Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery

Abstract

. Significance: Diffuse reflectance spectroscopy (DRS) allows discrimination of tissue type. Its application is limited by the inability to mark the scanned tissue and the lack of real-time measurements. Aim: This study aimed to develop a real-time tracking system to enable localization of a DRS probe to aid the classification of tumor and non-tumor tissue. Approach: A green-colored marker attached to the DRS probe was detected using hue-saturation-value (HSV) segmentation. A live, augmented view of tracked optical biopsy sites was recorded in real time. Supervised classifiers were evaluated in terms of sensitivity, specificity, and overall accuracy. A developed software was used for data collection, processing, and statistical analysis. Results: The measured root mean square error (RMSE) of DRS probe tip tracking was and for the and dimensions, respectively. The diagnostic accuracy of the system to classify tumor and non-tumor tissue in real time was 94% for stomach and 96% for the esophagus. Conclusions: We have successfully developed a real-time tracking and classification system for a DRS probe. When used on stomach and esophageal tissue for tumor detection, the accuracy derived demonstrates the strength and clinical value of the technique to aid margin assessment in cancer resection surgery.

Real-time Tracking and Classification of Tumor and Nontumor Tissue in Upper Gastrointestinal Cancers Using Diffuse Reflectance Spectroscopy for Resection Margin Assessment

“…The probe contained 6 peripheral illumination fibers together with a central light collection fiber, each measuring 600 micrometers in diameter, within a 0.125-inch ferrule in a cylindrical configuration. The instrumentation and setup have been previously described . A schematic representation and photograph of the instrumentation for data acquisition is shown in Figure 1.…”

“…The DRS probe was connected to both the light source (HL-2000-HP; Ocean Optics) and the spectrometer (USB4000; Ocean Optics) to allow for data acquisition and sample illumination. All electronic devices communicated with proprietary software designed with Python version 3.6 (Python Software Foundation) on the laptop . VNIR indicates visible and near-infrared.…”

“…The main limitation of the DRS probe was that it did not leave any marks on the tissue being scanned. To overcome this limitation and localize the optical biopsy sites on the specimen, an optical tracking method was used, as described in previous work . Briefly, to track the DRS probe, a color marker was chosen based on the color distribution of biological tissue in the hue saturation value (HSV) color space.…”

Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra

“…Machine learning and empirical models, such as multivariate statistical algorithms, have also been used to distinguish between human tumors and surrounding tumor‐free tissues [10–12, 14, 21, 22, 32, 33, 37, 57–61]. The advantage is that no prior knowledge of the absorption and scattering properties of the tissue is required.…”

“…Many research groups are attempting to establish diagnostic criteria, or a form of "optical biopsy", based on the correlation between the histology and optical signatures of tissues, for a variety of human tissues, such as colon [9][10][11], skin [12][13][14], breast [15][16][17][18][19][20], cervical [21][22][23], oral [23][24][25][26][27], prostate [28][29][30][31], nasopharynx [32,33] and lung tissues [34][35][36][37]. Studies have also showed the potential of probe-based DRS to discriminate between human liver tumors and tumor-free surrounding tissue [7,8,[38][39][40][41][42][43].…”