Noninvasive cholangitis and cholangiocarcinoma screening based on serum Raman spectroscopy and support vector machine

Su, Na; Dawuti, Wubulitalifu; Hu, Yan; Zhao, Hui

doi:10.1016/j.pdpdt.2022.103156

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…Making use of patients’ serum samples, Su et al. ( 47 ) applied Raman spectroscopy to identify typical biomolecular components in CCA and achieved good diagnostic accuracy combined with the SVM algorithm. Giordano et al.…”

Section: Ai and Diagnosismentioning

confidence: 99%

“…Spectroscopy is considered a useful method for identifying subtle changes associated with cancer development in biological samples (45,46). Making use of patients' serum samples, Su et al (47) applied Raman spectroscopy to identify typical biomolecular components in CCA and achieved good diagnostic accuracy combined with the SVM algorithm. Giordano et al (48) also applied mass spectrometry with SVM and RF to permit cholangiocarcinoma identification, with an accuracy of 99.0%, sensitivity of 98%, and specificity of 100%.…”

Section: Ai and Diagnosismentioning

confidence: 99%

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Application of AI on cholangiocarcinoma

Huang,

Bai,

Qiu

et al. 2024

Front. Oncol.

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Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

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Section: Ai and Diagnosismentioning

confidence: 99%

Section: Ai and Diagnosismentioning

confidence: 99%

Application of AI on cholangiocarcinoma

Huang,

Bai,

Qiu

et al. 2024

Front. Oncol.

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show abstract

“…Raman spectral data can then be combined with ML algorithms and multivariate analysis to enable accurate diagnostics of disease progression and discrimination between disease subtypes. By probing these metabolites, RS has characterized tissue, − cells, − and biofluids , including tears, − urine, saliva, − serum, − and plasma. − However, a limitation of spontaneous RS is the time required for a reasonable SNR, which limits both the ability for subcellular imaging and to monitor processes in real time. Both of these characteristics are necessary to understand the mechanistic pathway of metabolic changes.…”

Section: Introductionmentioning

confidence: 99%

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Cutshaw,

Uthaman,

Hassan

et al. 2023

Chem. Rev.

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Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

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Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease

Yang,

Chen,

Luo

et al. 2023

Sci Rep

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Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren’s syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients.

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Noninvasive cholangitis and cholangiocarcinoma screening based on serum Raman spectroscopy and support vector machine

Cited by 5 publications

References 51 publications

Application of AI on cholangiocarcinoma

Application of AI on cholangiocarcinoma

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease

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