Machine Learning CTC Identification via Nuclear Ploidy Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying and analyzing circulating tumor cells (CTCs) are limited by their inability to accurately distinguish rare CTCs, often resulting in false positives, damage to cells during preparation, and failure to preserve RNA, leading to difficulties in genetic and transcriptome analysis.
Innovation Solution
A computer-implemented machine learning system combined with a reagent system that includes specific fixing buffers and blocking agents to preserve cell integrity and reduce non-specific staining, allowing for accurate identification and classification of CTCs by analyzing pixel intensities and ploidy status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional physical properties and cell-surface markers are used to identify CTCs, then identification can be performed, but false positives increase and relevant pathogenic CTCs are missed
Solution Approach 1:
The patent transitions from using conventional physical properties and cell-surface markers to analyzing nuclear ploidy status as a new parameter for CTC identification. This parameter change enables more accurate distinction between CTCs and white blood cells, resolving the contradiction between identification reliability and detection precision
Solution Approach 2:
The patent replaces manual identification methods based on physical properties with an automated machine learning system that analyzes nuclear ploidy status. This substitution improves both the reliability and precision of CTC detection by eliminating human error and enabling consistent application of complex classification criteria
2Stability of the object's composition
If cell preparation methods using fixatives are applied, then cells can be preserved for analysis, but cell damage occurs and RNA integrity is lost
Solution Approach 1:
The patent performs ploidy status analysis on fresh, unfixed cells before any fixation or processing steps. This preliminary action allows CTC identification to occur while cells are still intact and RNA is preserved, eliminating the harmful effects of fixatives while maintaining cell preservation for subsequent analysis
Solution Approach 2:
The patent extracts the identification function to occur before fixation by analyzing nuclear ploidy status in fresh cells. This separation of identification from fixation processes allows cell preservation and RNA integrity to be maintained while still enabling CTC detection
3Measurement precision
If blocking buffers are used to improve staining specificity, then background staining decreases, but staining of rare cells and multi-antibody stains becomes inadequate
Solution Approach 1:
The patent extracts the identification function from staining-based methods to ploidy status analysis performed on fresh cells. This removal of dependence on blocking buffers and complex staining protocols simultaneously improves staining specificity for rare cells and enhances adaptability for multi-antibody stains by eliminating the limiting factor
Data Source
AI summary
Disclosed herein are systems and methods of identifying and classifying rare cells. A machine learning system comprising learning layers is trained to develop algorithms to rapidly identify and classify unknown biological samples on an image. The algorithms identify and classify regions of cells, cell types, and cell subtypes.