Prediction of forced expiratory volume in normal and restrictive respiratory functions using spirometry and self-organizing map

This work presents a prediction of forced expiratory volume in pulmonary function testing, using spirometry and neural networks. The pulmonary function data were recorded (n = 110) from volunteers using flow–volume spirometer with a standard acquisition protocol. From the recorded flow–volume curves, the acquired data are then used to predict forced expiratory volume in one second (FEV1) using a self-organizing map (SOM) and radial basis function neural networks. The SOM is used to determine the cluster centres of the hidden layer of radial basis function neural networks. The optimal widths of the Gaussian function of radial basis function neural networks were obtained from these centres and this network is then used to predict FEV1. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal and abnormal cases. The correlation between measured and predicted values of FEV1 for normal subjects was found to be 0.9. The prediction error for normal subjects is lower than that of restrictive subjects. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases.     

Lung function interpolation by means of neural-network-supported analysis of respiration sounds

Respiration sounds of individual asthmatic patients were analysed in the scope of the development of a method for computerised recognition of the degree of airways obstruction. Respiration sounds were recorded during laboratory sessions of allergen provoked airways obstruction, during several stages of advancing obstruction. The technique of artificial neural networks was applied for relating sound spectra and simultaneously measured lung function values (spirometry parameter FEV(1)). The ability of feedforward neural networks was tested to interpolate obstruction levels of FEV(1)-classes of which no members were included in the set used for training a network. In this way, a situation was simulated of an existing network recognising a new asthmatic attack under the same physiological conditions. It appeared to be possible to interpolate FEV(1) values, and it is concluded that a deterministic relationship exists between sound spectra and lung function parameter FEV(1). Variance optimisation appeared to be important in optimising the neural network configuration.     

A confident decision support system for interpreting electrocardiograms.

Computer-aided interpretation of electrocardiograms (ECGs) is widespread but many physicians hesitate to rely on the computer, because the advice is presented without information about the confidence of the advice. The purpose of this work was to develop a method to validate the advice of a computer by estimating the error of an artificial neural network output. A total of 1249 ECGs, recorded with computerized electrocardiographs, on patients who had undergone diagnostic cardiac catheterization were studied. The material consisted of two groups, 414 patients with and 835 without anterior myocardial infarction. The material was randomly divided into three data sets. The first set was used to train an artificial neural network for the diagnosis of anterior infarction. The second data set was used to calculate the error of the network outputs. The last data set was used to test the network performance and to estimate the error of the network outputs. The performance of the neural network, measured as the area under the receiver operating characteristic (ROC) curve, was 0.887 (0.845-0.922). The 25% test ECGs with the lowest error estimates had an area under the ROC curve as high as 0.995 (0.982-1.000), i.e. almost all of these ECGs were correctly classified. Neural networks can therefore be trained to diagnose myocardial infarction and to signal when the advice is given with great confidence or when it should be considered more carefully. This method increases the possibility that artificial neural networks will be accepted as reliable decision support systems in clinical practice.     

A neural network model to predict lung radiation-induced pneumonitis

A feed-forward neural network was investigated to predict the occurrence of lung radiation-induced Grade 2+ pneumonitis. The database consisted of 235 patients with lung cancer treated using radiotherapy, of whom 34 were diagnosed with Grade 2+ pneumonitis at follow-up. The network was constructed using an algorithm that alternately grew and pruned it, starting from the smallest possible network, until a satisfactory solution was found. The weights and biases of the network were computed using the error back-propagation approach. Momentum and variable leaning techniques were used to speed convergence. Using the growing/pruning approach, the network selected features from 66 dose and 27 non-dose variables. During network training, the 235 patients were randomly split into ten groups of approximately equal size. Eight groups were used to train the network, one group was used for early stopping training to prevent overfitting, and the remaining group was used as a test to measure the generalization capability of the network (cross-validation). Using this methodology, each of the ten groups was considered, in turn, as the test group (ten-fold cross-validation). For the optimized network constructed with input features selected from dose and non-dose variables, the area under the receiver operating characteristics (ROC) curve for cross-validated testing was 0.76 (sensitivity: 0.68, specificity: 0.69). For the optimized network constructed with input features selected only from dose variables, the area under the ROC curve for cross-validation was 0.67 (sensitivity: 0.53, specificity: 0.69). The difference between these two areas was statistically significant (p = 0.020), indicating that the addition of non-dose features can significantly improve the generalization capability of the network. A network for prospective testing was constructed with input features selected from dose and non-dose variables (all data were used for training). The optimized network architecture consisted of six input nodes (features), four hidden nodes, and one output node. The six input features were: lung volume receiving > 16 Gy (V16), generalized equivalent uniform dose (gEUD) for the exponent a = 1 (mean lung dose), gEUD for the exponent a = 3.5, free expiratory volume in 1 s (FEV1), diffusion capacity of carbon monoxide (DLCO%), and whether or not the patient underwent chemotherapy prior to radiotherapy. The significance of each input feature was individually evaluated by omitting it during network training and gauging its impact by the consequent deterioration in cross-validated ROC area. With the exception of FEV1 and whether or not the patient underwent chemotherapy prior to radiotherapy, all input features were found to be individually significant (p < 0.05). The network for prospective testing is publicly available via internet access.     

