Early Diagnosis Of Lung Cancer Research Articles

Serum Markers CA125, CA153, and CEA along with Inflammatory Cytokines in the Early Detection of Lung Cancer in High-Risk Populations.

Lung cancer mortality and morbidity rates are the first among malignant tumors. It is extremely crucial to pay more attention to the early diagnosis and treatment of lung cancer and to grasp and judge the progress of the patient's condition promptly. In this study, lung cancer patients' early diagnosis with tumor markers and inflammatory variables is examined. The general surgery department of our hospital treated 98 patients with lung cancer and 100 patients with benign pulmonary hyperplasia from January 2017 to February 2018. Additionally, 100 healthy subjects who completed physical examinations during this time period were included. Based on the findings of the pathological examination, 100 patients with benign pulmonary hyperplasia were chosen as the benign group, 100 patients with lung cancer were chosen as the malignant group, and 100 healthy individuals were chosen as the healthy group. The tumor markers carbohydrate antigen 125 (CA125), CA153, and carcinoembryonic antigen (CEA), as well as inflammatory factors such as tumor necrosis factor- (TNF-) and high-sensitivity C-reactive protein, were measured in the venous blood of three groups of patients (hs-CRP). There was no discernible difference in tumor marker levels between the benign and healthy groups (P > 0.05). In comparison to the benign and healthy groups, the malignant group had higher serum levels of CA153, CA125, and CEA (P 0.05). Between the benign and healthy groups, there was no discernible difference in the levels of inflammatory factors (P > 0.05). TNF- and hs-CRP serum levels were observed to be higher in the malignant group compared to the benign and healthy groups (P 0.05). The combined detection of CA153 + CA125 + CEA + TNF − +hs − CRP showed the highest sensitivity and specificity, which were, respectively, 62.22 percent and 92.00 percent, when compared to single or mixed detection of tumor markers or inflammatory factors solely. Serum levels of inflammatory agents TNF- and hs-CRP may be related to the pathophysiology and other functions of patients with lung cancer, as well as to the development and metastasis of the disease. These markers include CA153, CA125, and CEA. For the early detection of lung cancer and the evaluation of the disease's severity, the detection of tumor markers in combination with inflammatory variables has a significant reference value.

The value of chest computed tomography in evaluating lung cancer in a lobe affected by stable pulmonary tuberculosis in middle-aged and elderly patients: A preliminary study.

IntroductionLung cancer can be masked by coexisting stable tuberculosis lesions, which may result in delayed lung cancer diagnosis and treatment. Information about pulmonary tuberculosis patients who are at high-risk of developing lung cancer is scarce. We aimed to examine the value of chest computed tomography (CT) in evaluating lung cancer in a lobe affected by stable pulmonary tuberculosis in middle-aged and elderly patients.MethodsIn this single-centered, retrospective, observational study, we enrolled 41 middle-aged and elderly patients with pulmonary tuberculosis who developed lung cancer in the same lobe from January 30, 2011 to December 30, 2020. Comparisons of the clinical and chest CT data were made with age-matched and sex-matched control groups of patients with stable pulmonary tuberculosis but no lung cancer diagnosis (n = 38).ResultsSeventeen patients in the lung cancer group (41%) were initially misdiagnosed. Compared to lesions in the control group, lesions in the lung cancer group were significantly more likely to demonstrate the following CT features: large size, vessel convergence, lobulation, spiculation, spinous protuberance, bronchial obstruction or stenosis, vacuolation, ground-glass opacification, heterogeneous or homogeneous enhancement, and gradual increase in size. Nodular enlargement showed the best diagnostic performance in the diagnosis of lung cancer in a lobe affected by tuberculosis (area under the receiver operating characteristic curve = 0.974; P <0.001; accuracy = 98.2%; sensitivity =94.7%; specificity = 100%).ConclusionChest CT might play an important role in early diagnosis of lung cancer in a lobe affected by tuberculosis. Regular CT re-examination is necessary in continuous controls monitoring of patients with stable pulmonary tuberculosis. The study indicates necessity of prospective study in this field.

