In vivo evaluation of complex polyps with endoscopic optical coherence tomography and deep learning during routine colonoscopy: a feasibility study.

Journal: Scientific reports
Published Date:

Abstract

Standard-of-care (SoC) imaging for assessing colorectal polyps during colonoscopy, based on white-light colonoscopy (WLC) and narrow-band imaging (NBI), does not have sufficient accuracy to assess the invasion depth of complex polyps non-invasively during colonoscopy. We aimed to evaluate the feasibility of a custom endoscopic optical coherence tomography (OCT) probe for assessing colorectal polyps during routine colonoscopy. Patients referred for endoscopic treatment of large colorectal polyps were enrolled in this pilot clinical study, which used a side-viewing OCT catheter developed for use with an adult colonoscope. OCT images of polyps were captured during colonoscopy immediately before SoC treatment. A deep learning model was trained to differentiate benign from deeply invasive lesions for real-time diagnosis. 35 polyps from 32 patients were included. OCT imaging added on average 3:40 min (range 1:54-8:20) to the total procedure time. No complications due to OCT were observed. OCT revealed distinct subsurface tissue structures that correlated with histological findings, including tubular adenoma (n = 20), tubulovillous adenoma (n = 10), sessile serrated polyps (n = 3), and invasive cancer (n = 2). The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.984 (95%CI 0.972-0.996) and Cohen's kappa of 0.845 (95%CI 0.774-0.915) when compared to gold standard histopathology. OCT is feasible and safe for polyp assessment during routine colonoscopy. When combined with deep learning, OCT offers clinicians increase confidence in identifying deeply invasive cancers, potentially improving clinical decision-making. Compared to previous studies, ours offers a nuanced comparison between not just benign and malignant lesions, but across multiple histological subtypes of polyps.

Authors

  • Haolin Nie
    Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
  • Hongbo Luo
    From the Department of Biomedical Engineering (X.L., K.M.S.U., S.K., E.A., G.Y., Q.Z.), Division of Surgery, Barnes-Jewish Hospital (W.C., S.H., M.M.), and Department of Electrical and System Engineering (H.L.), Washington University in St. Louis, 1 Brookings Dr, Mail Box 1097, St Louis, MO 63130; Department of Pathology (D.C.) and Mallinckrodt Institute of Radiology (A.S., Q.Z.), Washington University School of Medicine, St Louis, Mo.
  • Vladimir Lamm
    Division of Gastroenterology, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
  • Shuying Li
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
  • Sanskar Thakur
    Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
  • Chao Zhou
    Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania.
  • Thomas Hollander
    Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
  • Daniel Cho
    Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
  • Erika Sloan
    Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
  • Jingxia Liu
    Division of Public Health Science, Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, 63110, USA.
  • Pooja Navale
    Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Ahmad N Bazarbashi
    Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
  • Juan Pablo Reyes Genere
    Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
  • Vladimir M Kushnir
    Division of Gastroenterology, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
  • Quing Zhu
    Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, U-2157, 06269 CT, Storrs, USA.