Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

Yang, Xien; Ou, Qiongrong; Qian, Kai; Yang, Jianru; Bai, Zhixun; Yang, Weiye; Shi, Yuhua; Liu, Gang

doi:10.3389/fonc.2021.753791

Cited by 20 publications

(15 citation statements)

References 38 publications

(48 reference statements)

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“…The peak at

assigned to the amide group is also higher in PCa patients, which implies an increase of protein concentration, and is consistent with the literature that found higher amide peak in breast cancer, 48 lung cancer, 49 and colorectal cancer tissues. 50 One of the proposed explanations for this difference is that it appears to inform on biological changes of malignancy such as increased alterations in protein structures and protein unfolding.…”

Section: Discussionsupporting

confidence: 91%

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization

et al. 2022

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. Significance: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. Aim: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. Approach: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ ( in vivo ) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. Results: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). Conclusions: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

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“…The peak at

Section: Discussionsupporting

confidence: 91%

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization

et al. 2022

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“…FTIR spectra of serum were measured in the range of 4000–600 cm −1 by a Frontier spectrometer (Perkin Elmer) with the same test method as in our previous work ( Yang et al, 2021a ). Each IR spectrum was an accumulation of 32 scans at a resolution of 4 cm −1 .…”

Section: Methodsmentioning

confidence: 99%

“…Vibrational spectroscopy is an important tool in the field of analytical chemistry and bioanalysis, of which Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy have been widely used in cancer diagnosis in recent years ( Auner et al, 2018 ; Christensen et al, 2019 ; Baiz et al, 2020 ). In our previous work, we studied the serum samples of patients with lung cancer and healthy people using FTIR spectroscopy and found that the concentrations of protein, lipid, and nucleic acid molecules in the serum of patients with lung cancer were higher than those of healthy people ( Yang et al, 2021a ). Song et al classified the tissues of healthy people and patients with lung squamous cell carcinoma using Raman spectroscopy combined with principal component analysis–linear discriminant analysis (PCA-LDA) ( Song et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform

Yang

et al. 2022

Front. Chem.

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Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.

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“…The standard normal variate (SNV) normalization was employed to normalize the spectra [46]. Regions of 3000-2800 cm −1 (lipid), 1700-1500 cm −1 (protein), and 1270-960 cm −1 (nucleic acid) were selected for analysis [39,47]. The spectral band area calculation was done with SpectraGryph version 1.2.14 (Dr. Friedrich Menges, Oberstdorf, Germany).…”

Section: Spectral Band Area Analysismentioning

confidence: 99%

“…Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy has emerged as a powerful tool in the medical diagnostic field [36,37]. ATR-FTIR has been extensively explored to identify molecular vibrations of biomolecules in various biological samples that are useful for monitoring health status [38,39]. Some advantages provided by ATR-FTIR include the ease of handling samples with relatively short measurement duration (only a few minutes), a small amount of required sample volume, a reagent-free approach, and high signal to noise ratio output that facilitates chemometric analysis.…”

Section: Introductionmentioning

confidence: 99%

Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells

Praja

Wongwattanakul

Tippayawat

et al. 2022

Cells

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In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700–1500 cm−1 region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700–1500 cm−1 region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800–900 cm−1. Exosome spectra in the 1700–1500 and combined 3000–2800 and 1800–900 cm−1 regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800–900 cm−1 region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

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Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

Cited by 20 publications

References 38 publications

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform

Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Discriminates the Elderly with a Low and High Percentage of Pathogenic CD4+ T Cells

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