Recent advances in biosensor for detection of lung cancer biomarkers

Yang, Gaojian; Xiao, Ziqi; Tang, Cheng; Deng, Yan; Huang, Hao; He, Ziyu

doi:10.1016/j.bios.2019.111416

Cited by 190 publications

(71 citation statements)

References 113 publications

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“…The lesions are generally bigger in SCC patients than in ADC patients [29]. Tumor markers in serum are beneficial for the diagnosis and prognosis of NSCLC [30]. The serum levels of SCCA and CYFRA21-1 are highly sensitive in NSCLC and significantly higher in SCC than in ADC [31].…”

Section: Discussionmentioning

confidence: 99%

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Ren

Zhang

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). Methods A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900–0.964), 0.901 (0.840–0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions. Conclusion This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

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Section: Discussionmentioning

confidence: 99%

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Ren

Zhang

et al. 2020

Eur J Nucl Med Mol Imaging

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“…Previous studies confirmed that the risk model constructed with these tumor markers could enhance the early diagnosis of lung cancer [18,19]. Certainly, tumor markers in serum provide microscopic molecular information related to the occurrence and progression of cancer, which points out a new direction for the early detection of lung cancer [16,20]. In addition, blood sampling, minimally invasive and repeatable, can be easily performed, making serum an excellent matrix for lung cancer diagnosis [20,21].…”

Section: Introductionmentioning

confidence: 78%

“…At present, many scholars try to extract radiomic features of CT nodules and establish models to achieve the intelligent identification of benign and malignant nodules [12,14,15]. On the other hand, there is an urgent need to seek an auxiliary means, which can enhance the diagnostic efficiency of lung cancer in combination with CT. As we know, tumor markers have been widely used in the detection of lung cancer in recent years, such as progastrin-releasing peptide (ProGRP), vascular endothelial growth factor (VEGF), carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1) and neuronspecific enolase (NSE) [16,17]. Previous studies confirmed that the risk model constructed with these tumor markers could enhance the early diagnosis of lung cancer [18,19].…”

Section: Introductionmentioning

confidence: 99%

Development of a machine learning-based multimode diagnosis system for lung cancer

Duan

Cao

Hong

et al. 2020

Aging

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As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broadspectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

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“…The AuNSs (core diameter = 27 nm, branch length = 12 nm, tip diameter = 9 nm) coupled with carboxyl functionalisation of the GO film (thickness = 0.963 nm) reduced the LOD 32 times in comparison to an unenhanced GO-based sensor. Another interesting strategy used in developing SPR sensors is the use of molecularly imprinted polymers SPR sensors have also been widely explored for early stage cancer diagnosis [43][44][45][46][47][48][49]. They have been used for the detection of prominent cancer biomarkers such as cytokeratins [50][51][52], prostate specific antigen (PSA) [53][54][55], nucleic acids [56] and several other such molecules [57][58][59][60][61][62].…”

Section: Surface Plasmon Resonance Sensors For Biomedical Diagnosticsmentioning

confidence: 99%

Recent Progress in Optical Sensors for Biomedical Diagnostics

Pirzada

Altıntaş

2020

Micromachines

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In recent years, several types of optical sensors have been probed for their aptitude in healthcare biosensing, making their applications in biomedical diagnostics a rapidly evolving subject. Optical sensors show versatility amongst different receptor types and even permit the integration of different detection mechanisms. Such conjugated sensing platforms facilitate the exploitation of their neoteric synergistic characteristics for sensor fabrication. This paper covers nearly 250 research articles since 2016 representing the emerging interest in rapid, reproducible and ultrasensitive assays in clinical analysis. Therefore, we present an elaborate review of biomedical diagnostics with the help of optical sensors working on varied principles such as surface plasmon resonance, localised surface plasmon resonance, evanescent wave fluorescence, bioluminescence and several others. These sensors are capable of investigating toxins, proteins, pathogens, disease biomarkers and whole cells in varied sensing media ranging from water to buffer to more complex environments such as serum, blood or urine. Hence, the recent trends discussed in this review hold enormous potential for the widespread use of optical sensors in early-stage disease prediction and point-of-care testing devices.

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Recent advances in biosensor for detection of lung cancer biomarkers

Cited by 190 publications

References 113 publications

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Development of a machine learning-based multimode diagnosis system for lung cancer

Recent Progress in Optical Sensors for Biomedical Diagnostics

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