Increased use of computed tomography in Denmark: stage shift toward early stage lung cancer through incidental findings

Background Computed tomography (CT) examinations are increasingly used worldwide and incidental findings are growing likewise. Lung cancer stage at diagnosis is pivotal to survival. The earliest stage of lung cancer, stage IA is in most cases asymptomatic. Potentially, increased use of clinical CTs could induce a stage shift toward earlier lung cancer diagnosis. Materials and methods Data on the number of CT thorax in Denmark and the stage distribution of Danish lung cancer patients 2013–2020 were acquired from, respectively, the Danish Health Data Authority and the Danish Lung Cancer Registry. Clinical auditing of stage IA lung cancer patients was performed in the period 2019–2021 in a Danish region to assess the reasons for referral. Auditing of stage IV lung cancer patients was done to see whether a CT thorax was performed in a two-year period before diagnosis. Results All regions showed an increase in CTs per 1000 inhabitants. However, the number of CTs performed in 2013 differed by more than 50% among regions, and the increase per year also differed, from an increase of 1.9 to 3.4 more examinations per year. A significant correlation between CTs and fraction of stage IA lung cancers was seen in four out of the five regions. The audit of stage IA lung cancer cases revealed that 86.8% were incidental findings. Audit of stage IV lung cancer found that 4.3% had a nodule/infiltrate on a previous CT within a 2-year period prior to the diagnosis of lung cancer that was the probable origin of stage IV lung cancer. Conclusion The study found that the vast majority of early-stage lung cancers were incidental findings. It highlights that follow-up algorithms of incidental findings should be used in accordance with guidelines and it should be unequivocally how the CT follow-up of pulmonary infiltrates is managed.

Current status and challenges of research on lung cancer screening and early diagnosis

<p indent="0mm">Lung cancer is the most common malignancy and the leading cause of cancer death in China. Effective screening and early diagnosis can significantly improve the prognosis and reduce the risk of death in patients with lung cancer. Currently, low-dose computed tomography is the main screening method. Simultaneously, lung cancer-associated autoantibodies, artificial intelligence, and others have been gradually used in clinical practice. The development of respiratory interventional techniques such as endobronchial ultrasonography and virtual bronchoscopic navigation provide powerful tools for the accurate diagnosis of lung cancer at the early stage. In addition, new technologies such as liquid biopsy and exhaled breath analysis are expected to provide important tools for lung cancer screening and early diagnosis. However, each technique has its limitations, and optimization and combination may be the best choice. This paper reviews the status and challenges in the screening and early diagnosis of lung cancer to provide a reference for clinical doctors and researchers.

Auto diagnostic system for detecting solitary and juxtapleural pulmonary nodules in computed tomography images using machine learning

Lung cancer is one of the most serious cancers in the world with the minimum survival rate after the diagnosis as it appears in Computed Tomography scans. Lung nodules may be isolated from (solitary) or attached to (juxtapleural) other structures such as blood vessels or the pleura. Diagnosis of lung nodules according to their location increases the survival rate as it achieves diagnostic and therapeutic quality assurance. In this paper, a Computer Aided Diagnosis (CADx) system is proposed to classify solitary nodules and juxtapleural nodules inside the lungs. Two main auto-diagnostic schemes of supervised learning for lung nodules classification are achieved. In the first scheme, (bounding box + Maximum intensity projection) and (Thresholding + K-means clustering) segmentation approaches are proposed then first- and second-order features are extracted. Fisher score ranking is also used in the first scheme as a feature selection method. The higher five, ten, and fifteen ranks of the feature set are selected. In the first scheme, Support Vector Machine (SVM) classifier is used. In the second scheme, the same segmentation approaches are used with Deep Convolutional neural networks (DCNN) which is a successful tool for deep learning classification. Because of the limited data sample and imbalanced data, tenfold cross-validation and random oversampling are used for the two schemes. For diagnosis of the solitary nodule, the first scheme with SVM achieved the highest accuracy and sensitivity 91.4% and 89.3%, respectively, with radial basis function and applying the (Thresholding + Kmeans clustering) segmentation approach and the higher 15 ranks of the feature set. In the second scheme, DCNN achieved the highest accuracy and sensitivity 96% and 95%, respectively, to detect the solitary nodule when applying the bounding box and maximum intensity projection segmentation approach. Receiver operating characteristic curve is used to evaluate the classifier’s performance. The max. AUC = 90.3% is achieved with DCNN classifier for detecting solitary nodules. This CAD system acts as a second opinion for the radiologist to help in the early diagnosis of lung cancer. The accuracy, sensitivity, and specificity of scheme I (SVM) and scheme II (DCNN) showed promising results in comparison to other published studies.

