Adaptive Boosting (AdaBoost)-based multiwavelength spatial frequency domain imaging and characterization for ex vivo human colorectal tissue assessment

Abstract

The current gold standard diagnostic test for colorectal cancer remains histological inspections of endoluminal neoplasia in biopsy specimens. However, biopsy site selection requires visual inspection of the bowel, typically with a white-light endoscope. Therefore, this technique is poorly suited to detect small or innocuous-appearing lesions. We hypothesize that an alternative modality-multiwavelength spatial frequency domain imaging (SFDI)-would be able to differentiate various colorectal neoplasia from normal tissue. In this ex vivo study of human colorectal tissues, we report the optical absorption and scattering signatures of normal, adenomatous polyp and cancer specimens. An abnormal vs. normal adaptive boosting (AdaBoost) classifier is trained to dichotomize tissue based on SFDI imaging characteristics, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 is achieved. We conclude that AdaBoost-based multiwavelength SFDI can differentiate abnormal from normal colorectal tissues, potentially improving endoluminal screening of the distal gastrointestinal tract in the future.

Keywords: AdaBoost; colorectal cancer; spatial frequency domain imaging.

FIGURE 1

Flow chart for AdaBoost algorithm

FIGURE 2

Absorption and reduced scattering coefficient maps (at 660 and 950 nm), H&E stained histology results and photographs of a T2 adenocarcinoma and corresponding normal tissue

FIGURE 3

Absorption and reduced scattering coefficient maps (at 660 and 950 nm), H&E stained histology results and photographs of an adenomatous polyp and corresponding normal tissue. The photographs were taken in vivo using white-light colonoscope

FIGURE 4

Boxplot of averaged absorption coefficient, A, and reduced scattering coefficient, B, of cancer, normal and adenomatous polyp (adenoma) groups

FIGURE 5

Receiver operating characteristic curve for abnormal vs normal AdaBoost classifier, A, SVM classifier with linear kernel, B, and SVM classifier with RBF kernel, C. The shaded zone marks the standard deviation

FIGURE 6

Receiver operating characteristic curve for adenomatous polyp vs normal AdaBoost classifier, A, and adenomatous polyp vs cancer AdaBoost classifier, B. The shaded zone marks the standard deviation