Abstract:We use Raman microscopic
images with high spatial and spectral
resolution to investigate differences between human follicular thyroid
(Nthy-ori 3-1) and follicular thyroid carcinoma (FTC-133) cells, a
well-differentiated thyroid cancer. Through comparison to classification
of single-cell Raman spectra, the importance of subcellular information
in the Raman images is emphasized. Subcellular information is extracted
through a coarse-graining of the spectra at high spatial resolution
(∼1.7 μm2), producing a set o… Show more
“…Among the possible choices for the spectral distance, we have chosen the Euclidean one, since it maximizes the similarities among the spectra. The choice to use these two methods was made to exploit their complementarity of hierarchical and non-hierarchical approaches and consequently to give solidity to the obtained results 13 , 14 , 27 – 29 . Figure 6 Spread of the individual K-means clusters as a function of their number.…”
Over the last 50 years, the incidence of human thyroid cancer disease has seen a significative increment. This comes along with an even higher increment of surgery, since, according to the international guidelines, patients are sometimes addressed to surgery also when the fine needle aspiration gives undetermined cytological diagnosis. As a matter of fact, only 30% of the thyroid glands removed for diagnostic purpose have a post surgical histological report of malignancy: this implies that about 70% of the patients have suffered an unnecessary thyroid removal. Here we show that Raman spectroscopy investigation of thyroid tissues provides reliable cancer diagnosis. Healthy tissues are consistently distinguished from cancerous ones with an accuracy of ∼ 90%, and the three cancer typology with highest incidence are clearly identified. More importantly, Raman investigation has evidenced alterations suggesting an early stage of transition of adenoma tissues into cancerous ones. These results suggest that Raman spectroscopy may overcome the limits of current diagnostic tools.
“…Among the possible choices for the spectral distance, we have chosen the Euclidean one, since it maximizes the similarities among the spectra. The choice to use these two methods was made to exploit their complementarity of hierarchical and non-hierarchical approaches and consequently to give solidity to the obtained results 13 , 14 , 27 – 29 . Figure 6 Spread of the individual K-means clusters as a function of their number.…”
Over the last 50 years, the incidence of human thyroid cancer disease has seen a significative increment. This comes along with an even higher increment of surgery, since, according to the international guidelines, patients are sometimes addressed to surgery also when the fine needle aspiration gives undetermined cytological diagnosis. As a matter of fact, only 30% of the thyroid glands removed for diagnostic purpose have a post surgical histological report of malignancy: this implies that about 70% of the patients have suffered an unnecessary thyroid removal. Here we show that Raman spectroscopy investigation of thyroid tissues provides reliable cancer diagnosis. Healthy tissues are consistently distinguished from cancerous ones with an accuracy of ∼ 90%, and the three cancer typology with highest incidence are clearly identified. More importantly, Raman investigation has evidenced alterations suggesting an early stage of transition of adenoma tissues into cancerous ones. These results suggest that Raman spectroscopy may overcome the limits of current diagnostic tools.
“…Deep learning (DL) uses artificial neural networks (ANNs) to circumvent this issue, and several designs such as convolutional neural networks (CNNs) in image analysis and data mining have been successfully applied to other spectroscopic techniques such as fluorescence lifetime imaging microscopy (FLIM), and multiphoton light‐sheet microscopy (Hollon et al, 2020; Krauß et al, 2018; Suzuki et al, 2019). Taylor et al (2019) reported a work on high‐resolution Raman microscopic detection of follicular thyroid cancer cells with unsupervised ML obtained a more accurate (89.8%) distinction of FTC‐133 and Nthy‐ori 3–1, in comparison to single‐cell spectra (77.6%). Moawad et al (2019) combined top‐ and sub‐level classifier identified the mallei‐complex with high sensitivities (>95%) for the identification of burkholderia mallei and related species with ML‐based Raman spectroscopic assay.…”
Section: Modality Of Raman Imaging On Tissuesmentioning
Direct imaging of metabolism in cells or multicellular organisms is important for understanding many biological processes. Raman scattering (RS) microscopy, particularly, coherent Raman scattering (CRS) such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS), has emerged as a powerful platform for cellular imaging due to its high chemical selectivity, sensitivity, and imaging speed. RS microscopy has been extensively used for the identification of subcellular structures, metabolic observation, and phenotypic characterization. Conjugating RS modalities with other techniques such as fluorescence or infrared (IR) spectroscopy, flow cytometry, and RNAsequencing can further extend the applications of RS imaging in microbiology, system biology, neurology, tumor biology and more. Here we overview RS modalities and techniques for mammalian cell and tissue imaging, with a focus on the advances and applications of CARS and SRS microscopy, for a better understanding of the metabolism and dynamics of lipids, protein, glucose, and nucleic acids in mammalian cells and tissues.
“…[ 54 ] More informative optical data can also lead to a higher accuracy in an unsupervised model. [ 55 ] Therefore, the generation and selection of high‐quality data is just as important, if not more so, than the sophisticated design of advanced ML algorithms. It is a common problem in optics community that simulated data can be generated rather easily, but experimental data is expensive.…”
Section: Brief Overviews Of Optical Data and Machine Learningmentioning
confidence: 99%
“…In this section, we will highlight some representative works. We will examine the versatility of ML algorithms in their applicability to different categories of diseases such as neoplastic, [ 26b,55,58 ] infectious, [ 25a,39,59 ] inflammatory, [ 60 ] and miscellaneous. [ 61 ]…”
Section: Cases Of Ml‐assisted Decoding Of Optical Datamentioning
confidence: 99%
“…carried out diagnosis of follicular thyroid cancer by ML‐assisted analysis of Raman microscopic images. [ 55 ] FTC‐133 cells (malignant) and Nthy‐ori‐3‐1 cells (benign) were chosen as specimens, and two sets of data were introduced at cellular and subcellular resolutions. Subcellular data provide more spectral information.…”
Section: Cases Of Ml‐assisted Decoding Of Optical Datamentioning
Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML‐based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science, and ML.
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