Predicting disease recurrence following trimodality therapy in non-small cell lung cancer using computed tomography derived radiomic features and clinico-pathologic features

An apparatus for generating a recommendation that a patient receive trimodality therapy (TMT) for non-small cell lung cancer (NSCLC), the apparatus comprising:

• a processor;
• a memory configured to store a digitized baseline non-contrast computed tomography (CT) image of a region of tissue (ROT) demonstrating NSCLC;
• an input/output (I/O) interface;
• a set of circuits;
• and
• an interface that connects the processor, the memory, the I/O interface, and the set of circuits, the set of circuits comprising:
• an image acquisition circuit configured to access a digitized baseline non-contrast CT image of an ROT demonstrating NSCLC, where the baseline CT image includes a tumoral region and a peritumoral region, the baseline CT image having a plurality of pixels, a pixel having an intensity;
• a tumoral radiomics circuit configured to extract a set of tumoral radiomic features from the tumoral region, where the set of tumoral radiomic features includes an area of lesion feature and a Gabor feature;
• a peritumoral radiomics circuit configured to extract a set of peritumoral radiomic features from the peritumoral region, where the set of peritumoral radiomic features includes a Laws feature and a Law_Laplacian feature;
• a clinico-pathologic feature circuit configured to extract a set of clinico-pathologic features from the ROT, where the set of clinico-pathologic features includes a lymphovascular invasion (LVI) feature and a percent viable tumor feature;
• a TMT recommendation circuit configured to:
• generate a probability that the ROT will experience post-TMT major pathologic response (MPR) based on the set of tumoral features, the set of peritumoral features, and the set of clinico-pathologic features;
• and
• generate a recommendation for TMT based on the probability;
• and
• a display circuit configured to display the recommendation.

A non-transitory computer-readable storage device storing computer executable instructions that when executed by a computer control the computer to perform a method for predicting outcome of trimodality therapy (TMT), the method comprising:

• accessing a pre-neoadjuvant chemotherapy, pre-surgery, baseline computed tomography (CT) image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), the region of tissue including a tumor region and a peritumoral region, the baseline CT image having a plurality of pixels, a pixel having an intensity;
• extracting a combined radiomic risk signature from the baseline CT image, where the combined radiomic risk signature includes at least one radiomic feature extracted from the tumoral region, at least one radiomic feature extracted from the peritumoral region, and at least one clinico-pathologic feature;
• providing the combined radiomic risk signature to a machine learning classifier, where the machine learning classifier computes a major pathologic response (MPR) prediction based on the combined radiomic risk signature, computing a risk score based on the combined radiomic risk signature, that is predictive of a high risk or low risk of disease free survival (DFS) and generating a DFS prediction based on the risk score, or generating an overall survival (OS) prediction based on the combined radiomic risk signature;
• receiving, from the machine learning classifier, the MPR prediction;
• generating a TMT outcome prediction based on the MPR prediction, the DFS prediction, or the OS prediction;
• and
• displaying the TMT outcome prediction and at least one of the MPR prediction, the DFS prediction, or the OS prediction.