Advanced Deep Learning Framework for Cancer Cell Morphological Analysis and Tumor Mutational Burden Prediction From Histopathological Images

Cancer cell morphological analysis represents a fundamental approach for understanding tumor behavior and predicting treatment responses in precision oncology. Tumor mutational burden (TMB) is a critical biomarker for immunotherapy efficacy, traditionally measured through expensive and time-consuming whole-exome sequencing methods. This study introduces CellMorphNet, an innovative deep learning architecture integrating cancer cell morphological feature extraction with advanced attention mechanisms to predict TMB levels from routine histopathological images. Our framework employs a novel cellular deconvolution structure that separates histological staining components, enabling enhanced visualization of cancer cell characteristics crucial for TMB assessment. The model incorporates a hierarchical routing attention mechanism that selectively focuses on morphologically relevant cellular regions, reducing computational complexity while improving prediction accuracy. The CellMorphNet architecture consists of a four-stage hierarchical pyramid structure, where each stage contains morphological pooling layers and specialized CellMorph blocks for extracting cancer cell features. Experimental validation across five independent training runs on TCGA datasets demonstrated that CellMorphNet achieved superior performance in binary classification with AUC, F1-score, precision, and recall values of 99.2$\pm$ 0.3%, 96.8$\pm$ 0.4%, 97.1$\pm$ 0.4%, and 96.5$\pm$ 0.5%, respectively. For ternary classification, accuracy, precision, recall, and F1-scores reached 96.8$\pm$ 0.4%, 96.3$\pm$ 0.5%, 96.5$\pm$ 0.4%, and 96.4$\pm$ 0.5%, respectively. Statistical tests confirmed the superiority of CellMorphNet over existing methods ($p< 0.001$), establishing this approach as a cost-effective alternative to molecular sequencing for clinical decision-making in cancer immunotherapy.