Use of genetic algorithms for neural networks to predict community-acquired pneumonia

BACKGROUND: Genetic algorithms have been used to solve optimization problems for artificial neural networks (ANN) in several domains. We used genetic algorithms to search for optimal hidden-layer architectures, connectivity, and training parameters for ANN for predicting community-acquired pneumonia among patients with respiratory complaints. METHODS: Feed-forward back-propagation ANN were trained on sociodemographic, symptom, sign, comorbidity, and radiographic outcome data among 1044 patients from the University of Illinois (the training cohort), and were applied to 116 patients from the University of Nebraska (the testing cohort). Binary chromosomes with genes representing network attributes, including the number of nodes in the hidden layers, learning rate and momentum parameters, and the presence or absence of implicit within-layer connectivity using a competition algorithm, were operated on by various combinations of crossover, mutation, and probabilistic selection based on network mean-square error (MSE), and separately on average cross entropy (ENT). Predictive accuracy was measured as the area under a receiver-operating characteristic (ROC) curve. RESULTS: Over 50 generations, the baseline genetic algorithm evolved an optimized ANN with nine nodes in the first hidden layer, zero nodes in the second hidden layer, learning rate and momentum parameters of 0.5, and no within-layer competition connectivity. This ANN had an ROC area in the training cohort of 0.872 and in the testing cohort of 0.934 (P-value for difference, 0.181). Algorithms based on cross-generational selection, Gray coding of genes prior to mutation, and crossover recombination at different genetic levels, evolved optimized ANN identical to the baseline genetic strategy. Algorithms based on other strategies, including elite selection within generations (training ROC area 0.819), and inversions of genetic material during recombination (training ROC area 0.812), evolved less accurate ANN. CONCLUSION: ANN optimized by genetic algorithms accurately discriminated pneumonia within a training cohort, and within a testing cohort consisting of cases on which the networks had not been trained. Genetic algorithms can be used to implement efficient search strategies for optimal ANN to predict pneumonia.     

Lung cancer cell identification based on artificial neural network ensembles.

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.     

XSZ-G系列电视胸腔镜下解剖性肺段切除在IB期非小细胞肺癌患者中的临床效果及对肺功能的影响

目的:探讨XSZ-G系列电视胸腔镜下解剖性肺段切除术在IB期非小细胞肺癌患者中的临床效果及对肺功能的影响。 方法:取2015年5月~2017年6月收治的IB期非小细胞肺癌患者70例,随机数字法分为对照组和观察组。对照组采用全胸腔镜肺叶切除术,观察组采用XSZ-G系列电视胸腔镜下解剖性肺段切除术,采用德国powerCube肺功能仪对患者治疗前、后肺功能水平进行测定,比较两组临床疗效及对肺功能的影响。 结果:两组淋巴结清扫数无统计学差异（P>0.05）;观察组术中出血量、手术时间、术后引流时间及术后住院时间均少（短）于对照组（P<0.05）;观察组治疗后肺1 s用力呼气量、FEV1占预计值百分比、最大呼气流量及FEV1/FVC水平均高于对照组（P<0.05）;观察组术后并发症发生率为11.43%,与对照组的20.00%比较差异有统计学意义（P<0.05）。 结论:IB期非小细胞肺癌患者采用XSZ-G系列电视胸腔镜下解剖性肺段切除术治疗效果理想,有助于提高肺功能水平,值得推广应用。     

The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors

BACKGROUND: The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritize medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients. METHODS: A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset. RESULTS: The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90. CONCLUSIONS: This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.     

