Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant...

Area of Science:

• Computational pathology
• Artificial intelligence in oncology
• Digital pathology image analysis

Background:

• Neoadjuvant therapy followed by surgery is standard for locally advanced esophageal squamous cell carcinoma (ESCC).
• Accurate pathological response assessment is crucial for evaluating treatment efficacy but can be laborious and inconsistent.
• There is a need for efficient and reliable methods for pathological response assessment in ESCC.

Purpose of the Study:

• To develop an interpretable deep-learning model for efficient pathological response assessment in ESCC.
• To evaluate the model's performance against pathologist consensus.
• To utilize spatial heatmaps for model explanation and visualization.

Main Methods:

• Retrospective analysis of 337 ESCC resection specimens (Cohort 1) and 114 (External Cohort 2).
• Whole slide images (WSIs) were used, with varying scanners to test model robustness.
• A deep-learning model with knowledge distillation was developed to classify tumor viability and estimate residual tumor percentage, generating spatial heatmaps.

Main Results:

• The deep-learning model achieved high concordance with pathologist consensus (R^2 of 0.8437), comparable to senior pathologists.
• The model's performance surpassed junior pathologists, demonstrating its potential.
• Visualizations localized residual viable tumor, augmenting microscopic assessment.

Conclusions:

• Deep learning shows significant potential for assisting pathological response assessment in ESCC.
• Interpretable AI, through spatial heatmaps and patch examples, can build clinical trust and facilitate adoption.
• Integrating computational pathology can enhance efficiency, consistency, and precision in oncology treatment decisions.