Clustering-based approach involves identifying hidden patterns in brain tumor cases by leveraging multi-modal data, e.g. imaging, genomic and clinical information, and incorporating deep learning-representations of magnetic resonance imaging scans to improve subtype classification (Hybrid patent)
	SHAIK M A; IRAM F
	2025-03-26
专利权人	UNIV SR (UYSR-Non-standard)
申请日期	2025-03-26
专利号	IN202541028229-A
成果简介	NOVELTY - Clustering-based approach involves identifying hidden patterns in brain tumor cases by leveraging multi-modal data, such as imaging, genomic, and clinical information by using unsupervised machine learning algorithms, such as k-means, hierarchical clustering and density-based spatial clustering, incorporating deep learning-based representations of magnetic resonance imaging (MRI) scans to improve subtype classification, and validating the identified clusters using clinical outcomes and survival analysis. (Abstract based on disclosure/claims). USE - Method of clustering-based approach is used for identifying distinct patterns and subtypes in brain tumor cases (claimed). ADVANTAGE - The clustering-based approach identifies distinct patterns and subtypes in brain tumor cases, which enables more precise treatment planning. The framework integrates advanced feature selection techniques to enhance clustering accuracy and interpretability. The system improves tumor subtype classification, and effectively distinguishes tumor subtypes, correlating with prognosis and therapeutic response patterns, which enables oncologists to make more precise and personalized treatment decisions based on distinct tumor characteristics, and improving patient outcomes, artificial intelligence (AI)-driven system enhances early detection of aggressive brain tumors, which allows for timely medical intervention and improved patient outcomes, reduces diagnostic ambiguity by integrating advanced clustering techniques leading to more accurate differentiation of tumor subtypes, improves survival rate predictions and treatment response assessments, which helps to tailor therapies to individual patients with greater precision, and optimizes hospital resource allocation by predicting treatment needs based on tumor classification to ensure efficient use of medical resources. The clustering framework is scalable and adaptable, which makes it suitable for real-world clinical applications in neuro-oncology, and provides a significant advancement in precision oncology, paving the way for AI-driven, data-centric, and personalized treatment strategies for brain tumor patients.
IPC 分类号	C12Q-001/6886 ; G06F-018/23213 ; G16H-020/10 ; G16H-050/20 ; G16H-050/70
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/13499
专题	中国科学院新疆生态与地理研究所
作者单位	UNIV SR (UYSR-Non-standard)
推荐引用方式 GB/T 7714	SHAIK M A,IRAM F. Clustering-based approach involves identifying hidden patterns in brain tumor cases by leveraging multi-modal data, e.g. imaging, genomic and clinical information, and incorporating deep learning-representations of magnetic resonance imaging scans to improve subtype classification (Hybrid patent). IN202541028229-A[P]. 2025.

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