1. Introduction

Cancer is undoubtedly one of the severe and life-threatening diseases, in which abnormal cell growth and spread to other parts of the body, and factors of the incidence are still unknown. Based on available statistical information on the issue, there are millions of people who have cancer-effected deaths due to late diagnosis, or no reliable clinical technique that can be used to diagnose the cancerous areas accurately, especially for diseased flat tissue [1]. Among all cancers, ovarian cancer is one of the most common and deadly malignancies among women. The survival rate of patients who are diagnosed in the first stage is elevated to 90% whereas those diagnosed in the second and third stages are decreased to 70% and 60%, respectively [2–4]. The results means that reliable detection and quick selection of therapy could prevent or slow down the disease impact on patients’ lives and increase the opportunity to eradicate the malignancy [5,6]. The possible benefit for cancer diagnosis can be marginal in some cases because of the limitations of detection methods or false results from the screening [7]. Consequently, there is an urgent need to develop new detection techniques to enhance sensitivity and specificity of available traditional diagnostic methods.

Optical diagnostics techniques based on optical spectroscopy and optical imaging are very valuable tools for detecting cancer tissues in the human body. Several optical methods such as fluorescence and Raman, diffuse reflectance, light scattering, low coherence, and coherent backscattering are used for cancerous detection [8–13]. Scattering, absorption, and emission of light that emerge from tissue biomarkers can provide functional, structural, and biochemical information about tissues, in turn, these pieces of information can be used to detect and manage of different types of cancerous [14–17]. These techniques are still in early stage of developments. Besides the optical methods, there are a number of other diagnostic techniques; such as transvaginal ultrasonography, MRI and positron emission tomography. But all these techniques have their limitations [18,19]. The common disadvantages of these technologies are being harmful to the live organs, quite-expensive, time-consuming, or requiring complicated sample preparation procedures. In addition, the incision by surgical tools results in the swelling and displacement of the tissue.

Laser-induced breakdown spectroscopy (LIBS) is an attractive analytical technique that utilizes the motion of ions and atoms and simple molecules in plasma for quantitative and qualitative analysis of elemental composition in the sample [20]. The interaction of high-power light pulses with biological samples is receiving growing interest from the scientific community in recent years for its potential application in biophysics [21]. LIBS method is being utilized in biomedical researches for discrimination and classification of biological samples such as tissues, blood, different bacteria nails, hair biological agents, bio-fluids and so on [22–26]. LIBS has also been applied for the diagnosis of cancer tissues. Distinguishing of the cutaneous melanoma from the surrounding healthy skin [27]. Differentiate breast and colorectal cancer by measuring the accumulation of nutrients elements in tissues [28]. determine colon cancer tissues from healthy ones by measuring heavy metals such as lead (Pb), chromium (Cr), and mercury (Hg) [29]. The plasma parameters have been used as the alternative diagnosis of several malignant tissue samples [30]. Proposed LIBS as potential intraoperative method to discriminated the infiltrative glioma boundary [31]. However, the common methods conducted to determine whether the tissue is healthy or cancer-infested depend on the difference in intensities ratios, element content and/or statistical methods such as linear discriminant analysis (LDA) and principal component analysis (PCA). Recent advances in LIBS for biological analysis potentially provide promising diagnosis, fast sensor systems for pathogen and biological agents and other important applications in the biomedical research area.

Currently, various methods have been used to determine the elemental concentrations of samples using LIBS which includes univariate/multivariate regression, partial least squares regression, calibration-curve, and calibration-free technique and so on [32]. The calibration curve method requires reference samples with known composition and similar to unknown samples to build the calibration curves versus the emission line intensities as a function of the elemental concentrations. Moreover, matrix-matched standards are not available in all situations. On the other hand, the Calibration Free Laser Induced Breakdown Spectroscopy (CF-LIBS) treats both matrix and analyte as part of the analytical problem to calculate plasma parameters and elemental concentration [33]. In this technique, the concentration of the existing element in the targeted samples mainly depends on the Boltzmann plot, whether for atomic or ionic lines. The Boltzmann plot requires more than two optically thin lines for enough accuracy which may be complicated to obtain for some of the component elements that exist in the sample, such as minor or trace elements. For that reason, the algorithm was built subsequently to calculate the elemental density using the value of theoretical electron density [34]. Several reports support the CF-LIBS method using reference samples or by comparison with the results of more established techniques. In majority of the cases, satisfactory results have been obtained while the accuracy was deficient in some [35].

