Raman spectroscopy combined with multivariate analysis to study the biochemical mechanism of lung cancer microwave ablation

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study

BACKGROUND

Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.

METHODS

Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.

RESULTS

The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.

CONCLUSION

Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Enhanced recovery after surgery for percutaneous CT-guided microwave ablation of lung tumors: A single-center retrospective cohort study

BACKGROUND

The feasibility and safety of enhanced recovery after surgery (ERAS) for percutaneous computed tomography (CT)-guided microwave ablation (MWA) for treating lung nodules remain unclear.

METHODS AND MATERIALS

A total of 409 patients with lung tumors treated at the Department of Thoracic Surgery, First Affiliated Hospital of Guangxi Medical University from August 2020 to May 2023 were enrolled. Perioperative data, including baseline characteristics, operation time, postoperative pain score (visual analog scale [VAS]), hospitalization expenses, postoperative complications, total hospital stay, and patient satisfaction, were observed and recorded.

RESULTS

No perioperative mortality occurred in either group and complete ablation was achieved in all patients. Patients in the ERAS group had significantly shorter hospital stays (P < 0.001), reduced operation times (P = 0.047), lower hospitalization expenses (P < 0.001), lower VAS scores (P < 0.001), and fewer complications (P = 0.047) compared with the traditional group.

CONCLUSIONS

ERAS for percutaneous CT-guided MWA (ERAA) is safe, effective, and feasible for the treatment of lung nodules.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

Inflammation and fibrosis in the coal dust-exposed lung described by confocal Raman spectroscopy

Background

Coal workers' pneumoconiosis (CWP) is an occupational disease that severely damages the life and health of miners. However, little is known about the molecular and cellular mechanisms changes associated with lung inflammation and fibrosis induced by coal dust. As a non-destructive technique for measuring biological tissue, confocal Raman spectroscopy provides accurate molecular fingerprints of label-free tissues and cells. Here, the progression of lung inflammation and fibrosis in a murine model of CWP was evaluated using confocal Raman spectroscopy.

Methods

A mouse model of CWP was constructed and biochemical analysis in lungs exposed to coal dust after 1 month (CWP-1M) and 3 months (CWP-3M) vs control tissues (NS) were used by confocal Raman spectroscopy. H&E, immunohistochemical and collagen staining were used to evaluate the histopathology alterations in the lung tissues.

Results

The CWP murine model was successfully constructed, and the mouse lung tissues showed progression of inflammation and fibrosis, accompanied by changes in NF-κB, p53, Bax, and Ki67. Meanwhile, significant differences in Raman bands were observed among the different groups, particularly changes at 1,248, 1,448, 1,572, and 746 cm^-1. These changes were consistent with collagen, Ki67, and Bax levels in the CWP and NS groups.

Conclusion

Confocal Raman spectroscopy represented a novel approach to the identification of the biochemical changes in CWP lungs and provides potential biomarkers of inflammation and fibrosis.

Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.

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

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.

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning

Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.

Accurate diagnosis of lung tissues for 2D Raman spectrogram by deep learning based on short-time Fourier transform

Multivariate statistical analysis methods have an important role in spectrochemical analyses to rapidly identify and diagnose cancer and the subtype. However, utilizing these methods to analyze lager amount spectral data is challenging, and poses a major bottleneck toward achieving high accuracy. Here, a new convolutional neural networks (CNN) method based on short-time Fourier transform (STFT) to diagnose lung tissues via Raman spectra readily is proposed. The models yield that the accuracies of the new method are higher than the conventional methods (principal components analysis -linear discriminant analysis and support vector machine) for validation group (95.2% vs 85.5%, 94.4%) and test group (96.5% vs 90.4%, 93.9%) after cross-validation. The results illustrate that the new method which converts one-dimensional Raman data into two-dimensional Raman spectrograms improve the discriminatory ability of lung tissues and can achieve automatically accurate diagnosis of lung tissues.

