Dermatologic manifestations in paediatric neurofibromatosis type 2: a cross sectional descriptive multicentric study.

BACKGROUND: Neurofibromatosis type 2 (NF2) is characterized by bilateral vestibular schwannoma (VS) more often in adults but a severe paediatric form with multiple neurological tumours is also described. In this population, a early diagnosis is important to prevent the onset of neurological complications but is difficult, particularly without a familial history. Cutaneous manifestations, which may precede VS or neurological tumours by several years, may contribute to an early diagnosis, but specific studies are lacking. The objective of this study was to characterize cutaneous manifestations of NF2 in a paediatric population. RESULTS: This observational, descriptive and multicentric study was conducted from April 2019 to April 2020 in seven academic French hospitals. We included patients ≤ 18 years old who fulfilled the Manchester diagnostic criteria or had a pathogenic mutation identified in the NF2 gene. All patients underwent a dermatological examination guided by a standardized questionnaire. 21 children were included, of whom 20 had at least one skin tumour (mean number 5 ± 4.6 [range 0-15]), which led to a diagnosis in four cases. In the other 17 cases, the diagnosis of NF2 was based on neurosensory complications (n = 10), family screening (n = 4) or ocular signs (n = 3). Before the NF2 diagnosis, 15 children had at least one "undiagnosed" cutaneous tumour that did not lead to a specific management. Patients' dermatological examination also revealed < 6 non specific café au lait macules (n = 15), hypopigmented macules (n = 12) with more than 3 lesions in 4 cases, and purple reticulated macules of the trunk (n = 4). CONCLUSION: Dermatological lesions are frequent and early in children with NF2 but rarely lead to the diagnosis. Cutaneous schwannomas are the most frequent but are often underdiagnosed. Café au lait macules are frequent, but atypical and mostly in small numbers. Multiple hypopigmented macules seem suggestive although inconsistent. The sensitivity of reticulated capillary malformation-like lesions remains to be assessed by further studies.

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography.

PURPOSE: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images. METHODS: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed. RESULTS: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions. CONCLUSIONS: The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.