UCD Centre for Precision Surgery, University College Dublin, 47 Eccles Street, Dublin 7, Ireland.
Department of Medical Physics, Mater Misericordiae University Hospital, Dublin, Ireland.
Sci Rep. 2021 May 31;11(1):11349. doi: 10.1038/s41598-021-90089-7.
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.
由于近红外（NIR）内镜下吲哚菁绿（ICG）增强了实时术中组织微血管灌注的评估，因此它还可以动态地将肿瘤与正常组织区分开来，特别是使用视频软件荧光分析。对患者的结直肠肿瘤进行成像，在静脉内给予 ICG（0.25mg/kg）后，使用内窥镜腹腔镜 NIR 系统（PINPOINT 内窥镜荧光系统，Stryker）进行粘膜内成像，包括立即连续原位可视化直肠病变，最长持续 20 分钟。使用 ImageJ（包括每秒一帧，fps）和一个专用的 MATLAB®应用程序对斑点和动态时间荧光强度（FI）进行定量，该应用程序提供了数字化视频跟踪和每秒 30 帧的信号记录（可通过 MATLAB®文件交换下载 Fluorescence Tracker App）。在 FI 曲线上升、峰值和下降期间，对 FI 时间图中的 FI 进行统计学分析，将肿瘤（良性和恶性）与对照进行比较。早期动力学 FI 信号测量从肿瘤（n=20，9 种癌症）与延迟点测量（n=10 种癌症）中得出了有区别的时间特征。与正常组织相比，恶性病变的动态曲线峰值明显延迟，梯度较浅，而良性病变的强度从顶点下降的速度和幅度明显大于癌症。自动跟踪器定量有效地扩展了手动结果，并提供了算法 KNN 聚类。光漂白在临床上似乎并不重要。对术中连续获得的早期 ICG 荧光数据的分析可以作为原位肿瘤标志物，提供比单独后期快照观察更详细的信息。这种动力学特征的软件定量分析可能能够区分侵袭性和非侵袭性肿瘤，具有实时计算诊断的潜力。
