  Herein we propose a deep learning-based approach for the prediction of lung
lesion response based on radiomic features extracted from clinical CT scans of
patients in non-small cell lung cancer trials. The approach starts with the
classification of lung lesions from the set of primary and metastatic lesions
at various anatomic locations. Focusing on the lung lesions, we perform
automatic segmentation to extract their 3D volumes. Radiomic features are then
extracted from the lesion on the pre-treatment scan and the first follow-up
scan to predict which lesions will shrink at least 30% in diameter during
treatment (either Pembrolizumab or combinations of chemotherapy and
Pembrolizumab), which is defined as a partial response by the Response
Evaluation Criteria In Solid Tumors (RECIST) guidelines. A 5-fold cross
validation on the training set led to an AUC of 0.84 +/- 0.03, and the
prediction on the testing dataset reached AUC of 0.73 +/- 0.02 for the outcome
of 30% diameter shrinkage.
