Endoscopic histological assessment of colonic polyps by using elastic scattering spectroscopy

Abstract

Background Elastic-scattering spectroscopy (ESS) can assess in vivo and in real-time the scattering and absorption properties of tissue related to underlying pathologies. Objectives To evaluate the potential of ESS for differentiating neoplastic from non-neoplastic polyps during colonoscopy. Design Pilot study, retrospective data analysis. Setting Academic practice. Patients A total of 83 patients undergoing screening/surveillance colonoscopy. Interventions ESS spectra of 218 polyps (133 non-neoplastic, 85 neoplastic) were acquired during colonoscopy. Spectral data were correlated to the classification of biopsy samples by 3 GI pathologists. High-dimensional methods were used to design diganostic algorithms. Main Outcome Measurements Diagnostic performance of ESS. Results Analysis of spectra from polyps of all sizes (N=218) resulted in a sensitivity of 91.5%, specificity of 92.2%, and accuracy of 91.9% with a high-confidence rate of 90.4%. Restricting analysis to polyps <1cm (n=179) resulted in a sensitivity of 87.0%, specificity of 92.1%, and accuracy of 90.6% with a high-confidence rate of 89.3%. Analysis of polyps ≤5 mm (n=157) resulted in a sensitivity of 86.8%, specificity of 91.2%, and accuracy of 90.1% with a high-confidence rate of 89.8%. Limitations Sample size, retrospective validation used to obtain performance estimates. Conclusions Results indicate that ESS permits accurate, real-time classification of polyps as neoplastic or non-neoplastic. ESS is a simple, low cost, clinically robust method with minimal impact on procedure flow, especially when integrated into standard endoscopic biopsy tools. Performance on polyps ≤ 5mm indicates that ESS may, in theory, achieve PIVI performance thresholds. ESS may one day prove to be a useful tool used in endoscopic screening and surveillance of colorectal cancer.

Optical Spectroscopy as a Method for Skin Cancer Risk Assessment

“…In conclusion, ESS measurements are effective in translating tissue morphology at the cellular and sub‐cellular levels into spectral features, and permit the development of a meaningful classification algorithm for lesions with divergent pathological features. The previously well‐established technical basis of ESS supports the findings of this study .…”

“…Due to its ability to translate tissue morphology into spectral features at the cellular and sub‐cellular levels, ESS relates directly to the observed tissue architecture and structure of histopathologic features . Different tissue types and histopathological status exhibit specific optical signatures, and ESS has been demonstrated clinically to assess malignancy in multiple tissue types . Spectral correlation with histopathologic diagnosis using variations of ESS has also been reported by other groups .…”

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

“…Considerable efforts of colonoscopy in the field have been spent on optimizing the yield of polyp detection. Rodriguez-Diaz et al 112 successfully applied elastic-scattering spectroscopy to the real-time classification of polyps as neoplastic or non-neoplastic lesions with a sensitivity of 91.5%, specificity of 92.2% and accuracy of 91.9%. In particular, a high-confidence rate of approximately 90% was associated with accuracies of 90.6% for polyps smaller than 1 cm and 90.1% for polyps equal to or smaller than 5 mm.…”

Spatially resolved diffuse reflectance spectroscopy endoscopic sensing with custom Si photodetectors

“…Noninvasive optical technologies to enhance WLE can be broadly classified as imaging or spectroscopic [2][3][4][5][6][7]. Imaging technologies such as optical coherence tomography, narrowband imaging, and confocal endomicroscopy complement WLE with images that are interpreted similarly to histopathologic cross sections [5,7].…”

“…In contrast, spectroscopic techniques can classify tissues (e.g. neoplastic or non-neoplastic) through quantitative measurements of tissue chemistry and structure [5,[7][8][9][10], or through spectral pattern recognition algorithms [4,9,11], often in real-time.…”