Development of Automated Risk Stratification for Sporadic Odontogenic Keratocyst Whole Slide Images with an Attention-Based Image Sequence Analyzer.

(1) Background: The categorization of recurrent and non-recurrent odontogenic keratocyst is complex and challenging for both clinicians and pathologists. What sets this cyst apart is its aggressive nature and high likelihood of recurrence. Despite identifying various predictive clinical/radiological/histopathological parameters, clinicians still face difficulties in therapeutic management due to its inherent aggressive nature. This research aims to build a pipeline system that accurately detects recurring and non-recurring OKC. (2) Objective: To automate the risk stratification of OKCs as recurring or non-recurring based on whole slide images (WSIs) using an attention-based image sequence analyzer (ABISA). (3) Materials and methods: The presented architecture combines transformer-based self-attention mechanisms with sequential modeling using LSTM (long short-term memory) to predict the class label. This architecture leverages self-attention to capture spatial dependencies in image patches and LSTM to capture sequential dependencies across patches or frames, making it suitable for this image analysis. These two powerful combinations were integrated and applied on a custom dataset of 48 labeled WSIs (508 tiled images) generated from the highest zoom level WSI. (4) Results: The proposed ABISA algorithm attained 0.98, 1.0, and 0.98 testing accuracy, recall, and area under the curve, respectively, whereas VGG16, VGG19, and Inception V3, standard vision transformer attained testing accuracies of 0.80, 0.73, 0.82, 0.91, respectively. ABISA used 58% fewer trainable parameters than the standard vision transformer. (5) Conclusions: The proposed novel ABISA algorithm was integrated into a risk stratification pipeline to automate the detection of recurring OKC significantly faster, thus allowing the pathologist to define risk stratification faster.

Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images.

The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy.

Unusual Variant of Unicystic Ameloblastoma with CEOT-Like Areas: A Rare Case Report with Review of Literature.

Ameloblastoma is an epithelial odontogenic neoplasm with clinical and histological diversity. They are locally invasive tumors with 3 clinical variants such as solid, unicystic, and peripheral ameloblastomas, and the unicystic variant constitutes only 13%. Histologically, it shows diverse microscopic patterns that may occur isolated or in combination with other patterns. The granular cell variant accounts for 3.5% of all ameloblastoma cases. The eosinophilic granules seen in the cytoplasm of the tumor are thought to be lysosomes and presumably contribute to the pathogenesis of the tumor. Although such a phenomenon is rare in unicystic ameloblastoma, granular cell differentiation in solid multicystic ameloblastoma is a well-established phenomenon. In this paper, we present a unique case of unicystic ameloblastoma with granular cell differentiation with a brief review.

Ameloblastomatous calcifying cystic odontogenic tumour: a rare variant.

Calcifying Cystic Odontogenic Tumor (CCOT) was previously described by Gorlin et al., in 1962 as Calcifying odontogenic cyst. CCOT is a rare lesion which accounts for 2% of all odontogenic pathological changes in the jaws. One of the variants, Ameloblastomatous proliferating type of CCOT is even more rare and very few cases have been reported in the light of literature review. This case report is an effort to bring forth a case of ameloblastomatous proliferating type of CCOT in a 65 year male, who presented with a painful swelling in the right jaw crossing midline causing facial asymmetry and confirmed by histopathological evaluation.