Cancer is one of the leading causes of death worldwide and lung cancer is the second most commonly diagnosed cancer in men and women. Non-small-cell lung cancer (NSCLC) patients usually demonstrate different clinical courses and outcomes, even within the same tumor stage.
A recent study explores deep learning applications in medical imaging that allow for the automated quantification of radiographic components and the potential of improving patient stratification.
The study found that radiomics outperforms existing prognostic methods (and predictive accuracy) and can play a role in the prognostication of lung cancer patient survival. This could spare low mortality risk groups from adjuvant chemotherapy, saving money, but more importantly, reducing pain and suffering for patients.
HealthMyne’s platform can identify important radiomic features and alert clinicians when a patient’s information matches, so that better decisions can be made for the patient’s care, resulting in better outcomes.
For more information on HealthMyne’s application of radiomics in oncology, email us at [email protected].