This work aims to differentiate neoplastic tissues from healthy ones using line intensities ratios in combined with CF-LIBS. The plasma parameters, including electron temperature and electron density, are determined as a function of time. The CF-LIBS technique is studied for fast and online analysis to obtain the concentrations of different elements present in the samples, especially for those samples where matrix-matched calibration is unavailable. Then, the CF-LIBS results obtained from ovarian cancer samples were compared with Inductive coupled plasma-optical emission spectroscopy (ICP-OES) method. The Successful real-time qualitative and quantitative analysis techniques by LIBS for differentiation of the biological tissues may contribute in broadening the field of applications and also increase the safety of clinical laser systems.

2. Experimental

2.1 Experimental setup

The schematic diagram of the experimental setup to investigate time-resolved LIBS is illustrated in Fig. 1(a). A Q-switched pulsed Nd: YAG laser works at a fundamental wavelength of 1064 nm delivering 10 ns pulse duration, a repetition rate of 10 Hz and output energy of 80 mJ/pulse. The laser pulses were reflected using four mirrors to achieve the vertical direction into the surface, and Plano-convex lens (f = 75 mm) was used to focus laser pulse onto the targeted sample to generate micro-plasma. The samples to be measured were placed on a manual three-dimensional stage to provide accurate adjustment of the detection system and a fresh target surface for successive laser shots. The plasma emission was collected in the same laser optical axis by using a focused lens that allows the collection of optimum intensity from plasma. The emission signal was separated from laser radiation by a short pass dichroic mirror and Plano-convex lens (f = 60 mm) to form plasma emission at the input of a fiber optics cable that is connected to a spectrometer. The spot diameter at sample surface was determined using Zemax software and the corresponding power density is equal approximately 2.18 W$c{m^{ - 3}}$. In the optical element configuration, the distance between the objective-lens and surface of the target were kept the same during the whole chain of experiments, thus results in the absence of any difference in laser spot size. The plasma emission was recorded by Spectrograph (SR-303I-A Spectrograph, Andor technology) with 600 grooves per mm. All the experiments reported here are taken with the same spectral resolution and wavelength range-in-order to have the optimum spectral line intensity and resolution. The detector array involved is equipped with a time-gated intensified charge-coupled device (ICCD) (USB iStare ICCD detector, make: Ander technology: model: DH312T-18U-03). The ICCD recorded the spectral line at spectrograph exit focal plane with a spectral range from 180 to 900 nm. Time-gated detection was set normally at 100 ns at each delay of signal recorded after laser shot. For optimized window-detection, the plasma emissions were recorded at different time-delay, in the variation range from 100 ns to 5 µs. LIBS spectra for quantitative and qualitative analyses were recorded at a time delay of 0.5 µs and time-gate of 2.5 µs. All LIBS spectra were accumulated of 20 laser pulses to mitigate the effect of the experimental fluctuations of the ablation process and sample matrix. For the following discrimination analysis, 100 spectra were collected from each tissue sample of the 32 samples. The system was controlled by the original software, developed by Andor technology.

 

Fig. 1. (a) Experimental setup used for LIBS. M: mirror, DM: Dichroic mirror and L: Lens. The main devices are laser, spectrograph and ICCD. (b) Flow diagram of operating principle of observation-windows at different delays.

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The operating principle of the Time-resolved technique is based on time-observation-windows illustrated in Fig. 1(b), where pulsed laser has been used to generate the plasma. The time-gated method measures the elements decay curve within multi-observation-gates with equal widths that are typically required. The decay curves were observed by keeping the width of gates equal, as typically required for better results.

2.2 Sample preparation

For this work, mouse ovarian cancer model (C57BL/6 mice) were used, aged 10-12 weeks at the time of tissue harvest for initiated experiments and data collection. The animals were kept and cared on in-housed plastic cages, provided with food and water in ventilated rooms. The mice were healthy and not suffering from local or systemic diseases. The mice were injected by ID8 tumor cells line for the purpose of growing subcutaneous tumors model. Animals were euthanized with the aim of collecting tissues when the tumor became greater than 1 cm × 1 cm in any dimension.

The tissues were obtained at the slaughter time, dissected manually to thickness of approximately 1 mm with the mean size of the samples was around 0.5 cm × 0.5 cm. Experiments were performed on thirty-two samples collected from nine mice. The tissues were kept for 2-3 hours before experiments to minimize subsequent alterations in the tissue that might cause protein degradation, exsiccation or to avoid any morphological alterations of the tissues which could affect the LIBS signals. After the preparation, all the samples were rinsed with sterile saline water to remove any residual superficial contaminants (blood, hair, etc.), and then stored and transported using refresh slide storage box at room temperature.

