  BACKGROUND Careful evaluation of the risk of stent under-expansions before
the intervention will aid treatment planning, including the application of a
pre-stent plaque modification strategy.
  OBJECTIVES It remains challenging to achieve a proper stent expansion in the
presence of severely calcified coronary lesions. Building on our work in deep
learning segmentation, we created an automated machine learning approach that
uses 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 image
data were obtained from 110 coronary lesions. Lumen and calcifications in
pre-stent images were segmented using deep learning, and numerous features per
lesion were extracted. We analyzed stent expansion along the lesion, enabling
frame, segmental, and whole-lesion analyses. We trained regression models to
predict 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 lumen
and calcification to train a Gaussian regression model for a segmental analysis
over 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 at
risk of stent under-expansion. Results suggest that the use of pre-stent images
can inform physicians of the need to apply plaque modification approaches.
