Brain glioma molecular marker nondestructive prediction method and prediction system based on radiomics

A technology of molecular markers and radiomics, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as lack of research on analysis and understanding

Active Publication Date: 2017-05-17
FUDAN UNIV
View PDF4 Cites 52 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Gene expression analysis and understanding of LGG is relatively understudied compared to glioblastoma (GBM, WHO grade IV)

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Brain glioma molecular marker nondestructive prediction method and prediction system based on radiomics
  • Brain glioma molecular marker nondestructive prediction method and prediction system based on radiomics
  • Brain glioma molecular marker nondestructive prediction method and prediction system based on radiomics

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0060] The following are the specific implementation steps of the entire algorithm:

[0061] 1. Firstly, perform operations such as brain removal and grayscale normalization on the original image, manually label 30 cases of 240 pieces of unbiased images as the training set of CNN, and divide the image into 32*32 small blocks and send them to the network for training .

[0062] 2. Use figure 2 The CNN shown segments the image, and subsequently adjusts the segmentation result with a CRF energy random field.

[0063] 3. Map the segmented tumors to the standard brain atlas MN152 with SPM12, superimpose 76 cases of IDH1 mutation and 34 cases of IDH1 wild-type tumors on the standard brain atlas, divide the superposition results into AAL116 partitions, and count the two Distribution of tumoroids on 116 partitions as 116 location features.

[0064] 4. A total of 555 grayscale, shape, texture, and wavelet features shown in Table 1 were extracted, plus 116 positional features, and a...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention belongs to the technical field of computer-aided diagnosis, and specifically relates to a brain glioma molecular marker nondestructive prediction method and a prediction system based on radiomics. The method comprises the following steps: adopting a three-dimensional magnetic resonance image automatic segmentation method based on a convolution neural network; registering a tumor obtained from segmentation to a standard brain atlas, and acquiring 116 position features of tumor distribution; getting 21 gray features, 15 shape features and 39 texture features through calculation; carrying out three-dimensional wavelet decomposition on the gray features and the texture features to get 480 wavelet features of eight sub-bands; acquiring 671 high-throughput features from the three-dimensional T2-Flair magnetic resonance image of each case; using a feature screening strategy combining p-value screening and a genetic algorithm to get 110 features highly associated with IDH1; and using a support vector machine and an AdaBoost classifier to get a classification of which the IDH1 prediction accuracy is 80%. As a novel method of radiomics, the method provides a nondestructive prediction scheme of important molecular markers for clinical diagnosis of gliomas.

Description

technical field [0001] The invention belongs to the technical field of computer-aided diagnosis, in particular to a radiomics-based nondestructive prediction method and a prediction system for molecular markers of brain glioma. Background technique [0002] Glioma is the most common brain malignancy, and about 30% of them are low-grade gliomas (LGG, WHO grades I and II). Although low-grade gliomas have a relatively good prognosis, almost all low-grade gliomas will develop into high-grade gliomas with high mortality. Compared with glioblastoma (GBM, WHO grade IV), research on gene expression analysis and understanding of LGG is relatively lacking. [0003] IDH1 (isocitrate dehydrogenase 1) has significant diagnostic, prognostic, and predictive values, and is the most important molecular marker in glioma [1]. Most lower-grade gliomas (WHO grades II and III) and secondary GBMs have IDH1 mutations, while IDH1 mutations are rarely observed in primary GBMs; IDH1 is independent o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/73
CPCG06T7/0012G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30016G06T2207/30096
Inventor 余锦华史之峰李泽榉汪源源陈亮毛颖
Owner FUDAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap