Imaging Omics Analysis for Brain Tumors

Brain tumors grow in the cranial cavity and display aggressive growth behavior with a high rate of local recurrence. Accurate tumor grading is paramount as it significantly influences treatment strategies and prognosis. Conventional imaging is not sufficient to accurately distinguish between high-grade and low-grade brain tumors. Alfa Cytology provides specialized imaging omics services customized for brain tumor research.

Imaging Omics in Brain Tumors

Diagnostic imaging plays a pivotal role in the timely detection and continuous monitoring of brain tumors, aiding researchers in developing tailored treatment strategies. The integration of omics data and biomedical imaging has significantly propelled the revolution of the diagnosis, therapeutics and prognosis of brain tumors, introducing a novel paradigm for unraveling the complexities of brain tumors.

Fig.1 Various types of imaging data. (Antonelli L., et al., 2019)

Our Services

A histopathological diagnosis is an invasive approach limited by the specific location of brain tumors, which is unable to assess the heterogeneity of the entire tumor with a pathological diagnosis of local tissues. Alfa Cytology provides our clients with brain tumor imaging omics analysis services that can extract high-throughput quantitative features from animal medical images, capturing the diverse heterogeneity of brain tumors in vivo. By converting this information into high-dimensional data, we enable the exploration of correlations with histological features which reflect the underlying genetic mutations and characteristics of brain tumors.

High-throughput Feature Extraction Services

The extraction of high-throughput features forms the foundation of our imaging omics analysis. Our expertise encompasses the extraction of diverse high-throughput features including but not limited to the following contents.

Shape-based features which include metrics like volume, surface area and density, etc.
First-order statistics include parameters such as mean, entropy, skewness and kurtosis, etc.
Second-order features encompass metrics like the gray level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM), etc.
Higher-order features that entail the application of filters like Laplacian of Gaussian (LOG), wavelet transform (WT) and split dimensionally to identify repetitive or non-repetitive patterns in the image data.

Sample Preparation

Library Construction

Sequencing

Data Analysis

Other computational imaging features such as local binary pattern (LBP) and scale-invariant feature transform (SIFT) are also applied to image characterization for brain tumors.

Predictive Model Creation Services

Predictive models of clinical outcomes with specific features are built to meet the diverse needs of our clients. Our reliable modeling methods include supervised, semi-supervised and unsupervised learning. The approaches we adopt will be tailored to the type of samples you provide and your specific analysis requirements.

Methods	Description
Supervised learning	Support vector machines (SVM), lasso logistic regression and random forest.
Unsupervised learning	K-means algorithm, Gaussian mixture clustering, consensus clustering, etc.
Semi-supervised learning	Includes an unsupervised feature learning phase and a supervised model training phase.

Why Choose Us?

Customized
Solutions

In-depth
Analysis

Strong
Expertise

Short
Turnaround

Alfa Cytology helps researchers develop novel diagnosis strategies for brain tumors by offering valuable imaging omics analysis services. We excel at extracting numerous quantitative features from intricate brain imaging arrays. Please don't hesitate to reach out to our staff to discuss and submit your analysis requirements.

Reference

Antonelli L.; et al. (2019). Integrating imaging and omics data: a review[J]. Biomedical Signal Processing and Control. 52, 264-280.

All of our services and products are intended for preclinical research use only and cannot be used to diagnose, treat or manage patients.