For ICP-OES quantitative analysis, 285 mg of tissue samples were placed in the digestion vessel and dissolved in 500 µL of nitric acid and kept for one week, and centrifuge to remove sediment, and extract the supernatant, then diluted in 10 mL of deionized water to the desired concentration. We used micro-filter to separate the sediment of the fluids. Eight samples were used for ICP-OES analysis.

3. Results and discussion

3.1 Analysis of optical emission lines

Experiments were performed to investigate the feasibility of LIBS as a tool to discriminate neoplastic tissue from non-neoplastic tissue. The acquired data of integrated-Spectra emission was recorded during vibration and recombination of induced-plasma. The observed emission lines were carefully recorded and compared with the data published by the National Institute of Standards and Technology (NIST). For qualitative analysis applications, the normalized of LIBS spectra are required to balance the spectral fluctuations caused by experimental variation and matrix conditions. We have used CN B-X (0,0) band at 388.34 nm [36] for data normalized because the CN-band emission was mainly contributed and stable of all LIBS measurements.

Figure 2 shows typical LIBS spectra of thirty-two samples. The data recorded belongs to two types of tissues. The emission spectrum collected from plasma plume suggested the existence of major elements such as Magnesium (Mg), silicon (Si), iron (Fe), calcium (Ca), sodium (Na), potassium (K), carbon (C), nitrogen (N), oxygen (O) and hydrogen (H). However, the oxygen, nitrogen, and hydrogen elements are present in ambient air. The ${C_2}$ and CN molecular emissions are also observable in the LIBS spectrum of both tissues. These elements show the most prominent peak in the recorded spectra.

 

Fig. 2. Mean LIBS spectra taken from Neoplastic and Non-neoplastic tissue samples.

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A closer look at the spectra allows for the identification of many other low-intensity peaks, which are neither classified with their competent elements nor used for the discernment analysis based on the ratio. It is due to their instability, low-discriminatory, and low reproducibility. It can be seen that both tissue types have notable changes between spectral profiles. The neoplastic tissue is characterized by strong emission lines especially of trace and major elements (Mg, Si, Fe, Ca, Na, and C). The CN-bond emission of the neoplastic tissue is weaker than those of the Non-neoplastic tissue. As reported in the literature, the abundance of trace elements and minerals in the tumor tissues is significantly higher than that in healthy ones [30]. The emission intensities based on LIBS technique are important for tissue differentiation. The carbon and trace elements emission intensities are important signs for discriminating tumors from cancer and fat from nerves [37]. Moreover, the higher intensities of carbon in non-neoplastic are observable not only from the pure lines but also by the simple molecular emissions such as CN-band (359 nm & 388.42 nm) and ${C_2}$-band (563.8 nm). A little higher emission peak of oxygen triplet (777.4 nm) was also observed in non-neoplastic tissues. The emission intensities of the other two lines, namely nitrogen (N) and hydrogen (H) (Fig. 2) are also higher in non-neoplastic tissues.

3.2 Determination of the elemental concentration of tissues using CF-LIBS

The interaction of each laser pulse with the sample leads to the generation of plasma. As soon as the plasma is generated, the characteristic spectra states are emitted from the induced plasma that is evolving in time due to plume’s hydrodynamic expansion. The time-gates model has a substantial interest in monitoring the decay time course of atoms, ions, and molecules, through the recording of the plasma spectrum at varying time-delay between the probe-pulse and detection gate. The operating principle diagram of the observation-gates is presented in Fig. 1(b). The calibration-free LIBS analysis procedure was used to record the spectra at different delay windows to determine the exponential calibration curve based on calibration-free and the optimal gates for LIBS investigation.

The calibration-curve is the most common approach to obtain quantitative information of multi-element samples in LIBS measurements. The extraction of the calibration curve for each element that exists in the sample is an arduous job due to the effect of the matrix on the emission lines of the same elemental composition [38]. The calibration-free method was proposed as alternative visibility, instead of dealing with the matrix as an external influential, it is taken of matrix effect as a part of the analytical problem [39]. The basic idea of calibration-free method is to use the basic equations derived from the local thermodynamic equilibrium (LTE) assumption equation instead of the matrix effect for self-consistent of every single measurement and avoid the requirement for any comparison with reference samples. The determination of plasma parameters such as electron density and electron temperature is of essential importance to the preformed CF-LIBS method. With the assistance of these parameters, it can be determined whether the plasma is following the LTE. Once this condition is achieved, these parameters can be used to measure the elemental composition of the sample.

