By Rose Higgins
The promise of personalized medicine, with its potential for treatments tailored to individual patients’ genomic footprints to generate better outcomes, has long tantalized the life sciences industry.
Now, advanced imaging analytics and the extraction of high-dimensional data from medical images, called radiomics, is emerging as the other side of the personalization coin. By adding an individual patients’ tumor phenotypic (structural) information coupled with the patients’ genetic data, drug developers can create more precise therapies.
Radiomics enables life sciences companies to conduct clinical trials with greater accuracy and speed by leveraging insights about the characteristics of lesions and tumors that are not discernible from traditional reading methods of standard imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) or positron emission tomography (PET) scans.
For drug developers, radiomics can contribute value in delivering the last mile in personalized medicine. The combination of genomic data, plus the type of phenotypic data radiomics derives from images, provides even greater adaptability to clinical trial design by mapping the effectiveness of drugs, showing whether treatments under trial are more effective for patients with shared characteristics.
By using artificial intelligence (AI) to discern and compute data from medical images, radiomics enables drug developers to profile a patient, tumor, and therapy across multiple dimensions to find patterns and similarities that would otherwise be unobtainable.
The more data that is captured and analyzed as the clinical trial proceeds, the more precise and accurate the conclusions the trial will yield. Radiomic data is a positive addition to this dataset. By showing previously hidden data and patterns within a lesion, this data can be an early indicator of the effectiveness of the treatment. Linking tumor phenotype and mechanism of action of a novel drug helps address tumor aggressiveness, metastatic potential and tumor response to therapy. In addition, researchers can leverage radiomics to gain a greater understanding of the characteristics shared by those for whom the treatment has been most effective, and those for whom it has had little to no effect.
Although the life sciences industry has made great progress in its pursuit of fulfilling personalized medicine’s promise, there is no question that work remains to be done. Radiomics represents the missing link that adds individualized patients’ phenotypic data to their genomic information, enabling drug developers to advance more targeted, precise therapies.