Yeni bir Evrişimsel Sinir Ağı Modeli Kullanarak Bilgisayarlı Tomografi Görüntülerinden Akciğer Kanseri Tespiti

Akciğer kanseri, ülkemizde ve dünyada yaygın bir şekilde görülen kanser tipidir ve kansere bağlı ölümlerde ilk sırada yer almaktadır. Akciğer kanserinin erken teşhisi, hastalık seyri hakkında daha bilinçli ilerlemeyi sağlar ve hastanın sağ kalım durumu açısından hayati bir önem taşımaktadır. Son zamanlarda teknolojinin gelişmesiyle birlikte yapay zekâ ve derin öğrenme tabanlı sistemler; Bilgisayarlı Tomografi (BT), Manyetik Rezonans (MR) vb. tıbbi görüntüleme sistemlerinden elde edilmiş verileri kullanarak hastalık teşhisinde hekimlere önemli destek sağlamaktadır. Bu çalışmada akciğer kanserinin BT görüntüleri kullanarak yeni bir Evrişimli Sinir Ağı (ESA) modeli önerilmiştir. Önerilen ESA modelinin sınıflandırma sonuçları, literatürde bulunan diğer ön eğitimli derin öğrenme modellerine göre daha başarılı olduğu için tercih ettiğimiz ResNeXt derin öğrenme modelinin sonuçları ile karşılaştırılmıştır. Modellerin eğitimi ve test aşamaları için açık erişimli akciğer BT görüntülerinin bulunduğu bir veri seti kullanılmıştır. Çalışma sonucunda, önerilen ESA modelinin %99 doğruluk oranı ile ResNeXt mimarisine göre daha yüksek performans sergilediği gözlemlenmiştir. Ayrıca mevcut çalışmadaki görüntülerde herhangi bir özellik çıkarımı yöntemi kullanılmadan görüntüler ham hali ile sınıflandırılmıştır. Ve önerilen ESA modelinin, literatürde yapılan benzer çalışmalarda kullanılan yöntemlere göre daha az katman sayısının olmasının yanında sınıflandırma başarısının da daha yüksek olduğu gözlemlenmiştir.

In Vivo Near-Infrared Fluorescence/Ratiometric Photoacoustic Duplex Imaging of Lung Cancer-Specific hNQO1.

Overexpressing human NAD(P)H:quinone oxidoreductase 1 (hNQO1) in lung cancer tissues is deemed to be an attractive biomarker, which is directly connected to cancerous pathological processes. Monitoring of hNQO1 activity is crucial to early diagnosis and prognosis of lung cancer. In this study, an activatable hemi-cyanine dye-based probe (denoted as the LET-10 probe) was synthesized for near-infrared fluorescence (NIRF) and ratiometric photoacoustic (RPA) imaging of hNQO1. LET-10 can realize the NIRF and PA signal opening in the presence of hNQO1. Taking the octabutoxy naphthalocyanine in the LET-10 probe as a built-in reference signal, the LET-10 probe further demonstrated a double-signal self-calibration process for RPA imaging. Finally, the LET-10 probe was successfully applied for NIRF/RPA duplex imaging in the hNQO1-positive A549 lung cancer model, which suggests that the LET-10 probe is a promising tool for in vivo hNQO1 detection, especially for lung cancer diagnosis.