3.2.1 Optically thin plasma

The plasma condition to be optically thin can be determine using by comparing the experimentally intensities ratio of two lines having same upper-level energy with the theoretical intensities calculation. The intensity ratio method is utilized to validate the optically thin conditions for chosen lines [40]:

The self-absorption correction was created by establish optically thin emission lines. Using of lines spaces which have high differences in energy and transition probability, these lines may be used to estimate plasma parameters because they are optically thin. These lines have been utilized for self-absorption correction which used for CF-LIBS analysis. We have used Ca-I 422.67nm line for self-absorbed lines as a reference. The lines intensities were corrected by line widths ratio [41]:

3.2.2 Measurement of electron number density

The Stark effect is the dominant mechanism for the broadening of spectral lines in laser induced plasma. The accuracy of the quantitative analysis of CF-LIBS method depends exceedingly on the existence of LTE conditions. The criterion documented to confirm the existence of LTE in plasma is the McWhirter’s criterion [42]:

To determine the ionization degree and excitation inside the generated plasma, the identification of electron density is mandatory. Whereas the contribution of ions to Stark broadening is minimal. The full width at half maximum (FWHM) determined the electron density of the broadened line using the following relationship [43]:

Here, the mean value of electron density of the integrated LIBS spectrum was determined by Stark effect from the lines profile of Ca-I atomic line at 422.67 nm as $1.8 \times {10^{16}}c{m^{ - 3}}\& \textrm{ }1.55 \times {10^{16}}c{m^{ - 3}}$ of neoplastic and non-neoplastic tissues, respectively.

Using the electron temperature obtained by Boltzmann plot and the transition energy (ΔE=2.93 eV) of Ca-I line at 422.67 nm, in Eq. (3), the estimated LTE threshold is ${N_e} \ge 3.9 \times {10^{15}}c{m^{ - 3}}\& \ge 3.65 \times {10^{15}}c{m^{ - 3}}$. This result established the validity of the LTE assumption because the measured densities are higher than ${10^{16}}c{m^{ - 3}}$. As well, the LTE conditions were interpolated for the different time-delay to determine the calcium concentrations as a function of gate-delay. The LTE are well satisfied in the temporal-observation-windows of 0.2 µs to 3.5 µs. Another criterion for evaluating the existence of LTE depends on the diffusion length which can be calculated by following Eq. [33]:

The resolution of the detection system used is high. In contrast, the main contributions to the Doppler broadening value in our instrument are quite small, which is 0.008 at neutral Ca-I line (422.67 nm) at an elevated temperature. The broadening value can be neglected in the calculations because of its small value. The instrumental line broadening is exhibiting Gaussian shape, and Stark broadening profile has been debriefed by simulating the line as a Voigt profile as depicted in Fig. 6(a). The electron density of neoplastic tissues is higher than non-neoplastic ones. The successive decrease in the electron density was observed with an increase of time-delay for both samples, as illustrated in Fig. 3. The electron density falls faster in the early temporal window from 0.200-0.8 µs and slower in the temporal window of 1–4 µs. The reduction of the electron density was caused by the recombination of electrons and ions during the expansion phase of the plasma.

 

Fig. 3. Electron intensity and electron temperature versus time of (a) Neoplastic and (b) non-neoplastic. Whereas, the horizontal error bars indicate the detector gate width.

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3.2.3 Measurement of electron temperature

To estimate the plasma electron temperature, the spectroscopic data of spectra lines were observed and confirmed with the standards of National Institute of Standards and Technology (NIST). The electron temperatures were estimated from the relative intensities of observed lines in both tissue types with the help of Boltzmann distribution function plot method using the following equation:

Assuming Boltzmann distribution, the R.H.S of Eq. (6) will directly show the slope (m) of the plot that associated with the electron temperature. From the plots of $ln({{\raise0.5ex\hbox{$\scriptstyle {{I_\lambda }}$}\kern-0.1em/\kern-0.15em\lower0.25ex\hbox{$\scriptstyle {{g_k}{A_k}}$}}} )$ with ${E_k}$(Fig. 6(b)), the Boltzmann plots have been fitted for the most abundant elements in the LIBS spectra of neoplastic and non-neoplastic tissues. The excitation temperatures are calculated from the slopes of the related lines $- {\raise0.5ex\hbox{$\scriptstyle 1$}\kern-0.1em/\kern-0.15em\lower0.25ex\hbox{$\scriptstyle {{k_B}T}$}}$.

