Quantifying the tumor-immune ecosystem in non-small cell lung cancer (nsclc) to identify clinical biomarkers of therapy response

A method of processing medical image data to predict disease progression in non-small cell lung cancer (NSCLC) patients, the method comprising:

• receiving a multiplexed tissue image comprising a plurality of cells stained for one or more markers;
• evaluating the multiplexed tissue image using a machine learning classifier model, wherein the machine learning classifier model classifies each of the plurality of cells as either stable or progressive;
• and
• predicting whether a patient's NSCLC will progress based on the evaluation of the multiplexed tissue image using the machine learning classifier model.

A system for processing medical image data related to non-small cell lung cancer (NSCLC), the system comprising:

• at least one processor;
• and
• memory having instructions stored thereon that, when executed by the at least one processor, cause the system to perform operations comprising:
• receiving a multiplexed tissue image comprising a plurality of cells stained for one or more markers;
• evaluating the multiplexed tissue image using one of a support vector machine (SVM) classifier or a boosted regression tree (BRT), wherein the SVM classifier classifies each of the plurality of cells as either stable or progressive, and wherein the BRT outputs a probability of NSCLC progression for each of a plurality of quadrants parsed from the multiplexed tissue image;
• and
• predicting whether a patient's NSCLC will progress based on the evaluation of the multiplexed tissue image using the SVM classifier or the BRT.