Development and evaluation of a deep learning framework for screening of cervical lymph node metastasis in microscopic images of oral squamous cell carcinoma: A proof of concept study

Abstract Introduction : Lymph node (LN) metastasis serves as a critical prognostic indicator in oral squamous cell carcinoma (OSCC), profoundly influencing treatment approaches and survival outcomes. This proof‐of‐concept study developed and internally evaluated an economical, deep learning (DL)‐assisted screening framework for identifying metastatic deposits in microscopic images of LNs from OSCC. The accessibility and operational costs of a WSI pose a major constraint in developing nations and resource‐limited environments. Methods : An in‐house dataset of conventional microscopic images (×400 magnification) was prepared from slides of LNs collected from 77 patients (one slide from each patient, 40 metastatic and 37 non‐metastatic cases). The ‘Training and Validation’ subset of this dataset contained 16,426 metastasis‐positive and 14,192 non‐metastatic image patches of 256 × 256 pixels prepared from the microscopic images and were later augmented and used for training five deep‐learning image classification models using the transfer learning approach. Among the five, fine‐tuned convolutional neural network model, ‘Xception’, achieved the best performance metrics without evident overfitting. The in‐house dataset also had a strictly separate EVALUATION component/EV subset that contained a total of 1938 metastasis‐positive and 1604 metastasis‐free microscopic images of LNs of size 1024 × 768 pixels (×400 magnification). Using a custom Python‐based framework and the fine‐tuned ‘Xception’ model, a software tool META_DETECT‐AI was developed and tested over the whole microscopic images of the EV subset. Results : The proposed tool demonstrated 97.94% specificity, 99.85% sensitivity, and 98.98% accuracy with visually explainable heatmap generation capabilities over the microscopic images of the EV subset. At the slide level, it showed 100% sensitivity, 71.43% specificity, and 88.24% accuracy. Conclusion : The developed framework is intended as a decision‐support tool for pathologist support and screening and prioritisation of suspicious microscopic images and not as a replacement for slide‐level diagnosis or immunohistochemistry. Upon further validation on a larger, heterogeneous, multicentre dataset, this framework may be integrated with augmented reality microscopy/digital pathology workflow.