Login / Signup

Diagnostic accuracy of optical coherence tomography for the identification of in-stent fibroatheroma following stent implantation: an ex vivo histological validation study.

Hiroki ShibutaniKenichi FujiiRika KawakamiTakahiro ImanakaKenji KawaiSatoshi TsujimotoKoichiro MatsumuraMunemitsu OtagakiShun MorishitaKenta HashimotoHiroyuki HaoSeiichi HirotaIchiro Shiojima
Published in: The international journal of cardiovascular imaging (2021)
The accurate identification of in-stent fibroatheroma by in vivo imaging is clinically important to preventing the late catch-up phenomenon after stent deployment. This study investigated the diagnostic accuracy of optical coherence tomography (OCT) for the detection of "in-stent fibroatheroma" following stent implantation. Fifty stented coronary arteries from the 31 autopsy hearts were examined to compare OCT and histological image findings. A histological in-stent fibroatheroma was defined as a neointima containing an acellular necrotic core generated by macrophage infiltration. OCT-derived in-stent fibroatheroma comprised a heterogeneous pattern with an invisible stent strut behind the low-signal-intensity region. A total of 122 matched OCT and histology cross-sections were evaluated. Using histological findings as the gold standard, the sensitivity, specificity, positive predictive value, and negative predictive value for OCT-derived in-stent fibroatheroma were 100%, 99%, 80%, and 100%, respectively. The only histological finding underlying the false-positive diagnosis of OCT-derived in-stent fibroatheroma was foam cell accumulation without a necrotic core on the neointimal surface. No false-negative diagnosis of OCT for in-stent fibroatheroma was apparent in this analysis. This study demonstrated the potential capability of OCT based on stent strut visualization behind low-signal-intensity regions to discriminate in-stent fibroatheroma from other neointimal tissues.
Keyphrases
  • optical coherence tomography
  • high resolution
  • gene expression
  • coronary artery disease
  • heart failure
  • machine learning
  • cell therapy
  • climate change
  • photodynamic therapy
  • aortic valve
  • data analysis