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Five percent of fetuses presents increased fetal nuchal translucency. It is a well-known marker for aneuploidy (T21, Turner syndrome) and a variety of monogenic syndromes such as Noonan syndrome and certain skeletal dysplasias, as well as associated with structural malformations such as congenital heart disease. Current diagnostic algorithms for increased nuchal translucency include a rapid test for aneuploidy (fluorescence in situ hybridization, FISH, or quantitative PCR), a cytogenetic analysis (karyotype or chromosomal microarray, CMA) followed by or concurrent with targeted gene panel analysis for RASopathies/Noonan syndrome. Some centers now propose whole exome sequencing as an adjunct, but its usefulness in isolated increased nuchal translucency remains debated. We describe the recurrence of apparently isolated increased nuchal translucency in 2 euploid fetuses. Whole genome sequencing identified two compound heterozygous variants in the NUP107 gene in both fetuses. Biallelic variants in NUP107 are responsible for severe steroid-resistant nephrotic syndrome, either isolated or syndromic (Galloway-Mowat syndrome); in addition to the renal phenotype, the latter also includes intellectual deficiency and dysmorphic features. Pregnancy termination made it impossible to assess whether the NUP107 variants found would have resulted in isolated or syndromic steroid-resistant nephrotic syndrome. However, identifying the responsible gene improved the accuracy of the genetic counseling. This family is an example of the added benefit of introducing WES/WGS in standardized protocols for prenatal diagnosis of euploid fetuses in "isolated" increased nuchal translucency., (© 2024 The Author(s). American Journal of Medical Genetics Part A published by Wiley Periodicals LLC.)

Objectives: To characterize a suggestive prenatal imaging pattern of Aicardi syndrome using ultrasound and MR imaging., Methods: Based on a retrospective international series of Aicardi syndrome cases from tertiary centers encountered over a 20-year period (2000-2020), we investigated the frequencies of the imaging features in order to characterize an imaging pattern highly suggestive of the diagnosis., Results: Among 20 cases included, arachnoid cysts associated with a distortion of the interhemispheric fissure were constantly encountered associated with complete or partial agenesis of the corpus callosum (19/20, 95%). This triad in the presence of other CNS disorganization, such as polymicrogyria (16/17, 94%), heterotopias (15/17, 88%), ventriculomegaly (14/20, 70%), cerebral asymmetry [14/20, 70%]) and less frequently extra-CNS anomaly (ocular anomalies [7/11, 64%], costal/vertebral segmentation defect [4/20, 20%]) represent a highly suggestive pattern of Aicardi syndrome in a female patient., Conclusion: Despite absence of genetic test to confirm prenatal diagnosis of AS, this combination of CNS and extra-CNS fetal findings allows delineation of a characteristic imaging pattern of AS, especially when facing dysgenesis of the corpus callosum., (© 2022 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.)

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)