Classifying low-grade and high-grade bladder cancer using label-free serum surface-enhanced Raman spectroscopy and support vector machine

Lai, Xiaoping; Zeng, Qiuyao; Li, Linfang; Lin, Lin; Li, Shaoxin; Liu, Zhiming; Su, Chengkang; Qi, Minni; Guo, Zhouyi

doi:10.1088/1555-6611/aa9d6d

Cited by 24 publications

(15 citation statements)

References 38 publications

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“…Meanwhile, the mentioned methods are costly and time-consuming in spite of their high precision. Finally, although the SVM method has been used as an appropriate and valid algorithm for identifying effective factors in different studies (Chang et al, 2003;Jumutc et al, 2011;Wang, 2005;Zhang et al, 2018), it still has several advantages and disadvantages (Abe, 2005;Guyon et al, 2002;Yoo et al, 2012). One of the most important advantages of SVM, as a classification method, is the improvement of efficiency with the increase of data dimension.…”

Section: Resultsmentioning

confidence: 99%

Make Intelligent of Gastric Cancer Diagnosis Error in Qazvin’s Medical Centers: Using Data Mining Method

Mortezagholi

Khosravizadeh

Menhaj

et al. 2019

Asian Pac J Cancer Prev

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Objective: Gastric cancer is one of the most common types of cancers, which will result in irreparable harm in the case of misdiagnosis or late diagnosis. The purpose of this study is to investigate the capability of data mining techniques and disease risk factor characteristics to predict and diagnose the gastric cancer. Methods: In this retrospective descriptive-analytic study, we selected 405 samples from two groups of patient and healthy participants. A total of 11 characteristics and risk factors were examined. we used four Machine learning methods, Include support vector machine (SVM), decision tree (DT), naive Bayesian model, and k nearest neighborhood (KNN) to classify the patients with gastric cancer. The evaluation criteria to investigate the model on the database of patients with gastric cancer included Recall, Precision, F-score, and Accuracy. Data was analyzed using MATLAB® software, version 3.2 (Mathworks Inc., Natick, MA, USA). Results: Based on the results achieved from the evaluation of four methods, the accuracy rates of SVM, DT, naive Bayesian model, and KNN algorithms were 90.08, 87.89, 87.60, and 87.60 percent, respectively. The findings showed that the highest level of F-Score was related to the SVM (91.99); whereas, the lowest rate was associated with the KNN algorithm (87.17). Conclusion: According to the findings, the SVM algorithm showed the best results in classification of Test samples. So, this intelligent system can be used as a physician assistant in medical education hospitals, where the diagnosis processes are performed by medical students.

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Section: Resultsmentioning

confidence: 99%

Make Intelligent of Gastric Cancer Diagnosis Error in Qazvin’s Medical Centers: Using Data Mining Method

Mortezagholi

Khosravizadeh

Menhaj

et al. 2019

Asian Pac J Cancer Prev

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“…The potential of SERS spectroscopy for discrimination of high-grade and low-grade bladder cancer cells was demonstrated. 47 − 49 The possibility to predict the bladder cancer grade by SERS analysis of urine supernatant and sediment was proposed. 47 Recently, a new elegant NIR-SERS platform based on modified Au–Ag nanohollows was developed for effective discrimination of high-grade and low-grade bladder cancer cells.…”

Section: Introductionmentioning

confidence: 99%

“…The SERS technique overcomes the low inherent sensitivity of ordinary Raman spectroscopy. Several groups have demonstrated the promising advantages of the SERS approach in analysis of bladder cancer. − The first attempts to employ SERS spectroscopy for the analysis of the cultured bladder cancer cells are described by Jin et al in 2015. Following studies have shown that in vivo imaging of the bladder tissue can be performed using SERS nanotags or that the noninvasive and muscle-invasive bladder cancer cells can be determined from liquid biopsies .…”

Section: Introductionmentioning

confidence: 99%

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

et al. 2022

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Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm –1 and was assigned to the C–C, C–O, and C–N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

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“…Biomarker detection is the most effective strategy to improve the prognosis of patients with nasopharyngeal carcinoma (NPC); therefore, the development of point-of-care test with high sensitivity and specificity for tracking NPC progression is critical for disease management. Raman spectroscopy has been developed as an outstanding vibrational spectroscopy technique for multiplexed molecular analysis 1 , 2 . This exclusive feature offers the potential for progression evaluation.…”

Section: Introductionmentioning

confidence: 99%

Geometrically encoded SERS nanobarcodes for the logical detection of nasopharyngeal carcinoma-related progression biomarkers

et al. 2021

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The limited availability of nasopharyngeal carcinoma-related progression biomarker array kits that offer physicians comprehensive information is disadvantageous for monitoring cancer progression. To develop a biomarker array kit, systematic identification and differentiation of a large number of distinct molecular surface-enhanced Raman scattering (SERS) reporters with high spectral temporal resolution is a major challenge. To address this unmet need, we use the chemistry of metal carbonyls to construct a series of unique SERS reporters with the potential to provide logical and highly multiplex information during testing. In this study, we report that geometric control over metal carbonyls on nanotags can produce 14 distinct barcodes that can be decoded unambiguously using commercial Raman spectroscopy. These metal carbonyl nanobarcodes are tested on human blood samples and show strong sensitivity (0.07 ng/mL limit of detection, average CV of 6.1% and >92% degree of recovery) and multiplexing capabilities for MMPs.

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Classifying low-grade and high-grade bladder cancer using label-free serum surface-enhanced Raman spectroscopy and support vector machine

Cited by 24 publications

References 38 publications

Make Intelligent of Gastric Cancer Diagnosis Error in Qazvin’s Medical Centers: Using Data Mining Method

Make Intelligent of Gastric Cancer Diagnosis Error in Qazvin’s Medical Centers: Using Data Mining Method

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Geometrically encoded SERS nanobarcodes for the logical detection of nasopharyngeal carcinoma-related progression biomarkers

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