Confocal Raman microspectral analysis and imaging of the drug response of osteosarcoma to cisplatin

Confocal Raman microspectral analysis and imaging were used to elucidate the drug response of osteosarcoma (OS) to cisplatin. Raman spectral data were obtained from OS cells that were untreated (UT group) and treated with 20 µM (20T group) and 40 µM (40T group) cisplatin for 24 hours. Statistical analysis of the changes in specific Raman signals was performed using a one-way ANOVA and multiple Tukey's honest significant difference (HSD) post hoc tests. Principal component analysis-linear discriminant analysis (PCA-LDA) was used to highlight the featured cellular drug responses based on the obtained spectral information. For spectral imaging analysis, k-means cluster analysis (KCA) was adopted to clarify the effect of cisplatin dose changes on the subcellular structure and its biochemical composition. The results suggest that the major biochemical changes induced by cisplatin in OS cells undergoing apoptosis are reduced protein and nucleic acid content. Through univariate analysis, the changes in the distribution of nucleic acids in OS cells induced by different doses of cisplatin were obtained. The combination of Raman spectroscopy and multivariate analysis shows that cisplatin mainly acts on the nucleus and causes changes in the secondary structure of proteins. These results indicate that Raman imaging technology has the potential to offer the basis of dose optimization for personalized cancer treatment by helping to understand in vitro cellular drug interactions.

A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition

It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user-friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky-Golay smoothing, area-under-curve normalization, mean-centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares-discriminant analysis, support vector machine (SVM), and PCA-SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open-source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.

The efficacy of Raman spectroscopy in lung cancer diagnosis: the first diagnostic meta-analysis

In recent years, many researches have explored the diagnostic value of Raman spectroscopy in multiple types of tumors. However, as an emerging clinical examination method, the diagnostic performance of Raman spectroscopy in lung cancer remains unclear. Relevant diagnostic studies published before 1 June 2020 were retrieved from the Cochrane Library, PubMed, EMBASE, China National Knowledge Internet (CNKI), and WanFang databases. After the literature was screened, two authors extracted the data from eligible studies according to the inclusion and exclusion criteria. Obtained data were pooled and analyzed using Stata 16.0, Meta-DiSc 1.4, and RevMan 5.3 software. Fourteen diagnostic studies were eligible for the pooled analysis which includes 779 patients. Total pooled sensitivity and specificity of Raman spectroscopy in diagnosing lung cancer were 0.92 (95% CI 0.87-0.95) and 0.94 (95% CI 0.88-0.97), respectively. The positive likelihood ratio was 15.2 (95% CI 7.5-30.9), the negative likelihood ratio was 0.09 (95% CI 0.05-0.14), and the area under the curve was 0.97 (95 % CI 0.95-0.98). Subgroup analysis suggested that the sensitivity and specificity of RS when analyzing human tissue, serum, and saliva samples were 0.95 (95% CI 0.88-0.98), 0.97 (95% CI 0.89-0.99), 0.88 (95% CI 0.80-0.93), 0.87 (95% CI 0.78-0.92), 0.91 (95% CI 0.80-0.96), and 0.95 (95% CI 0.73-0.99), respectively. No publication bias or threshold effects were detected in this meta-analysis. This initial meta-analysis indicated that Raman spectroscopy is a highly specific and sensitive diagnostic technology for detecting lung cancer. Further investigations are also needed to focus on real-time detection using Raman spectroscopy under bronchoscopy in vivo. Moreover, large-scale diagnostic studies should be conducted to confirm this conclusion.

Raman Microspectroscopic Investigation and Classification of Breast Cancer Pathological Characteristics

Breast cancer is one of the major cancers of women in the world. Despite significant progress in its treatment, an early diagnosis can effectively reduce its incidence rate and mortality. To improve the reliability of Raman-based tumor detection and analysis methods, we conducted an ex vivo study to unveil the compositional features of healthy control (HC), solid papillary carcinoma (SPC), mucinous carcinoma (MC), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissue samples. Following the identification of biological variations occurring as a result of cancer invasion, principal component analysis followed by linear discriminate analysis (PCA-LDA) algorithm were adopted to distinguish spectral variations among different breast tissue groups. The achieved results confirmed that after training, the constructed classification model combined with the leave-one-out cross-validation (LOOCV) method was able to distinguish the different breast tissue types with 100% overall accuracy. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.