Description:
- Diagnostics: Renal tumors
Abstract
Researchers at USC have developed a radiomics model that calculates a radiomics score to detect the probability of cancer from clinical imaging. This was successfully demonstrated with patients that presented an unknown kidney mass. USC researchers found that the radiomics score was significantly associated with final pathology on both univariate and multivariate analysis. In the future, they anticipate that the model can combine radiologic and clinical factors (such as clinical history and tumor characteristics) to predict malignancy with a high degree of accuracy.
Benefit
- The radiomics model acts as a digital biopsy to prevent unnecessary surgery for diagnosing suspected kidney tumors
- Accurately differentiates true cancers from benign kidney masses
Market Application
Up to 40% of all patients in the U.S. (120 million people) have abnormal kidney growth, but only 0.05% will have true kidney cancer. About 1 in 4 patients with a suspected kidney tumor undergo unnecessary surgery to diagnose whether the mass seen in the CT scan is cancerous. Moreover, the available strategies at solving this problem, such as pre-operative biopsies or costly nuclear medicine imaging, are not reliable alternatives to surgery. Given the risks and the high costs of surgery, there needs to be a more efficient, non-invasive tool to detect benign vs. malignant kidney masses.
Publications
In progress
Other
Stage of Development: Proof of concept demonstrated from data gathered from 2007-2018 in 684 patients
IP Status: US Patent Pending 18/286,918