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.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma.

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

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.

Search for biomarkers of Alzheimer's disease: Recent insights, current challenges and future prospects.

Due to the trend of prolonged lifespan leading to higher incidence of age-related diseases, the demand for reliable biomarkers of dementia rises. In this review, we present novel biomarkers of high potential, especially those found in blood, urine or saliva, which could lead to a more comfortable patient experience and better time- and cost-effectivity, compared to the currently used diagnostic methods. We focus on biomarkers that might allow for the detection of Alzheimer's disease before its clinical manifestations. Such biomarkers might be helpful for better understanding the etiology of the disease and identifying its risk factors. Moreover, it could be a base for developing new treatment or at least help to prolong the presymptomatic stage in patients suffering from Alzheimer's disease. As potential candidates, we present, for instance, neurofilament light in both cerebrospinal fluid and blood plasma or amyloid ß in plasma. Above all, we provide an overview of different approaches to the diagnostics, analyzing patient's biofluids as a whole using molecular spectroscopy. Infrared and Raman spectroscopy and especially chiroptical methods provide information not only on the chemical composition, but also on molecular structure. Therefore, these techniques are promising for the diagnostics of Alzheimer's disease, as the accumulation of amyloid ß in abnormal conformation is one of the hallmarks of this disease.