Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning.

Journal: Journal of biophotonics
Published Date: Feb 28, 2022

Abstract

Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent advances in convolutional neural networks (CNN) have enabled application in ophthalmology, cardiology, and gastroenterology malignancy detection with high sensitivity and specificity. Here, we describe a miniaturized OCT catheter and a residual neural network (ResNet)-based deep learning model manufactured and trained to perform automatic image processing and real-time diagnosis of the OCT images. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~7 μm, and an axial resolution of ~6 μm. A customized ResNet is utilized to classify OCT catheter colorectal images. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.

Authors

  • 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.
  • Shuying Li
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
  • Yifeng Zeng
    Department of Biomedical Engineering, Washington University in St. Louis.
  • Hassam Cheema
    Department of Anatomic & Molecular Pathology, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Ebunoluwa Otegbeye
    Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Safee Ahmed
    Department of Anatomic & Clinical Pathology, Washington University School of Medicine, St. Louis, Missouri, USA.
  • William C Chapman
    Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine.
  • Matthew Mutch
    Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine.
  • Chao Zhou
    Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania.
  • Quing Zhu
    Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, U-2157, 06269 CT, Storrs, USA.

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