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A Deep Learning AI Model for Histopathological Diagnosis and Grading of Mucoepidermoid Carcinoma of Salivary Glands
Doha Ibrahim Abd El Ati1, Safa Fathy2, Hamza EmadElDin Hamza3
1Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, Cairo University, Cairo, Egypt. doha-ibrahem@dentistry.cu.edu.eg.
Purpose:
This study aims to evaluate the effectiveness of deep learning AI model in the diagnosis and grading of mucoepidermoid carcinoma.
Methods:
We developed a multiple instance learning based-deep learning model which consisted of two modules. One to perform slide-level classification of whole slide images into mucoepidermoid carcinoma and non-mucoepidermoid carcinoma classes and the second one to perform grading into low, intermediate, and high grades. A total of 194 whole slide images were included in the study. Of these, 92 corresponded to mucoepidermoid carcinoma, while the remaining 102 represented non-mucoepidermoid carcinoma cases.
Results:
Our results showed that the classification model achieved 90.62% accuracy, 90.62% macro F1 score, 90.60% weighted F1 score, 91.67% macro precision, 91.18% macro recall. The grading model achieved an accuracy of 73.3%, weighted F1-score of 73.9%, weighted precision of 76.1% and weighted recall of 73.3%.
Conclusion:
The proposed deep learning classification model achieved promising performance in differentiating mucoepidermoid carcinoma cases. However, the grading model achieved an overall accuracy of 73.3%. Thus, depending on AI solely is still questionable, however it can be used to augment the work of the pathologist.