ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis.

Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).

Identification of CSF3R Mutations in B-Lineage Acute Lymphoblastic Leukemia Using Comprehensive Cancer Panel and Next-Generation Sequencing.

B-lineage acute lymphocytic leukemia (B-ALL) is characterized by different genetic aberrations at a chromosomal and gene level which are very crucial for diagnosis, prognosis and risk assessment of the disease. However, there is still controversial arguments in regard to disease outcomes in specific genetic abnormalities, e.g., 9p-deletion. Moreover, in absence of cytogenetic abnormalities it is difficult to predict B-ALL progression. Here, we use the advantage of Next-generation sequencing (NGS) technology to study the mutation landscape of 12 patients with B-ALL using Comprehensive Cancer Panel (CCP) which covers the most common mutated cancer genes. Our results describe new mutations in CSF3R gene including S661N, S557G, and Q170X which might be associated with disease progression.

Tetrasomy 18p: case report and review of literature.

Tetrasomy 18p syndrome (Online Mendelian Inheritance in Man 614290) is a very rare chromosomal disorder that is caused by the presence of isochromosome 18p, which is a supernumerary marker composed of two copies of the p arm of chromosome 18. Most tetrasomy 18p cases are de novo cases; however, familial cases have also been reported. It is characterized mainly by developmental delays, cognitive impairment, hypotonia, typical dysmorphic features, and other anomalies. Herein, we report de novo tetrasomy 18p in a 9-month-old boy with dysmorphic features, microcephaly, growth delay, hypotonia, and cerebellar and renal malformations. We compared our case with previously reported ones in the literature. Clinicians should consider tetrasomy 18p in any individual with dysmorphic features and cardiac, skeletal, and renal abnormalities. To the best of our knowledge, we report for the first time an association of this syndrome with partial agenesis of cerebellar vermis.