Digital dynamic discrimination of primary colorectal cancer using systemic indocyanine green with near-infrared endoscopy

Abstract

As indocyanine green (ICG) with near-infrared (NIR) endoscopy enhances real-time intraoperative tissue microperfusion appreciation, it may also dynamically reveal neoplasia distinctively from normal tissue especially with video software fluorescence analysis. Colorectal tumours of patients were imaged mucosally following ICG administration (0.25 mg/kg i.v.) using an endo-laparoscopic NIR system (PINPOINT Endoscopic Fluorescence System, Stryker) including immediate, continuous in situ visualization of rectal lesions transanally for up to 20 min. Spot and dynamic temporal fluorescence intensities (FI) were quantified using ImageJ (including videos at one frame/second, fps) and by a bespoke MATLAB® application that provided digitalized video tracking and signal logging at 30fps (Fluorescence Tracker App downloadable via MATLAB® file exchange). Statistical analysis of FI-time plots compared tumours (benign and malignant) against control during FI curve rise, peak and decline from apex. Early kinetic FI signal measurement delineated discriminative temporal signatures from tumours (n = 20, 9 cancers) offering rich data for analysis versus delayed spot measurement (n = 10 cancers). Malignant lesion dynamic curves peaked significantly later with a shallower gradient than normal tissue while benign lesions showed significantly greater and faster intensity drop from apex versus cancer. Automated tracker quantification efficiently expanded manual results and provided algorithmic KNN clustering. Photobleaching appeared clinically irrelevant. Analysis of a continuous stream of intraoperatively acquired early ICG fluorescence data can act as an in situ tumour-identifier with greater detail than later snapshot observation alone. Software quantification of such kinetic signatures may distinguish invasive from non-invasive neoplasia with potential for real-time in silico diagnosis.

Appendiceal Inversion Presenting as a Cecal Polypoid Mass on Screening Colonoscopy: A Case Report and Review of Available Diagnostic Adjuncts to Differentiate Benign From Malignant Colorectal Pathology

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Evaluating clinical near-infrared surgical camera systems with a view to optimizing operator and computational signal analysis

“…9 This work has also empowered the development of machine learning (ML) algorithms. 10 Furthermore, investigational applications of quantitative analysis of tissue microperfusion have expanded this field to the characterization of tissue pathology (e.g., diagnosis of cancer in rectal polyps 11 ) with opportunities in artificial intelligence powered cancer diagnosis. 12,13 Also important in this is the inclusion of simultaneous white light imaging especially in laparoscopy to facilitate the corroboration of the fluorescence angiogram with the operative field (e.g., the prepared mesentery in colonic resection).…”