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BACKGROUND Careful evaluation of the risk of stent under-expansions beforethe intervention will aid treatment planning, including the application of apre-stent plaque modification strategy.  OBJECTIVES It remains challenging to achieve a proper stent expansion in thepresence of severely calcified coronary lesions. Building on our work in deeplearning segmentation, we created an automated machine learning approach thatuses lesion attributes to predict stent under-expansion from pre-stent images,suggesting the need for plaque modification.  METHODS Pre- and post-stent intravascular optical coherence tomography imagedata were obtained from 110 coronary lesions. Lumen and calcifications inpre-stent images were segmented using deep learning, and numerous features perlesion were extracted. We analyzed stent expansion along the lesion, enablingframe, segmental, and whole-lesion analyses. We trained regression models topredict the poststent lumen area and then to compute the stent expansion index(SEI). Stents with an SEI < or >/= 80% were classified as "under-expanded" and"well-expanded," respectively.  RESULTS Best performance (root-mean-square-error = 0.04+/-0.02 mm2, r =0.94+/-0.04, p < 0.0001) was achieved when we used features from both the lumenand calcification to train a Gaussian regression model for a segmental analysisover a segment length of 31 frames. Under-expansion classification results(AUC=0.85+/-0.02) were significantly improved over other approaches.  CONCLUSIONS We used calcifications and lumen features to identify lesions atrisk of stent under-expansion. Results suggest that the use of pre-stent imagescan inform physicians of the need to apply plaque modification approaches.

Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography