Genomic Site Pattern Classification for Brain Tumor Diagnosis
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Solution Overview
Problem
Current diagnostic methods for cancer, particularly those involving brain tumors and spinal cord tumors, struggle to accurately classify tumors due to their complex histological features and varying molecular groups, leading to inadequate treatment planning and prognosis.
Innovation Solution
A computer-implemented method using a classification algorithm that analyzes the biological states of specific genomic DNA sites, such as gene sites, to classify cancer samples by comparing them to pre-determined biological state patterns, allowing for precise identification of tumor species.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histological classification methods are used for cancer diagnosis, then the diagnostic process can be performed with conventional tools, but the classification accuracy is insufficient due to complex and overlapping histological features
Solution Approach 1:
The patent replaces traditional mechanical/histological examination methods with computational algorithms that analyze biological state patterns of gene sites. The classification algorithm processes molecular data to identify cancer types, substituting the mechanical observation of tissue structures with computational pattern recognition, thereby achieving higher classification accuracy without requiring complex physical diagnostic equipment
Solution Approach 2:
The patent introduces biological state patterns as an intermediary representation between raw genomic data and cancer classification. Instead of directly analyzing complex histological features, the system converts gene site biological states into patterns that serve as mediators for the classification algorithm, simplifying the diagnostic process while maintaining high accuracy
2Measurement precision
If comprehensive analysis of all gene sites is performed to improve classification accuracy, then diagnostic precision increases, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and analyzes only the most relevant biological state patterns from the complete set of gene sites. By identifying and focusing on the critical patterns that distinguish cancer types, the system achieves high diagnostic precision without requiring comprehensive analysis of all genomic data, thereby reducing processing time and computational resource consumption
Solution Approach 2:
The patent segments the genomic analysis into distinct biological state patterns of gene sites, allowing the classification algorithm to process and evaluate only the most significant segments. This segmentation enables the system to maintain high diagnostic precision by focusing on key discriminatory patterns rather than processing every genomic detail equally
3Manufacturing precision
If detailed molecular group analysis is conducted to identify tumor species, then treatment planning accuracy improves, but the complexity of diagnostic work increases
Solution Approach 1:
The patent replaces complex manual diagnostic work with automated classification algorithms that process biological state patterns. The algorithm systematically evaluates molecular characteristics to identify tumor species, substituting human expert judgment with computational methods, thereby improving treatment planning accuracy while reducing the complexity burden on diagnostic workers
Data Source
AI summary
The present disclosure pertains to an in vitro method for the diagnostic classification of cancer based on the biological state of specific genomic sites. The disclosure provides a method that allows for a classification of a tumour sample obtained from a patient by analysing a multitude, preferably genome wide, collection of gene sites, combining the biological state of the analysed gene sites into a biological state pattern and comparing with pre-determined biological state patterns pertaining to different cancer types or tumour species. The disclosure is in particular useful for classifying cancer e.g. of the central nervous system, such as brain tumour samples and tumours of the spinal cord, since these are characterized by a large variety of distinct tumour species which have different prognostic values and require a developed treatment regime for each species in the clinical context. However, other cancers could similarly profit from the disclosure, for example sarcomas.