![]() ![]() ![]() By extracting features from imaging data and applying predictive models, researchers can visualize disease characteristics not visible to the human eye. A subdiscipline of machine learning, radiomics involves extracting high-dimensional data from images such as CT scans to better understand tumor biology and behavior. ![]() Researchers developed a radiomic nomogram, representing the relations between key variables, as a new method of calculating risk beyond patient-specific histopathologic and clinical variables. The newly published research demonstrates that use of the persistent homology (PHOM) score of a patient’s CT scan, which is information derived from the risk calculator, is the strongest predictor of overall survival of any standard clinical measure. Risk calculators are crucial for treating NSCLC, due to the high variability in survival rates. This work was recently published in JCO Clinical Cancer Informatics. Researchers at Cleveland Clinic have developed a risk calculator to predict overall survival of patients with non-small cell lung cancer (NSCLC) after stereotactic body radiation therapy (SBRT), a type of external radiation therapy that uses special equipment to position the patient and precisely deliver radiation to a tumor. ![]()
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