Deep Learning Framework for Nucleic Acid Yield Prediction
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Solution Overview
Problem
Current methods for assessing tumor biomarkers like PD-L1 and TILs in cancer diagnosis are limited by insufficient tissue samples and resource constraints, leading to inefficient analysis and potential waste of tissue samples, and existing deep learning approaches for histopathology image analysis are not practical due to high computational requirements for processing large images.
Innovation Solution
A deep learning framework is developed to predict the yield of nucleic acid from tumor cells in histopathology images, allowing for efficient identification of biomarkers and optimization of immunotherapy treatments by analyzing digital images of H&E slides and providing a dissection boundary for precise nucleic acid extraction, reducing the need for redundant tissue sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning approaches are used for histopathology image analysis, then measurement precision of tumor biomarkers is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the histopathology image into multiple tiles or regions, processing each region independently through the deep learning model. This divides the computationally intensive task of analyzing large whole-slide images into smaller, more manageable units, reducing memory requirements and enabling parallel processing while maintaining measurement precision for tumor biomarker assessment
Solution Approach 2:
The system performs preliminary filtering and preprocessing of histopathology images before applying the full deep learning analysis. This includes initial quality assessment, region-of-interest identification, and sample adequacy evaluation that prepares data in advance, reducing the computational burden during the main analysis phase while preserving measurement accuracy
2Reliability
If more tissue samples are collected for analysis, then reliability of biomarker assessment is improved, but loss of substance increases due to insufficient tissue samples
Solution Approach 1:
The system performs preliminary assessment of tissue sample adequacy using the deep learning model to predict whether a slide contains sufficient tumor cells for reliable biomarker assessment. This preliminary evaluation prevents unnecessary processing of inadequate samples, reducing tissue waste while ensuring that only samples with sufficient reliability potential are selected for further analysis
Solution Approach 2:
The system provides feedback on the predicted nucleic acid yield and sample adequacy to guide pathologists in selecting which slides to process further. This feedback mechanism enables optimization of tissue usage by directing analysis toward slides most likely to provide reliable biomarker data, thereby improving reliability while minimizing tissue sample waste
3Productivity
If comprehensive analysis of all slides is performed, then productivity of diagnostic process is improved, but loss of time increases due to processing requirements
Solution Approach 1:
The system segments the diagnostic workflow into prioritized stages, processing slides in order of predicted value and urgency. High-yield slides are processed first to deliver quick diagnostic insights, while lower-priority slides are processed subsequently or selectively, improving overall productivity without requiring all slides to be processed simultaneously, thus reducing time loss
Solution Approach 2:
The system performs partial analysis by focusing computational resources on a subset of slides most likely to contain diagnostically valuable information, rather than comprehensively analyzing every slide. This selective approach maintains high productivity by delivering sufficient diagnostic information from a representative sample while significantly reducing total processing time
Data Source
AI summary
A method for predicting an expected yield of nucleic acid from tumor cells within a dissection boundary on a hematoxylin and eosin (H&E) slide is provided.