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  1. Salivary gland anlage tumour of the nasopharynx: A case report and review for histopathological characteristics
    Başak K, Günhan Ö, Akbulut S, Aydin S
    Malays J Pathol, 2019 Dec;41(3):345-350.
    PMID: 31901920
    INTRODUCTION: Congenital salivary gland anlage tumour of the nasopharynx is a lesion which usually presents with nasal and upper respiratory tract obstruction in the neonatal period. Timely diagnosis is essential to prevent the occurrence of respiratory complications in later childhood.

    CASE REPORT: We present a 8-year-old boy complaining from difficulty in breathing and breastfeeding in the neonatal period due to an adenoid-like nasopharyngeal mass. Histological examination revealed solid and cystic squamous nests and numerous duct-like structures within collagenised stroma. Both epithelial and myoepithelial differentiation were noted in the tubular component.

    DISCUSSION: A review of the clinical and histopathological features of published cases revealed that ancient lesions showed more prominent and complex epithelial component and more collagen rich stroma. We would like to suggest the possibility of salivary gland anlage tumour to be considered in the differential diagnosis of neonatal respiratory distress cases.

  2. Fulltext Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
    Jameel SK, Aydin S, Ghaeb NH, Majidpour J, Rashid TA, Salih SQ, et al.
    Biomolecules, 2022 Dec 16;12(12).
    PMID: 36551316 DOI: 10.3390/biom12121888
    Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.
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