Artificial intelligence model to predict slow progression for advanced non-small cell lung cancer (aNSCLC) patients receiving second-line therapies

There are ongoing efforts to understand and predict exceptional response to existing cancer therapies, but few clinical characteristics of these patients are known. We trained a machine learning model using the ConcertAI database of oncology EMR data that includes clinical data from CancerLinQ Discovery to predict slow progression, a proxy for exceptional response, in aNSCLC in the second line setting. The study concluded that machine learning and real world-data provide promising results in predicting slow progression in aNSCLC and may be useful in discovering novel drivers of favorable response.