Serum-derived piR-hsa-164586 of extracellular vesicles as a novel biomarker for early diagnosis of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a major cause of death in those with malignant tumors. To achieve the early diagnosis of NSCLC, we investigated serum-derived Piwi-interacting RNA (piRNA) of extracellular vesicles to filter diagnostic biomarkers for NSCLC. High-throughput sequencing from cancerous tissues and adjacent noncancerous tissues in patients with NSCLC was first applied to recognize candidate piRNAs as diagnostic biomarkers. These screened piRNAs were further validated in 115 patients (including 95 cases in stage I) and 47 healthy individuals using quantitative real-time PCR (qRT-PCR). We showed that piR-hsa-164586 was significantly upregulated compared with paracancerous tissues and extracellular vesicles from the serum samples of healthy individuals. Moreover, the area under the curve (AUC) value of piR-hsa-164586 was 0.623 and 0.624 to distinguish patients with all stages or stage I of NSCLC, respectively, from healthy individuals. The diagnostic performance of piR-hsa-164586 was greatly improved compared with the cytokeratin-19-fragment (CYFRA21-1). Additionally, piR-hs-164586 was associated with the clinical characteristics of patients with NSCLC. Its expression was associated with the age and TNM stage of patients with NSCLC, indicating that it can serve as an effective and promising biomarker for the early diagnosis of NSCLC.

Design of a flexible robot toward transbronchial lung biopsy

AbstractTransbronchial lung biopsy is an effective and less-invasive treatment for the early diagnosis of lung cancer. However, the limited dexterity of existing endoscopic instruments and the complexity of bronchial access prevent the application of such procedures mainly for biopsy and diagnosis. This paper proposes a flexible robot for transbronchial lung biopsy with a cable-driven mechanism-based flexible manipulator. The robotic system of transbronchial lung biopsy is presented in detail, including the snake-bone end effector, the flexible catheters and the actuation unit. The kinematic analysis of the snake-bone end effector is conducted for the master-slave control. The experimental results show that the end effector reaches the target nodule through a narrow and tortuous pathway in a bronchial model. In conclusion, the proposed robotic system contributes to the field of advanced endoscopic surgery with high flexibility and controllability.

Diagnostic Efficacy of CT Examination on Early Detection of Lung Cancer during Pandemic of COVID-19.

Background: Since the outbreak of COVID-19 in 2020, routine CT examination was recommended to hospitalized patients at some hospitals and discovered lung cancer patients at an early stage. This study aimed to investigate the detection efficacy of routine CT examination on early diagnosis of lung cancer, especially on pathological characteristics. Methods: The epidemic of COVID-19 outbreak in January 2020 in China, and routine CT examination was recommended to hospitalized patients in June 2020 and ended in July 2021. Based on the time points, we compared the diagnosis efficacy between three periods: pre-period, peri-period, and the period of routine CT examination. Results: During the period of routine CT examination, more early stages of lung cancer were detected and the tumor size was reduced to 2.14 cm from 3.21 cm at pre-period (p = 0.03). The proportion of lung adenocarcinoma and early stage adenocarcinoma was increased by 12% and 30% in the period of routine CT examination, with referral to the pre-period of CT examination (p < 0.05). A total of 61% of diagnosed patients had the wild type of TP53 gene during the period of routine CT examination, compared to 45% of patients at the pre-period of CT examination (p = 0.001). The median Ki-67 index was 15% among patients diagnosed at the period of routine CT examination and increased to 35% at the pre-period of CT examination (p < 0.001). The period of routine CT examination was associated with a 78% higher probability of detecting an early stage of adenocarcinoma (OR = 1.78, 95%CI 1.03, 3.08) but no significant association was observed for squamous cell carcinoma. From the pre-period to the period of routine CT examination, the proportion of female patients and non-smoking patients increased by 57% and 44%, respectively (p < 0.001). Conclusion: Routine CT examination could detect more lung cancer at an early stage, especially for adenocarcinoma, and detect patients with less aggressive features. Further studies were warranted to confirm the findings.

Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network.

Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model's good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.

The Progress on Lung Computed Tomography Imaging Signs: A Review

Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields.

The Journey for Lung Cancer Screening where we Stand Today.

Lung cancer remains a leading cause of cancer mortality worldwide with many patients presenting with advanced disease. We reviewed the available literature for lung cancer screening using low dose computed tomography (LDCT). We reviewed the National Lung Screening Trial (NLST), Early Lung Cancer Action Program (ELCAP) and the (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trials. We also look at different lung cancer risk prediction models that may aid in identifying target populations and also discuss the cost-effectiveness of LDCT screening in different groups of smokers and ex-smokers. Lastly, we discuss recent guideline changes that have occurred in line with new and emerging evidence on lung cancer screening. LDCT has been shown reduce lung cancer mortality in certain groups of current and former smokers and should be considered to help in the early diagnosis of lung cancer.

Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer

Currently, in clinical practice there is a pressing need for potential biomarkers that can identify lung cancer at early stage before becoming symptomatic or detectable by conventional means. Several researchers have independently pointed out that the volatile organic compounds (VOCs) profile can be considered as a lung cancer fingerprint useful for diagnosis. In particular, 16% of volatiles contributing to the human volatilome are found in urine, which is therefore an ideal sample medium. Its analysis through non-invasive, relatively low-cost and straightforward techniques could offer great potential for the early diagnosis of lung cancer. In this study, urinary VOCs were analysed with a gas chromatography-ion mobility spectrometer (GC-IMS) and an electronic nose (e-nose) made by a matrix of twelve quartz microbalances complemented by a photoionization detector. This clinical prospective study involved 127 individuals, divided into two groups: 46 with lung cancer stage I–II–III confirmed by computerized tomography or positron emission tomography—imaging techniques and histology (biopsy), and 81 healthy controls. Both instruments provided a multivariate signal which, after being analysed by a machine learning algorithm, identified eight VOCs that could distinguish lung cancer patients from healthy ones. The eight VOCs are 2-pentanone, 2-hexenal, 2-hexen-1-ol, hept-4-en-2-ol, 2-heptanone, 3-octen-2-one, 4-methylpentanol, 4-methyl-octane. Results show that GC-IMS identifies lung cancer with respect to the control group with a diagnostic accuracy of 88%. Sensitivity resulted as being 85%, and specificity was 90%—Area Under the Receiver Operating Characteristics: 0.91. The contribution made by the e-nose was also important, even though the results were slightly less sensitive with an accuracy of 71.6%. Moreover, of the eight VOCs identified as potential biomarkers, five VOCs had a high sensitivity (p ⩽ 0.06) for early stage (stage I) lung cancer.

Research Progress of Markers Related to Early Diagnosis of Lung Cancer

Research Progress of Markers Related to Early Diagnosis of Lung Cancer

EP01.05-004 Trial in Progress: An Observational Study for Management of Lung Nodules across Latin America (DOuBLED Study)

Early diagnosis of lung cancer (LC) in the asymptomatic stage through screening programs and/or incidental pulmonary nodule identification and follow-up aids in identifying potentially curable tumours, thus improving survival. However, several gaps exist in early detection of LC, especially in low- and middle-income countries, mainly driven by socioeconomic and infrastructural factors (limited number of specialised human resources and technical capacity). Even if a lung nodule is identified, the pathway towards a definitive diagnosis is often unorganised and redundant, resulting in loss to follow up and a missed opportunity to early diagnosis.

