Summary:
UCLA researchers in the Department of Radiological Sciences have developed a novel computational pipeline to predict the likelihood of cancer based on subtle changes observed on chronological medical images using deep learning algorithms.
Background:
Advances in biotechnology have generated large quantities of detailed information on patients’ genetic risk for cancer such as the presence of cancer-promoting germline mutations and highly sensitive blood tests that can detect tumor DNA circulating in the blood. While these developments have the potential to improve the detection of cancer at earlier stages at the microscopic level, medical imaging remains an integral player in localizing and characterizing observable changes at the macroscopic level. Opportunities exist to utilize advanced machine learning approaches combined with information from advanced biotechnology to predict the formation of cancer before a distinct abnormality is observed. The ability to generate and present an individualized probability map for a given patient will be valuable for treatment planning and decision making.
Innovation:
UCLA researchers in the Department of Radiological Sciences have developed a novel computer-vision based approach for analyzing serial screening and diagnostic medical images to predict the risk of cancer onset using deep learning algorithms. The developed system incorporates clinical, imaging, and genetic data from patients with known genetic risk for cancer, compares changes in the microenvironmental tissue states that are observed in serial imaging scans, and derives the probability of cancer formation based on an assessment of these changes. The detection of subtle changes in tissue imaging features can increase the radiologist’s diagnostic certainty in identifying potential cancers earlier and informing decisions about treatment.
Patent:
System and Method for Tissue Classification Using Quantitative Image Analysis of Serial Scans
Potential Applications:
• Cancer diagnosis, monitoring, and prevention
• Monitor cancer development
Advantages:
• Accurate detection
• Distinguishes static and dynamic tissue abnormalities in cancer microenvironment observed in serial medical images
Development to Date:
Computational pipeline developed; patent application filed.