Real-time tissue differentiation based on optical emission spectroscopy for guided electrosurgical tumor resection

Abstract

Complete surgical removal of cancer tissue with effective preservation of healthy tissue is one of the most important challenges in modern oncology. We present a method for real-time, in situ differentiation of tissue based on optical emission spectroscopy (OES) performed during electrosurgery not requiring any biomarkers, additional light sources or other excitation processes. The analysis of the optical emission spectra, enables the differentiation of healthy and tumorous tissue. By using multi-class support vector machine (SVM) algorithms, distinguishing between tumor types also seems to be possible. Due to its fast reaction time (0.05s) the method can be used for real-time navigation helping the surgeon achieve complete resection. The system's easy realization has been proven by successful integration in a commercial electro surgical unit (ESU). In a first step the method was verified by using ex vivo tissue samples. The histological analysis confirmed, 95% of correctly classified tissue types.

Keywords: (170.1610) Clinical applications; (170.3890) Medical optics instrumentation; (170.6510) Spectroscopy, tissue diagnostics.

Figures

Fig. 1

(a) Microscope image of the tip of an electroscalpel and (b) light phenomenon appearing during activation. With an optical emission spectrometer, the spectral components of the spark are analyzed. (c) Typical tissue spectrum of the spark measured with our system. In the wavelength range of this example, bands and emission lines of atmospheric elements (N2, O2, H2) and molecular fragments of biological molecules (proteins, DNA, ..), for example NH, CH, CN and OH, can be recognized. In addition, specific atomic emission lines of essential and trace elements as Ca, K, Mg, Na, P, Cl, Cd, Zn, Rb, Cr, Co, Fe, I, Cu, Mn, Mo, Se, F, Si, As, Ni, Sn and V are assigned in this range. The relative intensities of these emission lines depend on the concentrations of the elements. The combination of relative intensities of many peaks form a specific tissue finger print and allows real-time identification of tissue within milliseconds.

Fig. 2

Electrosurgical OES-system to perform high speed tissue identification. (a) Basic function of the system: Light of the generated spark is coupled into an optical fiber at the distal end of the electroscalpel. The fiber is protected against contamination by an argon gas stream during activation. By fiber optical technology, the photons are guided to an analysis unit consisting of a commercial optical grating spectrometer and a processing unit to allow real-time analysis of the tissue information in the spark. (b), (c) Prototypes of the OES-electroscalpel tested in a preclinical study. The functional optical fiber is visualized by red laser light coupled from the proximal side. All functional components required at an operating table are integrated into a standard electroscalpel.

Fig. 3

Operational procedure of the OES-system. (a) The algorithm is first trained by including spectra from a patient-specific calibration into an existing database of spectra. In this way, the differentiation criteria (tissue finger print) can be calculated and real-time analysis is started. (b) Result of the tissue analysis with our system (y-axis) by means of a real tissue sample from the preclinical study (Section Preclinical pilot study, patient 11). Each point represents one measurement (x-axis); the color shows the histological result of each measurement point; the encircled points were used for calibration. In this example, all tissue measurements are correctly classified with our system. The point marked with an X is omitted, since no spectrometer data was available.

Fig. 4

Tissue sample from a preclinical study (Patient 11, ChRCC). (a) Nephrectomy specimen, (b) removed tissue sample, and (c,d) histological cut of the tissue sample after analysis with our system. Every lesion corresponds to one OES measurement. Each lesion was classified as tumorous or non-tumorous based on its histology.

Fig. 5

Results of the Linear Discriminant Analysis. Each point corresponds to one OES measurement; the color encodes the histological result of the measurement. The red points, for example, represent the measurements of healthy tissue, whereas blue points indicate the identification of CCRCC. This analysis illustrates, that it is possible to differentiate between healthy tissue (red) and tumorous tissue. Moreover, different types of tumors can also be classified. The purpose of this plot is to qualitatively illustrate the reliability in tissue differentiation. Because of small sample size (two measurements) the PRCC measurements are not considered in this plot.