OA05.06 Early Diagnosis of Lung Cancer Among Younger vs. Older Adults: Widening Disparities in the Era of Lung Cancer Screening

Young U.S. adults at high risk for lung cancer are currently ineligible for lung cancer screening, potentially leading to large disparities in the stage of lung cancer diagnosed between younger and older patients diagnosed with lung cancer. The objective of this study was to examine recent trends in the stage of lung cancer diagnosed among younger and older patients in the U.S.

Recent advances in the electrochemical sensing of lung cancer biomarkers

In the current scenario, about 25% of all cancer deaths reported globally are related to lung cancer. Despite the development of different diagnosing techniques, including X-ray, magnetic resonance imaging, biopsy, etc., there is still a significant challenge in dealing with lung cancer mortality and morbidity. Therefore, the early diagnosis and treatment of lung cancer is essential and holds a considerable place in lung cancer research. Biomarkers, an indicator, are overexpressed in the malignant tissues and are found in the body fluids. The existence of these biomolecules beyond the cut-off level can be used as an indicator for sensing tumour markers in the diagnosis, prognosis, and clinical management of lung cancer. Hence, fast and precise detection of biomarkers is helpful in the early detection of cancer. Moreover, the redox materials used for sensing biomarkers play a critical role in the sensitivity of electroanalytical devices. Therefore, we critically review the early diagnosis of lung cancer using an electrochemical biosensor. First, the importance of biomarkers and their application in determining various stages are discussed. A detailed section is devoted to recent electrochemical electrode materials, including graphene (Gr), carbon nanotube (CNT), and metal-based nanomaterials. Finally, the limitations and future prospects of electrochemical sensors are elaborated.

An attentive and adaptive 3D CNN for automatic pulmonary nodule detection in CT image

Automatic pulmonary nodule detection is the most crucial technology in early diagnosis of lung cancer and early treatment. However, the various nodule type, shape and size, especially the diameter of lung nodules (ranging from 3 mm to 30 mm), make the lung nodule detection results with high false positives. It significantly affects the detection performance of lung nodules. In this paper, an adaptive and attentive 3D Convolutional Neural Network (CNN) is proposed for automatic pulmonary nodule detection, which contains two parts: the candidate nodule detection and false positive reduction. In the first stage, the globule, spital and fine-grained information of focal nodules are scratched by the high-resolution fused attention module in the proposed method. In the second stage, an adaptive 3D CNN structure is designed to further reduce the false positives, which extracts the multilevel contextual information via an adaptive 3D convolution kernel. Extensive experiments are conducted on publicly available LUNA16. The results demonstrate that the proposed method can increase the sensitivity and decrease the false positives rate for automated pulmonary nodule detection effectively.

CRISPR/Cas12a Coupling with Magnetic Nanoparticles and Cascaded Strand Displacement Reaction for Ultrasensitive Fluorescence Determination of Exosomal miR-21.

Exosomal MicroRNA-21 (miRNA-21, miR-21) is significantly up-regulated in blood samples of patients with lung cancer. Exosomal-derived miR-21 can be used as a promising biomarker for the early diagnosis of lung cancer. This paper develops a fluorescent biosensor based on the combination of magnetic nanoparticles (MNPs), cascade strand displacement reaction (CSDR) and CRISPR/Cas12a to detect the exosomal miR-21 from lung cancer. The powerful separation performance of MNPs can eliminate the potential interference of matrix and reduce the background signal, which is very beneficial for the improvement of specificity and sensitivity. The CSDR can specifically transform one miR-21 into plenty of DNA which can specifically trigger the trans-cleavage nuclease activity of Cas12a, resulting in the cleavage of ssDNA bi-labeled with fluorescent and a quencher. Under the optimized experimental conditions, the developed fluorescence biosensor exhibited high sensitivity and specificity towards the determination of exosomal-derived miR-21 with a linear range from 10 to 1 × 105 fM and a low detection limit of about 0.89 fM. Most importantly, this method can be successfully applied to distinguish the exosomal miR-21 from the lung cancer patients and the healthy people.