Automated Tumor Bud Detection on H&E Slides via AI
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Solution Overview
Problem
Current methods for identifying tumor budding in tumors are subjective, time-consuming, and lack reproducibility, hindering their routine clinical use as a prognostic factor in cancer evaluation.
Innovation Solution
An automated image analysis method using artificial intelligence (AI) that learns from datasets of both pan-cytokeratin (AE1/3) and hematoxylin & eosin (H&E) stained slides, allowing for the detection and quantification of tumor budding on H&E slides alone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify tumor budding, then detection can be performed on H&E slides, but the process is time-consuming and subjective
Solution Approach 1:
The patent replaces manual mechanical evaluation by pathologists with an automated image analysis system using deep learning neural networks. The system processes H&E stained tissue images automatically, eliminating the time-consuming manual review process while maintaining detection accuracy through AI-based classification models trained on tumor budding characteristics.
Solution Approach 2:
The patent creates a digital copy of the manual assessment process through training neural networks on annotated image datasets. The AI model learns from expert pathologist annotations and replicates their detection capabilities, allowing rapid automated identification of tumor buds without requiring continuous manual intervention.
2Reliability
If manual methods are used to identify tumor budding, then H&E staining can be used, but reproducibility is poor
Solution Approach 1:
The patent replaces subjective manual assessment with an objective automated image analysis system. The deep learning model provides consistent, reproducible results by applying the same algorithmic criteria to all images, eliminating the variability inherent in manual evaluation while maintaining the ability to work with standard H&E stains.
Solution Approach 2:
The patent implements feedback mechanisms through iterative training of the neural network on annotated datasets. The model continuously refines its detection accuracy based on performance feedback, ensuring reliable and reproducible tumor budding identification that converges on consistent results across different applications.
3Measurement precision
If pan-cytokeratin staining is used to improve tumor budding detection, then detection accuracy improves, but cost and time increase
Solution Approach 1:
The patent uses the neural network as an intermediary that bridges the gap between standard H&E staining and the superior detection capabilities of immunohistochemical stains. The AI model processes H&E images and produces detection results comparable to pan-cytokeratin staining, eliminating the need for additional specialized stains while maintaining high detection accuracy.
Solution Approach 2:
The patent copies the enhanced detection capability of pan-cytokeratin staining into the neural network model. By training on data that captures the distinguishing features of tumor buds, the AI model replicates the superior detection performance without requiring the actual immunohistochemical staining process, thereby reducing cost and time.
Data Source
AI summary
Tumor budding (TB) is defined as a cluster of one to four tumor cells at the tumor invasive front. Though promising as a prognostic factor for colorectal cancer, its routine clinical use is hampered by high inter-and intra-observer disagreement on routine H&E staining. Therefore, automated methods are provided that minimize the inter-and intra-observer disagreement for tumor bud detection on images of H&E-stained tissue sections.