基于灰度共生矩阵和人工神经网络的肺癌CT图像的分类研究

目的研究小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC）的分类问题。方法217例肺癌患者．其中男性165例．殳性52例;年龄35～80岁,平均年龄61．5岁。其中SCLC108例,NSCLC109例。提取患者764幅肺癌CT图像的灰度共生矩阵,选取对比度、熵、能量和逆差矩4个特征值,借助临床确诊结果,利用多层前向（BP）、径向基函数（RBF）人工神经网络对特征进行训练测试。结果BP人工神经网络对10％的78例样本进行测试,SCLC42例预测正确．NSCLC33例预测正确．3例预测失败。RBF神经网络对10％的78例测试样本进行测试,SCLC42例预测正确．NSCLC36例预测正确、类似方法对样本总数的70％进行训练,用30％的230例进行测试;BP人工神经网络有209例预测正确。正确率为90．9％：其中SCLC111例预测正确,正确检出率为88．8％;NSCLC98例预测正确,正确检出率为93.3％。RBF人工神经网络有216例预测正确．正确率为93．9％,其中SCLC117例预测正确,正确率为93．6％;NSCLC99例预测正确,止确检出率为94.3％。可见BP、RBF人1二神经网络对SCLC和NSCLC均具有90％以上的正确率,高于人工诊断结果。结论基于灰度共生矩阵的对比度、熵、能量和逆差矩4个特征值能反映SCLC和NSCLC的有效特征参量．通过人工神经网络能达到分类目的,辅助临床治疗。     

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room

OBJECTIVE: Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. METHODS AND MATERIALS: Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. RESULTS: The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. CONCLUSION: Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.     

A neural network to predict symptomatic lung injury.

A nonlinear neural network that simultaneously uses pre-radiotherapy (RT) biological and physical data was developed to predict symptomatic lung injury. The input data were pre-RT pulmonary function, three-dimensional treatment plan doses and demographics. The output was a single value between 0 (asymptomatic) and 1 (symptomatic) to predict the likelihood that a particular patient would become symptomatic. The network was trained on data from 97 patients for 400 iterations with the goal to minimize the mean-squared error. Statistical analysis was performed on the resulting network to determine the model's accuracy. Results from the neural network were compared with those given by traditional linear discriminate analysis and the dose-volume histogram reduction (DVHR) scheme of Kutcher. Receiver-operator characteristic (ROC) analysis was performed on the resulting network which had Az = 0.833 +/- 0.04. (Az is the area under the ROC curve.) Linear discriminate multivariate analysis yielded an Az = 0.813 +/- 0.06. The DVHR method had Az = 0.521 +/- 0.08. The network was also used to rank the significance of the input variables. Future studies will be conducted to improve network accuracy and to include functional imaging data.     

Classifying coronary dysfunction using neural networks through cardiovascular auscultation

The paper applies artificial neural networks (ANNs) to the analysis of heart sound abnormalities through auscultation. Audio auscultation samples of 16 different coronary abnormalities were collected. Data pre-processing included down-sampling of the auscultated data and use of the fast Fourier transform (FFT) and the Levinson-Durbin autoregression algorithms for feature extraction and efficient data encoding. These data were used in the training of a multi-layer perceptron (MLP) and radial basis function (RBF) neural network to develop a classification mechanism capable of distinguishing between different heart sound abnormalities. The MLP and RBF networks attained classification accuracies of 84% and 88%, respectively. The application of ANNs to the analysis of respiratory auscultation and consequently the development of a combined cardio-respiratory analysis system using auscultated data could lead to faster and more efficient treatment.     

A neural network approach to breast cancer diagnosis as a constraint satisfaction problem

A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84+/-0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs.     

Neural network predictions of significant coronary artery stenosis in men

OBJECTIVE: A neural network system was designed to predict whether coronary arteriography on a given patient would reveal any occurrence of significant coronary stenosis (>50%), a degree of stenosis which often leads to coronary intervention. METHODOLOGY: A dataset of 2004 records from male cardiology patients was derived from a national cardiac catheterization database. The catheterizations selected for analysis from the database were first-time and elective, and they were precipitated by chest pain. Eleven patient variables were used as inputs in an artificial neural network system. The network was trained on the earliest 902 records in the dataset. The next 902 records formed a cross-validation file, which was used to optimize the training. A third file composed of the next 100 records facilitated the choice of a cutoff number between 0 and 1. The cutoff number was applied to the last 100 records, which comprised a test file. RESULTS: When a cutoff of 0.25 was compared to the network outputs of all 100 records in the test file, 12 of 46 (specificity=26%) patients without significant stenosis had outputs0.25 (sensitivity=100%). Therefore, the network identified a fraction of the patients in the test file who did not have significant coronary artery stenosis, while at the same time the network identified all of the patients in the test file who had significant stenosis capable of causing chest pain. CONCLUSION: Artificial neural networks may be helpful in reducing unnecessary cardiac catheterizations.     

