate lesions that are visible in MRI but not independently distinguishable in TRUS images. The aim of this study was to estimate the needle placement accuracy of an image guidance system (SmartTargetÒ), developed by our research group, using a surgical training phantom.

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.

Diagnostic value of H3F3A mutations in giant cell tumour of bone compared to osteoclast-rich mimics

Driver mutations in the two histone 3.3 (H3.3) genes, H3F3A and H3F3B, were recently identified by whole genome sequencing in 95% of chondroblastoma (CB) and by targeted gene sequencing in 92% of giant cell tumour of bone (GCT). Given the high prevalence of these driver mutations, it may be possible to utilise these alterations as diagnostic adjuncts in clinical practice. Here, we explored the spectrum of H3.3 mutations in a wide range and large number of bone tumours (n 5 412) to determine if these alterations could be used to distinguish GCT from other osteoclast-rich tumours such as aneurysmal bone cyst, nonossifying fibroma, giant cell granuloma, and osteoclast-rich malignant bone tumours and others. In addition, we explored the driver landscape of GCT through whole genome, exome and targeted sequencing (14 gene panel). We found that H3.3 mutations, namely mutations of glycine 34 in H3F3A, occur in 96% of GCT. We did not find additional driver mutations in GCT, including mutations in IDH1, IDH2, USP6, TP53. The genomes of GCT exhibited few somatic mutations, akin to the picture seen in CB. Overall our observations suggest that the presence of H3F3A p.Gly34 mutations does not entirely exclude malignancy in osteoclast-rich tumours. However, H3F3A p.Gly34 mutations appear to be an almost essential feature of GCT that will aid pathological evaluation of bone tumours, especially when confronted with small needle core biopsies. In the absence of H3F3A p.Gly34 mutations, a diagnosis of GCT should be made with caution.

Kroppenstedtia pulmonis sp. nov. and Kroppenstedtia sanguinis sp. nov., isolated from human patients

Three human clinical strains (W9323T , X0209T and X0394) isolated from lung biopsy, blood and cerebral spinal fluid, respectively, were characterized using a polyphasic taxonomic approach. Comparative analysis of the 16S rRNA gene sequences showed the three strains belonged to two novel branches within the genus Kroppenstedtia : 16S rRNA gene sequence analysis of W9323T showed closest sequence similarity to Kroppenstedtia eburnea JFMB- ATET (95.3 %), Kroppenstedtia guangzhouensis GD02T (94.7 %) and strain X0209T (94.6 %); sequence analysis of strain X0209T showed closest sequence similarity to K . eburnea JFMB- ATET (96.4 %) and K. guangzhouensis GD02T (96.0 %). Strains X0209T and X0394 were 99.9 % similar to each other by 16S rRNA gene sequence analysis. The DNA- DNA relatedness was 94.6 %, confirming that X0209T and X0394 belong to the same species. Chemotaxonomic data for strains W9323T and X0209T were consistent with those described for the genus Kroppenstedtia : whole- cell peptidoglycan contained LL- diaminopimelic acid; the major cellular fatty acids were iso- C15 and anteiso- C15 ; and the major menaquinone was MK- 7. Different endospore morphology, carbon utilization profiles, and whole cell wall sugar patterns of strains W9323T and X0209T supported by phylogenetic analysis enabled us to conclude that the strains represent two new species within the genus Kroppenstedtia , for which the names Kroppenstedtia pulmonis sp. nov. (type strain W9323T = DSM 45752T = CCUG 68107T) and Kroppenstedtia sanguinis sp. nov. (type strain X0209T = DSM 45749T = CCUG 38657T) are proposed.

Example-Based Face Shape Recovery Using the Zenith Angle of the Surface Normal

We present a method for recovering facial shape using an image of a face and a reference model. The zenith angle of the surface normal is recovered directly from the intensities of the image. The azimuth angle of the reference model is then combined with the calculated zenith angle in order to get a new field of surface normals. After integration of the needle map, the recovered surface has the effect of mapped facial features over the reference model. Experiments demonstrate that for the lambertian case, surface recovery is achieved with high accuracy. For non-Lambertian cases, experiments suggest potential for face recognition applications.