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

Depciuch, Joanna; Jakubczyk, Paweł; Paja, Wiesław; Pancerz, Krzysztof; Wosiak, Agnieszka; Kula-Maximenko, Monika; Yaylım, İlhan; Tarhan, Nevzat; Hakan, Mehmet Tolgahan; Sönmez, Dilara; Sarıbal, Devrim; Arıkan, Soykan; Güleken, Zozan

doi:10.1016/j.nano.2023.102657

Cited by 5 publications

(4 citation statements)

References 58 publications

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“…However, their results refer to the 10-fold cross-validation of the whole spectral dataset. In addition, J. Depciuch et al [19] analyzed Raman data collected from 75 blood serum samples of healthy and colon cancer patients using three machine learning methods (Deep Learning, SVM, and eXtreme Gradient Boosting trees) in order to determine the efficiency of discrimination of sick and healthy people. They obtained accuracy values ranging from 59% to 96%, and both depended on the investigated spectral range and on the number of selected spectral features.…”

Section: Spectral Position (Cmmentioning

confidence: 99%

“…The SVM algorithm uses the input data, belonging to known classes, to identify a hyperplane in the space of the selected spectral features, which optimize the separation of data belonging to different classes; next, the projection of unknown data onto the hyperplane allows them to be classified correctly [10,15]. The SVM algorithm was able to detect colorectal cancer by analyzing the Raman spectra of blood serum samples from 75 patients; in particular, the analysis of 43 properly selected spectral features in the 800-3000 cm −1 range allowed the investigated samples to be classified with accuracy, sensitivity, and specificity values of 96%, 93%, and 98%, respectively [19]. X. Fang et al implemented the surface enhanced Raman technique with SVM method to distinguish lung cancer cells from normal cells including blood cells and immortalized lung cells; they achieved a classification accuracy between 98.8% and 100% for differentiation of cancer cells from normal ones [20].…”

Section: Introductionmentioning

confidence: 99%

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Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2024

Photonics

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Colorectal cancer is very widespread in developed countries. Its diagnosis partly depends on pathologists’ experience and their laboratories’ instrumentation, producing uncertainty in diagnosis. The use of spectroscopic techniques sensitive to the cellular biochemical environment could aid in achieving a reliable diagnosis. So, we used Raman micro-spectroscopy, combined with a spectral analysis by means of machine learning methods, to build classification models, which allow colon cancer to be diagnosed in cell samples, in order to support such methods as complementary tools for achieving a reliable identification of colon cancer. The Raman spectra were analyzed in the 980–1800 cm−1 range by focusing the laser beam onto the nuclei and the cytoplasm regions of single FHC and CaCo-2 cells (modelling healthy and cancerous samples, respectively) grown onto glass coverslips. The comparison of the Raman intensity of several spectral peaks and the Principal Component Analysis highlighted small biochemical differences between healthy and cancerous cells mainly due to the larger relative lipid content in the former cells with respect to the latter ones and to the larger relative amount of nucleic acid components in cancerous cells compared with healthy ones. We considered four classification algorithms (logistic regression, support vector machine, k nearest neighbors, and a neural network) to associate unknown Raman spectra with the cell type to which they belong. The built machine learning methods achieved median values of classification accuracy ranging from 95.5% to 97.1%, sensitivity values ranging from 95.5% to 100%, and specificity values ranging from 93.9% to 97.1%. The same median values of the classification parameters, which were estimated for a testing set including unknown spectra, ranged between 93.1% and 100% for accuracy and between 92.9% and 100% for sensitivity and specificity. A comparison of the four methods pointed out that k nearest neighbors and neural networks better perform the classification of nucleus and cytoplasm spectra, respectively. These findings are a further step towards the perspective of clinical translation of the Raman technique assisted by multivariate analysis as a support method to the standard cytological and immunohistochemical methods for diagnostic purposes.

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Section: Spectral Position (Cmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2024

Photonics

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“…Colorectal cancer (CRC) is a prevalent disease that threatens public health, as it affects many people globally [ 3 ]. Globally, it ranked third in terms of prevalence and second in terms of death rate [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Gabralla,

Hussien,

AlMohimeed

et al. 2023

Diagnostics

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Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI).

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“…partial least squares discriminant analysis and support vector machine (SVM), were used for early detection of gastric cancer [15]. As reported in [16], deep learning (DL), SVM, and extreme gradient boosting trees were used with Raman spectroscopy to determine tumor markers for colon cancer. In addition, the combination of DL and Raman spectroscopy allows rapid detection of melanoma at the single cell level [17].…”

Section: Introductionmentioning

confidence: 99%

Using random forest for brain tissue identification by Raman spectroscopy

Zhang,

Giang,

Cai

et al. 2023

Mach. Learn.: Sci. Technol.

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The traditional definitive diagnosis of brain tumors is performed by needle biopsy under the guidance of imaging-based exams. This paradigm is based on the experience of radiogolists, and accuracy could be affected by uncertainty in imaging interpretation and needle placement. Raman spectroscopy has the potential to improve needle biopsy by providing fingerprints of different materials and performing in situ tissue identification. In this paper, we present the development of a supervised machine learning algorithm using random forest (RF) to distinguish the Raman spectrum of different types of tissue. An integral process from raw data collection and preprocessing to model training and evaluation is presented. To illustrate the feasibility of this approach, viable animal tissues were used, including ectocinerea (grey matter), alba (white matter) and blood vessels. Raman spectra were acquired using a custom-built Raman spectrometer. The hyperparameters of the RF model were determined by combining a cross-validation-based algorithm and manually adjusting. The experimental results show the ability of our approach to discriminate different types of tissues with high accuracy.

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

Cited by 5 publications

References 58 publications

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Classifying Raman Spectra of Colon Cells Based on Machine Learning Algorithms

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Using random forest for brain tissue identification by Raman spectroscopy

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