3D Optical Tomography for Lung Cancer Detection
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Solution Overview
Problem
Current lung cancer detection methods, such as traditional cytologic methods, suffer from poor sensitivity and specificity, making early detection challenging and potentially increasing healthcare costs and patient risks due to invasive procedures, while also requiring the presence of cancer cells in patient samples which are rare and difficult to detect.
Innovation Solution
A method using 3D optical tomography to analyze subtle changes in cellular morphology imparted to non-cancer cells through the cancer field effect, allowing for the development of classifiers to discriminate between normal and cancer cells without the need for actual cancer cells in the specimen, utilizing the Cell-CT platform for imaging and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cytologic methods are used for lung cancer detection, then the procedure can be performed with existing technology, but the sensitivity is poor (only 40-60% positive results)
Solution Approach 1:
The patent replaces traditional mechanical cytologic examination with optical tomography imaging technology. The system uses optical fields to capture 3D cellular morphology data, substituting the mechanical/visual inspection method with an optical measurement system that provides superior detection sensitivity while maintaining operational simplicity through automated image analysis.
Solution Approach 2:
The patent transitions from 2D cytologic slides to 3D optical tomography images. By adding the depth dimension, the system captures complete cellular morphology information including nuclear and cytoplasmic structures in three dimensions, enabling detection of subtle malignant changes that are invisible in traditional 2D preparations.
2Reliability
If cancer cells are targeted for detection, then direct identification is possible, but cancer cells are rare and difficult to detect in patient samples
Solution Approach 1:
The patent uses non-cancer cells as intermediaries to detect cancer presence. Instead of directly targeting rare cancer cells, the system analyzes morphological changes in abundant non-cancer cells that are influenced by the cancer field effect, using these intermediary cells as proxies to infer cancer presence with high accuracy.
Solution Approach 2:
The patent creates a copy of the cancer state through morphological analysis of non-cancer cells. By capturing and analyzing the morphological features of non-cancer cells that have been altered by the cancer microenvironment, the system produces a detectable signal that copies the presence of cancer without requiring direct observation of cancer cells.
3Reliability
If invasive procedures are performed for detection, then diagnostic accuracy may improve, but patient health risks increase and healthcare costs rise
Solution Approach 1:
The patent replaces invasive mechanical biopsy procedures with non-invasive optical tomography imaging. The system uses optical fields to analyze cellular morphology in sputum or other non-invasive samples, eliminating the need for tissue biopsy while maintaining high diagnostic accuracy and reducing patient risk.
4Reliability
If 3D optical tomography is used to analyze cellular morphology, then sensitivity and specificity increase to 92% and 95%, but the system complexity increases
Solution Approach 1:
The patent segments the complex 3D optical tomography system into modular functional components: optical imaging module, 3D reconstruction module, morphometric analysis module, and classification module. This segmentation allows each component to be optimized and managed independently, reducing overall system complexity while maintaining high detection specificity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies detection, increases sensitivity and specificity, and expedites processing by identifying abnormal cells in sputum samples with high accuracy, achieving 92% sensitivity and 95% specificity for lung cancer detection, and allows for the analysis of subtle changes in cellular morphology without requiring cancer cells, thus overcoming the limitations of traditional methods.
Implementation Method 1
imaging cells to produce 3D cell images
Implementation Method 2
optical tomography system
Data Source
AI summary
A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.