Raman spectroscopy in lung cancer diagnostics: Can an in vivo setup compete with ex vivo applications?

Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis. In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics. During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8-94.6%) and 100.0% (95% CI, 92.1-100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.

Efficacy of blood plasma spectroscopy for early liver cancer diagnostics in obese patients.

INTRODUCTION AND OBJECTIVES: Hepatocellular carcinoma (HCC) represents one of the most common cancers worldwide. A considerable proportion of HCC is caused by cirrhosis related to metabolic dysfunction-associated steatohepatitis (MASH). Due to the increasing prevalence of metabolic syndrome, it is estimated that MASH-related HCC will become the most prevalent etiology of HCC. Currently, HCC screening is based on liver ultrasonography; however, the sensitivity of ultrasonography for early HCC stages in obese patients only reaches 23 %. To date, no studied biomarker shows sufficient efficacy for screening purposes. Nevertheless, the usage of spectroscopic methods offers a new perspective, as its potential use would provide cheap, fast analysis of samples such as blood plasma. MATERIAL AND METHODS: We employed a combination of conventional and chiroptical spectroscopic methods to study differences between the blood plasma of obese cirrhotic patients with and without HCC. We included 20 subjects with HCC and 17 without evidence of liver cancer, all of them with body mass index ≥ 30. RESULTS: Sensitivities and specificities reached values as follows: 0.780 and 0.905 for infrared spectroscopy, 0.700 and 0.767 for Raman spectroscopy, 0.840 and 0.743 for electronic circular dichroism, and 0.805 and 0.923 for Raman optical activity. The final combined classification model based on all spectroscopic methods reached a sensitivity of 0.810 and a specificity of 0.857, with the highest area under the receiver operating characteristic curve among all models (0.961). CONCLUSIONS: We suggest that this approach can be used effectively as a diagnostic tool in patients who are not examinable by liver ultrasonography. CLINICAL TRIAL REGISTRATION: NCT04221347.

Automated classification pipeline for real-time in vivo examination of colorectal tissue using Raman spectroscopy.

Colorectal cancer is the third most common malignancy worldwide and one of the leading causes of death in oncological patients with its diagnosis typically involving confirmation by tissue biopsy. In vivo Raman spectroscopy, an experimental diagnostic method less invasive than a biopsy, has shown great potential to discriminate between normal and cancerous tissue. However, the complex and often manual processing of Raman spectra along with the absence of a suitable instant classifier are the main obstacles to its adoption in clinical practice. This study aims to address these issues by developing a real-time automated classification pipeline coupled with a user-friendly application tailored for non-spectroscopists. First, in addition to routine colonoscopy, 377 subjects underwent in vivo acquisitions of Raman spectra of healthy tissue, adenomatous polyps, or cancerous tissue, which were conducted using a custom-made microprobe. The spectra were then loaded into the pipeline and pre-processed in several steps, including standard normal variate transformation and finite impulse response filtration. The quality of the pre-processed spectral data was checked based on their signal-to-noise ratio before the suitable spectra were decomposed and classified using a combination of principal component analysis and a support vector machine, respectively. After five-fold cross-validation, the developed classifier exhibited 100% sensitivity toward adenocarcinoma and adenomatous polyps. The overall accuracy was 96.9% and 79.2% for adenocarcinoma and adenomatous polyps respectively. In addition, an application with a graphical user interface was developed to facilitate the use of our data pipeline by medical professionals in a clinical environment. Overall, the combination of supervised and unsupervised machine learning with algorithmic pre-processing of in vivo Raman spectra appears to be a viable way of reducing the relatively large number of biopsies currently needed to definitively diagnose colorectal cancer.

In vivo Raman spectroscopy in the diagnostics of colon cancer.

