At OracleBio, we have extensive experience in performing quantitative analysis of tissue samples in clinical research. We perform analysis in a detailed and unbiased manner across digital whole slide tissue images to ensure data is fully representative of the staining and features present.
OracleBio can provide expert pathologist input to support algorithm development, offer guidance on specific tissue region of interest selection and to lead annotations on clinical tissue images. Image analysis algorithms are verified and can be locked to ensure consistent, quantification across multiple batches of clinical samples.
Quality Management Framework
We have established a Quality Management Framework that provides a structured environment and associated processes to ensure that image analysis and reporting activities are performed to SOPs and predefined SOWs.
Consultative Approach to Quantitative Image Analysis
Our workflow is designed to ensure robust quantitative image output with complete progress transparency for the Client:
- Quality control points at image QC, tissue annoation and algorithm development stages.
- Image sharing capabilities for both Indica Halo and Visiopharm software
- Validation steps to ensure algorithm accuracy
- Staged approach to ensure quality is maintained between seperate clinical batches of images
Quantification of specific tissue markers, mechanistic readouts or intercellular signalling proteins can predict drug response or disease status within specific patient populations. Our services provide detailed quantification of changes in marker expression across whole tissues or within specific regions of interest to better guide choice of drug dose or clinical population for trial inclusion.
Benefits of Working with OracleBio for your Clinical Studies:
- Data you can trust – We have thorough quality control procedures in place throughout our workflow to ensure the data we provide is of a high-quality standard
- Working alongside Clinical Project managers – experienced with ensuring data parity between time separated batches of clinical samples