In-vitro study on the identification of gastrointestinal stromal tumor tissues using laser-induced breakdown spectroscopy with chemometric methods.

Early-stage detection of tumors helps to improve patient survival rate. In this work, we demonstrate a novel discrimination method to diagnose the gastrointestinal stromal tumor (GIST) and its healthy formalin fixed paraffin embedded (FFPE) tissues by combining chemometric algorithms with laser-induced breakdown spectroscopy (LIBS). Chemometric methods which include partial least square discrimination analysis (PLS-DA), k-nearest neighbor (k-NN) and support vector machine (SVM) were used to build the discrimination models. The comparison of PLS-DA, k-NN and SVM classifiers shows an increase in accuracy from 94.44% to 100%. The comparison of LIBS signal between the healthy and infected tissues shows an enhancement of calcium lines which is a signature of the presence of GIST in the FFPE tissues. Our results may provide a complementary method for the rapid detection of tumors for the successful treatment of patients.

Evaluation of human melanoma and normal formalin paraffin-fixed samples using Raman and LIBS fused data.

In this research, we developed a novel method of quantitative analysis to increase the detection potential for screening and classification of skin cancer (melanoma). We fused two distinct optical approaches, an atomic spectroscopic detection technique laser-induced breakdown spectroscopy (LIBS) and a vibrational molecular spectroscopic technique known as Raman spectroscopy. Melanoma is a kind of skin cancer, also known as malignant melanoma, that developed in melanocytes cells, which produced melanin. Classification of melanoma cancerous tissues is a fundamental problem in biomedicine. For early melanoma cancer diagnosis and treatment, precise and accurate categorizing is critically essential. Laser-based spectroscopic approaches can be used as an operating instrument for simultaneous tissue ablation and ablated tissue elemental and molecular analysis. For this purpose, melanoma and normal paraffin-embedded tissues are used as a sample for LIBS and Raman measurement. We studied the data provided by laser-based spectroscopic methods using different machine learning classification techniques of extreme learning machine (ELM), partial least square discriminant analysis (PLS-DA), and K nearest neighbors (kNN). For visualization of melanoma and normal data, principal component analysis (PCA) is also used. Three different ways are used to process the data, LIBS measurement, Raman measurement, and combine data measurement (merged/fused data), and then compared the results. ELM classification model achieved the highest accuracy (100%) for combined data as well as for Raman and LIBS data, respectively. According to the experimental results, we can assume that Raman spectroscopy and LIBS combine can significantly improve the identification and classification accuracy of melanoma and normal specimens.

Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.

The identification and preservation of parathyroid glands (PGs) is a major issue in thyroidectomy. The PG is particularly difficult to distinguish from the surrounding tissues. Accidental damage or removal of the PG may result in temporary or permanent postoperative hypoparathyroidism and hypocalcemia. In this study, a novel method for identification of the PG was proposed based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS spectra were collected from the smear samples of PG and non-parathyroid gland (NPG) tissues (thyroid and neck lymph node) of rabbits. The emission lines (related to K, Na, Ca, N, O, CN, C2, etc.) observed in LIBS spectra were ranked and selected based on the important weight calculated by random forest (RF). Three machine learning algorithms were used as classifiers to distinguish PGs from NPGs. The artificial neural network classifier provided the best classification performance. The results demonstrated that LIBS can be adopted to discriminate between smear samples of PG and NPG, and it has a potential in intra-operative identification of PGs.

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

Quick and accurate diagnosis helps shorten intraoperative waiting time and make a correct plan for the brain tumor resection. The common cryostat section method costs more than 10 minutes and the diagnostic accuracy depends on the sliced and frozen process and the experience of the pathologist. We propose the use of molecular fragment spectra (MFS) in laser-induced breakdown spectroscopy (LIBS) to identify different brain tumors. Formation mechanisms of MFS detected from brain tumors could be generalized into 3 categories, for instance, combination, reorganization and break. Four kinds of brain tumors (glioma, meningioma, hemangiopericytoma, and craniopharyngioma) from different patients were used as investigated samples. The spiking neural network (SNN) classifier was proposed to combine with the MFS (MFS-SNN) for the identification of brain tumors. SNN performed better than conventional machine learning methods for the analysis of similar and limited MFS information. With the ratio data type, the identification accuracy achieved 88.62% in 2 seconds.