Early detection and accurate diagnosis of colorectal carcinoma are crucial for successful treatment, yet current methods can be invasive and even inaccurate in some cases. In this work, we present a novel approach for in vivo tissue diagnostics of colorectal carcinoma using Raman spectroscopy. This almost non-invasive technique allows for fast and accurate detection of colorectal carcinoma and its precursors, adenomatous polyps, enabling timely intervention and improved patient outcomes. Using several methods of supervised machine learning, we were able to achieve over 91% accuracy in distinguishing colorectal lesions from healthy epithelial tissue and more than 90% classification accuracy for premalignant adenomatous polyps. Moreover, our models enabled the discrimination of cancerous and precancerous lesions with a mean accuracy of almost 92%. Such results demonstrate the potential of in vivo Raman spectroscopy to become a valuable tool in the fight against colon cancer.

Vibrational and chiroptical analysis of blood plasma for hepatocellular carcinoma diagnostics.

More than one fifth of the world's population suffers from liver cirrhosis or other chronic liver diseases. Unfortunately, some of them will inevitably develop hepatocellular carcinoma (HCC), due to the vast majority of HCC cases arising against the background of liver cirrhosis. Despite this clearly identified high-risk group, the lack of early diagnostic options causes HCC mortality to approach its incidence. As opposed to many types of cancer, the incidence of HCC is expected to grow in the coming decades, which makes the search for an effective early diagnostic option a pressing necessity. This study presents evidence that blood plasma analysis employing a combination of chiroptical and vibrational spectroscopic methods might be the key to the improvement of the current status. One hundred samples of patients with HCC and controls with cirrhosis were classified using principal component analysis together with a random forest algorithm. Differentiation of the specific spectral patterns of the studied groups was successful in more than 80%, indicating the prospect of including spectroscopy in the screening of high-risk groups, such as patients with cirrhosis.

Early Detection of Pancreatic Cancer in Type 2 Diabetes Mellitus Patients Based on 1H NMR Metabolomics.

The association of pancreatic cancer with type 2 diabetes mellitus was investigated by 1H NMR metabolomic analysis of blood plasma. Concentration data of 58 metabolites enabled discrimination of pancreatic cancer (PC) patients from healthy controls (HC) and long-term type 2 diabetes mellitus (T2DM) patients. A panel of eight metabolites was proposed and successfully tested for group discrimination. Furthermore, a prediction model for the identification of at-risk individuals for future development of pancreatic cancer was built and tested on recent-onset diabetes mellitus (RODM) patients. Six of 59 RODM samples were assessed as PC with an accuracy of more than 80%. The health condition of these individuals was re-examined, and in four cases, a correlation to the prediction was found. The current health condition can be retrospectively attributed to misdiagnosed pancreatogenic diabetes or to early-stage pancreatic cancer.

Diagnosis of pancreatic cancer via1H NMR metabolomics of human plasma.

Metabolic changes induced by pancreatic cancer were investigated by 1H NMR spectroscopy of plasma samples of patients and healthy controls. The acquired data were submitted to multivariate statistical analysis providing clear discrimination between both groups. The most significant differences were found in levels of 3-hydroxybutyrate and lactate. The obtained results (100% sensitivity, 90% specificity) clearly show the potential of 1H NMR spectroscopy in pancreatic cancer diagnosis. Therefore, the NMR-based metabolomics may contribute to the early diagnosis, prevention and/or therapy of diseases in the future. On the other hand, the number of samples in the presented pilot study is limited and has to be significantly increased in the future in order to obtain solid statistical models and to confirm the current findings.

Chiroptical spectroscopy and metabolomics for blood-based sensing of pancreatic cancer.

To enable the early diagnosis of pancreatic cancer, the search for and definition of reliable biomarkers remain a subject of great interest, with the specificity and sensitivity of the currently used biomarkers being below the required values. We tested a novel diagnostic approach for pancreatic cancer based on the specific molecular signature of blood plasma components. To acquire more detailed structural information, structure-sensitive chiroptical methods (electronic circular dichroism and Raman optical activity) were supplemented by conventional Raman and infrared spectroscopies. The obtained spectra were subsequently processed by linear discriminant analysis yielding high values of specificity and sensitivity. In addition, to monitor not only large biomolecules as potential biomarkers but also those of low molecular weight, we conducted an analysis of blood plasma samples by using metabolomics. The achieved results suggest a panel of promising biomarkers for a reliable detection of pancreatic cancer.