The electron temperature was estimated by Boltzmann plot using the relative emission intensities of observed lines. The average value of electron temperature was calculated from integrated spectrum to be 11766 ± 452 K and 11260 ± 378 K or neoplastic tissues and non-neoplastic tissues, respectively. These average values were further used to do the quantitative analysis. The electron temperature of 10000 K is sufficiently high to detect the atomic and ionic lines of first ionized species (Ca, Mg, Fe, K, C, and Na). In contrast, the electron temperature does not appear to be sufficiently hot that can emit the single ionized of O-II and N-II due to their relatively high ionization energies [44,45].

The temporal evolution of the electron temperature as a function of delay time in the range of 0.2–5 µs and upper-level energies from calcium atomic transitions Ca-I for both samples under investigation is shown in Fig. 3. The electron temperature decreases with an increase in time-delay due to the regular adiabatic cooling-down of plasma with time after the laser pulse disappears [46]. The decrease in temperature is faster during the initial phase of plasma between 0.2–0.8 µs, while it is slower after 0.8 µs.

3.2.4 Calibration-free measurement

The concentration of elemental composition in the samples can be determined by applying basic equations resulting from the LTE assumption and using the plasma parameters of all the element of interest in the samples. Besides measuring the element concentrations using the integrated LIBS intensity, we used exponential calibration curves based on calibration-free for the evaluation of concentration of different samples as a function of a time delay with investigating the influence of acquisition time-delay in the calibration-free analysis. As well, the investigation of the various factors influencing the calibration-free analysis at different time-delay for determining the optimized detection windows.

The CF-LIBS method utilized in this work was proposed by in [39]. This method required optically thin plasma and LTE, which was achieved in the temporal and spatial observation-windows under specific conditions of plasma for unperturbed target composition. The plasma in LTE can be assumed as a spatially homogeneous source of radiation. Based on the theoretical foundation of the CF-LIBS method population, measured line’s of excited state of the measured line can be expressed as:

Table 1. List of identified emission lines of trace and major elements present in tissues.

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3.2.5 Performance investigation of observation-gates-delay in LIBS analysis

Usually, time-delay and observation windows methods are used to determine the mean decay-time and to optimize signal-to-noise ratio (SNR), for the analysis of spectral line intensity. The optimal spectral time-gate should be minimal to achieve enough accuracy in measuring time decay, which enables us to record good spectra. With time evaluation, there is the same dynamic process effect on the intensity, one of the most common self-absorption effects but relative errors should also be considered. The self-absorption lines of atoms and ions are directly proportional to time-delay, indicating that the optimal spectral windows are based on the minimum self-absorption. It also has to be noted that the analytical accuracy and stability are higher at early-observed-window of 0.2-0.6 µs [48].

In Fig. 4, the quantitative analysis of the calcium measured in neoplastic and healthy tissues were compared as a function of time-delay and average intensities. The calibration curves were created for calcium lines. The exponential fit calibration curves for measuring the correlation coefficient of element concentrations at various time intervals were also created. The approximate stability in the reduction of element concentrations throughout all gate time intervals indicates the validity of the measurement at various gate timing. In the literature, the validity of the LTE model for CF-LIBS analysis of spectra recorded for aluminum samples at the various time intervals between 0.5-6 µs was observed [49]. The relative error of the self-absorption effect at different delays is obtained by the exponential fitting parameters of Fig. 4, which contributed in select the optimum detection windows for qualitative and quantitative LIBS analysis. The exponential calibration curves method for an estimate of the self-absorption was proposed by in [50].

 

Fig. 4. The calibration curves of calcium lines for tissues samples from the spectra were recorded with different time-delays.

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The concentration ratio of calcium showed a regular decrease for both tissue types with temporal-evolution of plasma as shown in Fig. 4. In addition, the concentration values of the elements in tumor samples are higher than that in non-neoplastic samples at early-observation-gates of 0.2-1 µs, while it shown less difference in concentration value at a time delay of 1-5 μs.

