Use of ATR-FTIR spectroscopy to differentiate between cirrhotic/non-cirrhotic HCV patients

Ali, Salmann; Naveed, Ammara; Hussain, Irshad; Qazi, Javaria

doi:10.1016/j.pdpdt.2023.103529

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Recently, urine surface-enhanced Raman spectroscopy combined with the SVM algorithm enabled the diagnosis of liver cirrhosis and hepatocellular carcinoma with accuracy levels of 85.9% and 84.8%, respectively [34]. Also, FTIR of serum samples, in conjunction with the SVM algorithm, proved to be a sensitive tool for the detection of HCV infection and to assess the non-cirrhotic/cirrhotic status of patients [35].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2023

Applied Sciences

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Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator’s skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000–1760 cm−1) and in the high-wavenumber range (2700–3700 cm−1). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection.

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Section: Introductionmentioning

confidence: 99%

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2023

Applied Sciences

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“…The evaluation of the reported studies’ publication quality identified the most common gap in publications as the lack of external validation, which was conducted by only two studies [ 13 , 49 ]. Twelve of the reported studies predefined the success criteria for model performance [ 15 , 19 , 22 , 25 , 26 , 36 , 38 , 41 , 43 , 47 , 48 ] and nine studies discussed the generalizability of the model [ 14 , 17 , 20 , 27 , 29 , 33 , 35 , 42 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

et al. 2023

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Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient–provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019–2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles’ search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019–2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

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Diagnosis and monitoring of hepatocellular carcinoma in Hepatitis C virus patients using attenuated total reflection Fourier transform infrared spectroscopy

Ali,

Naveed,

Hussain

et al. 2023

Photodiagnosis and Photodynamic Therapy

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Use of ATR-FTIR spectroscopy to differentiate between cirrhotic/non-cirrhotic HCV patients

Cited by 3 publications

References 37 publications

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Diagnosis and monitoring of hepatocellular carcinoma in Hepatitis C virus patients using attenuated total reflection Fourier transform infrared spectroscopy

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