Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network

Signal enhancement in spark-assisted laser-induced breakdown spectroscopy for discrimination of glioblastoma and oligodendroglioma lesions

Here, the discrimination of two types of lethal brain cancers, i.e., glioblastoma multiforme (GBM) and oligodendroglioma (OG) are investigated under the laser-induced breakdown spectroscopy (LIBS) and the electrical spark-assisted laser-induced breakdown spectroscopy (SA-LIBS) in order to discriminate the human brain glioma lesions against the infiltrated tissues. It is shown there are notable differences between the plasma emissions over the brain gliomas against those of infiltrated tissues. In fact, a notable enhancement appears in the characteristic emissions in favor of SA-LIBS against those of conventional LIB spectra. Moreover, the plasma properties such as temperature, electron density, and degree of ionization are probed through the data processing of the plasma emissions. The corresponding parameters, taken from SA-LIBS data, attest to be lucidly larger than those of LIBS up to one order of magnitude. In addition, the ionic species such as Mg II characteristic line at 279 nm and caII emission at 393 nm are notably enhanced in favor of SA-LIBS. In general, the experimental evidence verifies that SA-LIBS is beneficial in the discrimination and grading of GBM/OG neoplasia against healthy (infiltrate) tissues in the early stages.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C₂ molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

Real-time detection of bone-invasive oral cancer with laser-induced breakdown spectroscopy: A proof-of-principle study

OBJECTIVES

Intraoperative definition of resection margin status in bone-invasive oral cancer is a fundamental problem in oncologic surgery due to the lack of rapid bone analysis methods. Laser-induced breakdown spectroscopy (LIBS) provides direct measurement with real-time examination of a minimal tissue sample. This proof-of-principle study aimed to evaluate the possibility of distinguishing tumorous and healthy areas with LIBS.

MATERIALS AND METHODS

LIBS experiments were executed on native segmental mandibulectomy specimens from 15 patients with bone-invasive oral cancer. Normalized and intensity-matched spectra were compared. Under biological derivation, peak area calculation and principal component analysis (PCA) were applied. The discriminatory power of the PCAs was correlated with the architectural and cytological characteristics of the lasered tumor tissue. Receiver operating characteristics analysis was used to evaluate the performance of LIBS in the real-time detection of bone-invasive cancer.

RESULTS

Calcium (Ca), which is high in healthy bone, is replaced by potassium (K) and sodium (Na) in bone-invasive cancer. The degree of stromal induction is significantly correlated with the discriminatory power between healthy and tumorous spectra. In this study, LIBS ensured an overall sensitivity of 95.51% and a specificity of 98.64% via the intracellular detection of K and Na.

CONCLUSION

This study demonstrated robust real-time detection of bone-invasive oral cancer with LIBS, which may lay the foundation for establishing LIBS as a rapid bone analysis method. Further development of a LIBS-guided assessment of bone tumor resection margins might reduce the extent of bony resection without compromising oncologic safety.

Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis

Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.