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

Area of Science:

• Computational pathology
• Artificial intelligence in oncology
• Biomarker discovery

Background:

• Cancer cell morphology analysis aids in understanding tumor behavior and treatment response.
• Tumor mutational burden (TMB) is vital for predicting immunotherapy efficacy but traditionally requires costly sequencing.
• There is a need for efficient methods to assess TMB in clinical settings.

Purpose of the Study:

• To introduce CellMorphNet, a deep learning model for predicting TMB from histopathological images.
• To develop a cost-effective and rapid method for TMB assessment.
• To integrate morphological feature extraction with attention mechanisms for accurate TMB prediction.

Main Methods:

• Developed CellMorphNet, a deep learning architecture with a four-stage hierarchical pyramid structure.
• Utilized a novel cellular deconvolution for enhanced visualization of cancer cell characteristics.
• Incorporated a hierarchical routing attention mechanism to focus on relevant cellular regions.
• Validated the model on TCGA datasets for binary and ternary TMB classification.

Main Results:

• CellMorphNet achieved high performance in binary classification (AUC 99.2%, F1-score 96.8%) and ternary classification (Accuracy 96.8%, F1-score 96.4%).
• The model demonstrated statistically significant superiority over existing methods (p<0.001).
• CellMorphNet effectively extracts relevant morphological features for TMB prediction.

Conclusions:

• CellMorphNet provides a highly accurate and efficient method for predicting TMB from histopathology images.
• This deep learning approach offers a cost-effective alternative to traditional sequencing methods.
• CellMorphNet has the potential to aid clinical decision-making in precision oncology and cancer immunotherapy.