Cancer Scoring Tool With Confidence Indicator
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Solution Overview
Problem
Current cancer detection and diagnosis methods lack a fast and reliable computerized system for analyzing tissue slides to generate accurate scores indicative of cancer severity, treatment success probability, or cancer subtype, with limited confidence in the accuracy of these scores.
Innovation Solution
A novel cancer scoring tool that receives digital images of tissue, identifies cell objects, generates a severity score, and provides a confidence number by using false negative and false positive rate values to iteratively adjust and refine the scoring process, displaying both the score and confidence level alongside the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a computerized method is used to analyze tissue slides for cancer detection, then the speed and efficiency of analysis is improved, but the reliability and accuracy of the generated scores is insufficient
Solution Approach 1:
The system implements feedback by using false negative and false positive rate values to iteratively adjust decision thresholds. The confidence number provides feedback on the reliability of each cancer score, allowing users to assess when additional review or verification is needed. This creates a closed-loop system that continuously improves accuracy while maintaining computational efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-calculating false negative and false positive rate values from training data before actual cancer scoring. These pre-computed statistical parameters are then used to guide the scoring process, enabling fast analysis while incorporating lessons from historical data to improve accuracy.
2Reliability
If traditional manual analysis methods are used, then the accuracy of cancer scoring can be maintained through expert review, but the time required for diagnosis increases significantly
Solution Approach 1:
The system introduces a confidence number as an intermediary metric that bridges automated scoring and expert review. This confidence indicator, derived from false rate values, helps determine when automated scores are sufficiently reliable and when they require additional expert verification, optimizing the allocation of expert time while maintaining accuracy.
Solution Approach 2:
The system applies partial expert review by using automated analysis for the majority of cases and reserving expert time only for cases with low confidence scores. This partial application of expert review maintains high accuracy while dramatically reducing overall diagnosis time compared to universal manual review.
3Speed
If the cancer scoring system uses simple threshold-based classification, then the computational speed is improved, but the precision and reliability of the scores deteriorates
Solution Approach 1:
The system changes parameters by incorporating false negative and false positive rate values as additional dimensions to the scoring process. Instead of relying solely on simple threshold classification, the system uses these statistical parameters to dynamically adjust decision boundaries and generate confidence numbers, improving precision while maintaining computational efficiency through vectorized operations.
4Device complexity
If the system generates only a single cancer score without confidence measures, then the simplicity of the output is maintained, but the usefulness for clinical decision-making is reduced
Solution Approach 1:
The system segments the output into two distinct components: the cancer score itself and the confidence number. This segmentation allows the main cancer score to remain simple and interpretable while the confidence number provides additional reliability information. The two-component output structure maintains simplicity for primary decision-making while preserving critical information for risk assessment.
Data Source
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AI summary
A novel cancer scoring tool not only generates a score, but it also generates and confidence number. The tool receives a digital image of tissue of a patient. The tool identifies cell objects in the image and from that determines a first score. The magnitude of this first score is indicative of the severity of cancer in the tissue of the patient. The tool uses an overall false negative rate value and an overall false positive rate value to generate a set of second scores. The rate values are determined from training information. From the second scores, the tool determines the confidence number. The confidence number indicates the confidence the tool has in the first score being correct. The first score and an indication of the confidence number and the digital image are all displayed together on the display of the tool.