Abstract:Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop noninvasive methods for the early detection … Show more
“…This is the first time; breast cancer biopsy data was explored using AI and ML approaches based on subtype classification. AI approach studies have identified malignant tissues from normal and benign in various cancers such as lung cancer [54,60], skin cancer [61,62], brain cancer [18,63], gastro intestinal cancer [64,65], and oral cancer [66].…”
Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.
ARTICLE HISTORY
“…This is the first time; breast cancer biopsy data was explored using AI and ML approaches based on subtype classification. AI approach studies have identified malignant tissues from normal and benign in various cancers such as lung cancer [54,60], skin cancer [61,62], brain cancer [18,63], gastro intestinal cancer [64,65], and oral cancer [66].…”
Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.
ARTICLE HISTORY
“…18,19 It can directly detect tumor tissue samples and quickly determine the internal molecular structure and characteristics of the samples, with the ability to identify subtle molecular changes in biological components even at the initial stage of the lesions. 20 Our team has performed Raman spectroscopy to detect breast cancer microcalcification, thus confirming its diagnostic utility. 13,21,22 Compared with normal tissues, the C-O bond stretching vibration of the b-sheet of amide I band (1610 cm À1 ) in protein appeared in breast AH tissues, indicating that the C-O group in the protein was severely damaged when the breast epithelial cells became cancerous, resulting in destruction of the protein conformation space.…”
Objective To identify atypical hyperplasia (AH) of the breast by shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), and to explore the molecular fingerprinting characteristics of breast AH. Methods Breast hyperplasia was studied in 11 hospitals across China from January 2015 to December 2016. All patients completed questionnaires on women’s health. The differences between patients with and without breast AH were compared. AH breast lesions were detected by Raman spectroscopy followed by the SHINERS technique. Results There were no significant differences in clinical features and risk-related factors between patients with breast AH (n = 37) and the control group (n = 2576). Fifteen cases of breast AH lesions were detected by Raman spectroscopy. The main different Raman peaks in patients with AH appeared at 880, 1001, 1086, 1156, 1260, and 1610 cm−1, attributed to the different vibrational modes of nucleic acids, β-carotene, and proteins. Shell-isolated nanoparticles had different enhancement effects on the nucleic acid, protein, and lipid components in AH. Conclusion Raman spectroscopy can detect characteristic molecular changes in breast AH lesions, and may thus be useful for the non-invasive early diagnosis and for investigating the mechanism of tumorigenesis in patients with breast AH.
“…Moreover, in the progressive stages of cancer, the epithelial tissue thickness increases, that results in reduced amount of light delivery to the collagen fiber of connective tissue, which ultimately alters the scattering coefficient and the diffuse reflectance spectral intensity. 68 Therefore, DRS is a sensitive tool, enabling diagnosis of cervical cancer, 69 gastrointestinal cancer, 70 skin cancer, 22 colon cancer, 71 breast cancer, 72 brain tumor, 73 lung cancer, 74 and renal carcinoma, 75 because they rely on the tissues biochemical and morphological feature. In the following section, we shortly describe the application of DRS for certain types of cancer diagnosis, without covering all the above-mentioned cancers.…”
Diffuse reflectance spectroscopy is a widely used technique for medical applications that may analyze the optical characteristics of biological tissues. By using diffuse reflectance spectroscopy, different tissue types can be distinguished based on specific changes on reflected light spectrum that are a result of differences on a molecular level between compared tissues. Identification of the structural features of tissue can be performed by applying diffuse reflectance spectroscopy, and the spectra obtained from this technique could provide important diagnostic information about the tissue morphology and physiology. Moreover, different tissue types can be classified using diffuse reflectance spectroscopy, during surgery on the basis of their optical properties that are related to the tissue morphology and constituents. In recent years, several research groups have been shown the feasibility of diffuse reflectance spectroscopy in discriminating benign and malignant tissue, and thus making it a good competitor for margin assessment. Therefore, the diffuse reflectance spectroscopy has the possibility to become an important optical means for disease diagnosis, treatment and prognosis monitoring. This review represents a summary of the literature on diffuse reflectance spectroscopy and its important clinical applications.
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