Predictive model uses radiomics from CT scans to reduce overtreatment, save resources

April 18, 2019 | Article

A study published in the Journal of the American College of Radiology outlines how a model based on radiomic features extracted from CT scans can help predict which ground glass nodule (GGN) cases require surgery and may reduce overtreatment of the condition. The study found that combining clinical information and the radiomics nomogram achieved the best predictive ability and calibration in both the training set and validation set.

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HealthMyne’s Quantitative Imaging Decision Support (QIDS) Platform is the only commercially available system that can extract radiomic features from CT scans within the standard clinical workflow. The Platform will be able to inform clinicians immediately of matches to signatures like these potentially saving patients from the danger, pain and expense of unnecessary procedures.