Expanding Genotype/Phenotype Correlation in 2p11.2-p12 Microdeletion Syndrome.

Chromosomal abnormalities on the short arm of chromosome 2 in the region p11.2 have been associated with developmental delay, intellectual disability, facial anomalies, abnormal ears, skeletal and genital malformations. Here we describe a patient with a de novo interstitial heterozygous microdeletion on the short arm of chromosome 2 in the region p11.2-p12. He presents with facial dysmorphism characterized by a broad and low root of the nose and low-set protruding ears. Clinical examinations during follow-up visits revealed congenital pendular nystagmus, decreased visual acuity and psychomotor development disorder including intellectual disability. The heterozygous 5 Mb-microdeletion was characterized by an array CGH (Comparative Genomic Hybridization) analysis. In the past two decades, nine patients with microdeletions in this region have been identified by array CGH analysis and were reported in the literature. All these patients show psychomotor development disorder and outer and/or inner ear anomalies. In addition, most of the patients have mild to severe intellectual disability and show facial malformations. We reviewed the literature on PubMed and OMIM using the gene/loci names as search terms in an attempt to identify correlations between genes located within the heterozygous microdeletion and the clinical phenotype of the patient, in order to define a recognizable phenotype for the 2p11.2p12 microdeletion syndrome. We discuss additional symptoms that are not systematically present in all patients and contribute to a heterogeneous clinical presentation of this microdeletion syndrome.

A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making.

(1) Background: To evaluate the performance of a deep learning model to automatically segment femoral head necrosis (FHN) based on a standard 2D MRI sequence compared to manual segmentations for 3D quantification of FHN. (2) Methods: Twenty-six patients (thirty hips) with avascular necrosis underwent preoperative MR arthrography including a coronal 2D PD-w sequence and a 3D T1 VIBE sequence. Manual ground truth segmentations of the necrotic and unaffected bone were then performed by an expert reader to train a self-configuring nnU-Net model. Testing of the network performance was performed using a 5-fold cross-validation and Dice coefficients were calculated. In addition, performance across the three segmentations were compared using six parameters: volume of necrosis, volume of unaffected bone, percent of necrotic bone volume, surface of necrotic bone, unaffected femoral head surface, and percent of necrotic femoral head surface area. (3) Results: Comparison between the manual 3D and manual 2D segmentations as well as 2D with the automatic model yielded significant, strong correlations (Rp > 0.9) across all six parameters of necrosis. Dice coefficients between manual- and automated 2D segmentations of necrotic- and unaffected bone were 75 ± 15% and 91 ± 5%, respectively. None of the six parameters of FHN differed between the manual and automated 2D segmentations and showed strong correlations (Rp > 0.9). Necrotic volume and surface area showed significant differences (all p < 0.05) between early and advanced ARCO grading as opposed to the modified Kerboul angle, which was comparable between both groups (p > 0.05). (4) Conclusions: Our deep learning model to automatically segment femoral necrosis based on a routine hip MRI was highly accurate. Coupled with improved quantification for volume and surface area, as opposed to 2D angles, staging and course of treatment can become better tailored to patients with varying degrees of AVN.