Publications by authors named "K T Shahwan"
Article Synopsis
- - The study aimed to develop a deep learning segmentation model to locate basal cell carcinoma (BCC) on Mohs surgery (MMS) frozen section slides, which is crucial for precise tumor removal.
- - Researchers utilized a dataset of 348 tissue slides and trained the model using the Ultralytics YOLOv8 framework, achieving varying sensitivity and specificity rates by BCC subtype.
- - Results indicated good overall performance with a sensitivity of 71% and specificity of 75%, but highlighted the need for improved performance metrics for clinical application in segmentation studies.
Basal cell carcinoma (BCC) is the most frequently diagnosed form of skin cancer, and its incidence continues to rise, particularly among older individuals. This trend puts a significant strain on health care systems, especially in terms of histopathologic diagnostics required for Mohs micrographic surgery (MMS), which is used to treat BCC in sensitive locations to minimize tissue loss. This study aims to address the challenges in BCC detection within MMS whole-slide images by developing and evaluating a deep learning model that bridges weakly supervised learning with interpretable segmentation-based methods through attention maps.
View Article and Find Full Text PDFImportance: Cutaneous squamous cell carcinoma (CSCC) is the second most common malignant disease in the US. Although it typically carries a good prognosis, a subset of CSCCs are highly aggressive, carrying regional and distant metastatic potential. Due to its high incidence, this aggressive subset is responsible for considerable mortality, with an overall annual mortality estimated to equal or even surpass melanoma.
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