To determine the observation-windows-delay for the most optimized qualitative-quantitative analysis, we evaluated the measurement errors at different measurement times. The distinctive time evolution is shown in Fig. 5, for non-neoplastic and neoplastic tissues, produced by Ca-I lines. For this evaluation, all relevant error sources were taken into account, including (i) electron density (ii) electron temperature, (iii) SNR ratio and uncertainty of the instrument's response, and (iv) self-absorption. For present target samples, the most accurate measurement windows were achieved in the time interval from 0.7 μs to 2.2 μs. For earlier recording times, the related measurement error rises mostly due to enhanced Stark broadening and the increase in the continuum emission intensity, resulting in stronger line interferences and a lower SNR ratio. For later times, the self-absorption of the stronger lines increases. Thus, the measurements should be performed with lines of mean intensities for which the SNR is high.

 

Fig. 5. Relative errors of neoplastic and Non-neoplastic tissues using Ca-I as function of time.

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3.2.6 Comparison of quantitative measurements of tissues using CF-LIBS and ICP-OES

The concentration of different elements obtained by the Calibrated-free method using the intensity ratios obtained from LIBS spectra was directly compared with ICP-OES for five major elements. Figure 6(c) shows the comparison of the concentration ratio for five elements (Mg, Fe, Ca, Na, and K) that exhibited increased concentrations in neoplastic samples compared to healthy tissues. The concentration-effect was the strongest for K, Na, and Ca peaks while a tiny difference appears for Fe and Mg.

 

Fig. 6. Stark broadened line profile of Ca-I line 422.67nm (a). Boltzmann plot of Ca-I lines (b). Comparison of concentration of neoplastic and non-neoplastic tissue samples between CF-LIBS result and ICP-MS techniques (c).

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Concentrations of major and/or trace elements in cancer tissues have been measured in many studies, but few studies measured their concentrations in paired cancer and healthy tissues. To the best of our knowledge, here we present the first report on the differentiation of neoplastic tissues from healthy ones by the CF-LIBS method. Observations of the abnormal abundance of some specific elements in tissues usually were associated with health or increased risk of cancers [51,52]. Iron is an essential element to the human body for multiple fundamental roles in a wide range of cellular functions, including cellular respiration, proliferation, DNA synthesis, and cell signaling; yet iron overabundant can be cytotoxic [53]. Calcium ions act as a universal intracellular second messenger, controlling a varying scope of cellular processes such as gene transcription, cell motility, muscle contraction, and exocytosis. The body controls calcium levels in the blood, so, the rises in calcium level may be lethal to the cells [54]. In cancer, calcium is involved in diverse processes of tumorigenesis including proliferation, gene transcription, migration, evasion of apoptosis, and angiogenesis [55]. Magnesium and sodium are also vital micronutrients for the human body [56]. However, by comparing the element abundance of both tissue types, our results demonstrated that the quantity of Fe, Mg, Ca, Na, and K were significantly higher in neoplastic tissues than in their paired non-neoplastic tissues. This observation can be ascribed to the influence of numerous factors linked in the distribution of atomic and ions which include the difference in the blood supply, proliferation, and hormonal disturbance. The causal correlation-ship of disturbances of elements concentrations with cancer risk is complicated, and so far still unknown. Statistical significance for eight samples was obtained, a p-value of greater than 0.05 meaning no appreciable statistical difference was observed of different tissues

The calibration ratio was in reasonable agreement with the LIBS spectra intensity of investigated elements. It was found that there is a significant increase in potassium, sodium, and magnesium, calcium and iron concentration in neoplastic tissues. Moreover, there is a strong correlation between LIBS lines intensity and relative elemental concentration [57]. Thus, the apparent rise of line intensities in the neoplastic samples subtended higher abundance than the healthy ones. The investigation should be undertaken using the Calibration-free method for other cancer types, with the development of new algorithms to increase the accuracy and coverage of the concentrations of all obtained elements in the LIBS spectrum.

4. Conclusion

In conclusion, besides qualitative analysis, the plasma parameters and compositional analysis were used as alternative diagnostic factors to investigate the neoplastic and non-neoplastic tissues. The possibility of cancer diagnosis by LIBS technique has been demonstrated using ovarian tissue. The calibration-free laser-induced breakdown spectroscopy provides satisfactory results for compositional analysis of multi-elemental biological tissues. Furthermore, the plasma parameters in LTE conditions show that the electron temperature and electron density are measurably higher for neoplastic tissues accompanying the higher abundance of major and trace elements. Analysis performed for different time-delays after disappearing of laser pulse and the detection-windows reveal that the most accurate dimensions are obtained in the early time period from (0.7 μs to 2.2 μs). LIBS can be utilized as a fast and online analysis technique to identify the neoplastic events and to obtain the elemental concentrations in the samples.