Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum

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.

A Review on Emerging Techniques for Diagnosis of Colorectal Cancer

Common detection methods in practice for diagnosing colorectal cancer (CRC) are painful and invasive leading to less participation of individuals for CRC diagnosis. Whereas, improved or enhanced imaging systems and other minimally invasive techniques with shorter detection times deliver greater detail and less discomfort in individuals. Thus, this review is a summary of the diagnostic tests, ranging from the simple potential use in developing a flexible CRC treatment to the patient's potential benefits in receiving less invasive procedures and the advanced treatments that might provide a better assessment for the diagnosis of CRC and reduce the mortality related to CRC.

Comparative analysis of serum and saliva samples using Raman spectroscopy: a high-throughput investigation in patients with polycystic ovary syndrome and periodontitis

BACKGROUND

Polycystic ovary syndrome (PCOS) and periodontitis significantly affect women's oral and systemic health worldwide, and yet increase the risk of cardiovascular-metabolic diseases like diabetes and coronary heart disease. Regarding the PCOS-periodontitis connection, whether sex hormones, metabolic and inflammatory mediators could account for the underlying linking mechanism needs to be further investigated. This case-control study evaluated the hormonal, metabolic and inflammatory profiles in PCOS and non-PCOS subjects with various periodontal conditions, via assessing serum and saliva samples by Raman spectroscopy.

METHODS

A total of 66 females with PCOS and 22 systemically healthy female volunteers were recruited in a single hospital. Full-mouth periodontal examination was undertaken for identifying the subjects with periodontal health, gingivitis or periodontitis. The datasets of sex hormones and metabolic indicators were retrieved from the hospital information system. Both serum and saliva samples were collected for detecting inflammatory mediators and Raman spectroscopic assessment. The subjects were categorized into four groups according to their conditions of PCOS and periodontitis for Raman spectroscopic analysis. Partial least squares discriminant analysis was performed to examine the inter-group differences in Raman spectra.

RESULTS

PCOS patients exhibited greater mean probing depth (P < 0.05) and higher serum levels of triglycerides (P < 0.05) and matrix metalloproteinase-8 (P < 0.05) than those in non-PCOS participants. Both probing depth and triglyceride level were positively correlated with PCOS (P < 0.05). There was a significant difference in mean Raman spectra of saliva samples among the four groups with different conditions of PCOS and periodontitis (P < 0.05), while no significant inter-group difference existed in serum samples.

CONCLUSIONS

The present study shows that periodontal condition may affect the biomolecular profiles of Raman spectra in serum and saliva of PCOS patients. It underscores the importance of the collaborative teamwork of dentists and gynecologists for enhancing women's oral health, general wellbeing and quality of life.

An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis

Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Determination of esophageal squamous cell carcinoma and gastric adenocarcinoma on raw tissue using Raman spectroscopy

BACKGROUND

Cancer detection is a global research focus, and novel, rapid, and label-free techniques are being developed for routine clinical practice. This has led to the development of new tools and techniques from the bench side to routine clinical practice. In this study, we present a method that uses Raman spectroscopy (RS) to detect cancer in unstained formalin-fixed, resected specimens of the esophagus and stomach. Our method can record a clear Raman-scattered light spectrum in these specimens, confirming that the Raman-scattered light spectrum changes because of the histological differences in the mucosal tissue.

AIM

To evaluate the use of Raman-scattered light spectrum for detecting endoscop-ically resected specimens of esophageal squamous cell carcinoma (SCC) and gastric adenocarcinoma (AC).

METHODS

We created a Raman device that is suitable for observing living tissues, and attempted to acquire Raman-scattered light spectra in endoscopically resected specimens of six esophageal tissues and 12 gastric tissues. We evaluated formalin-fixed tissues using this technique and captured shifts at multiple locations based on feasibility, ranging from six to 19 locations 200 microns apart in the vertical and horizontal directions. Furthermore, a correlation between the obtained Raman scattered light spectra and histopathological diagnosis was performed.

RESULTS

We successfully obtained Raman scattered light spectra from all six esophageal and 12 gastric specimens. After data capture, the tissue specimens were sent for histopathological analysis for further processing because RS is a label-free methodology that does not cause tissue destruction or alterations. Based on data analysis of molecular-level substrates, we established cut-off values for the diagnosis of esophageal SCC and gastric AC. By analyzing specific Raman shifts, we developed an algorithm to identify the range of esophageal SCC and gastric AC with an accuracy close to that of histopathological diagnoses.

CONCLUSION

Our technique provides qualitative information for real-time morphological diagnosis. However, further in vivo evaluations require an excitation light source with low human toxicity and large amounts of data for validation.

Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELİSA method. Raman spectra showed higher Raman intensities at 1447 cm^-1 1560 cm^-1, 1665 cm^-1, and 1769 cm^-1, which originated from CH₂ proteins and lipids, amide II and amide I, and CO lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm^-1 and 1306 cm^-1 can be used as spectroscopy markers of colon cancer.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review

Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.

Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches

Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.