Detection of lung nodules in digital chest radiographs using artificial neural networks: A pilot study

Radiologists can fail to detect up to 30% of pulmonary nodules in chest radiographs. A back-propagation neural network was used to detect lung nodules in digital chest radiographs to assist radiologists in the diagnosis of lung cancer. Regions of interest (ROIs) that cantained nodules and normal tissues in the lung were selected from digitized chest radiographs by a previously developed computer-aided diagnosis (CAD) scheme. Different preprocessing techniques were used to produce input data to the neural network. The performance of the neural network was evaluated by receiver operating characteristic (ROC) analysis. We found that subsampling of original 64- × 64-pixel ROIs to smaller 8- × 8-pixel ROIs provides the optimal preprocessing for the neural network to distinguish ROIs containing nodules from false-positive ROIs containing normal regions. The neural network was able to detect obvious nodules very well with an A_z value (area under ROC curve) of 0.93, but was unable to detect subtle nodules. However, with a training method that uses different orientations of the original ROIs, we were able to improve the performance of the neural network to detect subtle nodules. Artificial neural networks have the potential to serve as a useful classifier to help to eliminate the false-positive detections of the CAD scheme.     

椎动脉支架内再狭窄的人工神经网络分析

探讨椎动脉狭窄支架植入术后再狭窄的危险因素,并利用人工神经网络对椎动脉支架内再狭窄（ISR）进行预测分析。首先,随访97例临床患者,对 12种可能影响椎动脉支架内再狭窄的因素进行单因素分析,总结出具有统计学意义的相关因素。然后,利用BP神经网络建立影响因素样本集与对应的ISR之间的隐性联系模型。最后,利用神经网络预测患者是否会发生支架内再狭窄,并对预测准确率进行评估。结果表明,置入支架后,再狭窄组中支架长度平均值为15 mm,无再狭窄组患者中支架长度平均值为17 mm,两者具有显著差异(P=0.005);再狭窄组患者平均扩张比为1.15,无再狭窄组患者平均扩张比为1.17,两者具有显著差异（P=0.01）;再狭窄组和无再狭窄组患者椎动脉侧别也具有显著差异（P=0.045）。同时,评估结果显示,BP神经网络模型预测结果令人满意,不会发生ISR的确诊率q175%,会发生ISR确诊率q2=100%。支架长度、椎动脉侧别和支架扩张比对椎动脉ISR具有显著性影响。BP神经网络模型可用于预测椎动脉ISR的发生。     

Prediction of oculocardiac reflex in strabismus surgery using neural networks.

Successfully predicting an oculocardiac reflex (OCR) is difficult to achieve despite various proposed maneuvers. The aim of this study was to test the models built up by neural networks to predict the occurrence of OCR during strabismus surgery in children. Premedication was not given. Atropine 0.01 mg/kg was medicated just before induction. Induction was performed with fentanyl or ketorolac, followed by propofol. Atracurium or vecuronium was given for intubation. Anesthesia was maintained with O2-N2O with continuous propofol infusion. Chi-square test was performed for induction agents, gender, weight, muscle blockade, repaired muscle, number of repaired muscles, duration of operation to detect any association between the occurrence of OCR and to develop the model of neural networks. The multi-layer perceptron, radial basis function and Bayesian backpropagation network were tested. The occurrence of OCR was significantly associated with gender and repaired muscle (p < 0.05). Gender, repaired muscle and age were considered as input for the multi-layer perceptron, radial basis function and Bayesian backpropagation network. Three neural networks had predicted the same correction rate in the occurrence of OCR as being 87.5% overall among 16 patients' records tested. These models are conceptually different in predicting compared to conventional maneuvers, and have the advantage of testing individually and foretelling the propensity. By comparison neural networks use grouped experiential data and predict OCR by the learning rule. Neural networks require a relatively abundant number of experienced and homogenous patients' records to establish an accurate model. The multi-layer perceptron, radial basis function and Bayesian backpropagation modeling network may be an alternative way, and preferable to vagal tone maneuvers if the associated relationships to the occurrence of OCR are more clearly defined.     

Logistic回归和ROC曲线综合分析三种肿瘤标志物对非小细胞肺癌的诊断价值

目的：探讨Logistic回归和ROC曲线综合分析三种肿瘤标志物对非小细胞肺癌的诊断价值。方法：采用酶联免疫吸附法和电化学发光免疫分析仪（Elecsys2010）分别检测70例非小细胞肺癌（NSCLC）患者及50例肺良性疾病患者外周血中TumorM2-PK、CEA和CYFRA21-1的含量。Logistic回归筛选相关指标,并建立相应回归方程,ROC曲线分析临床性能。结果：NSCLC患者TumorM2-PK、CEA、CYFRA21-1水平明显高于肺良性疾病患者,差异有统计学意义（P〈0.01）。NSCLC患者三种肿瘤标志物水平与TNM分期密切相关,TNM分期越晚,三种标志物水平也越高。建立回归方程Y=1/[1＋EXP（2.256-0.132X1-0.303X2）],新变量Y诊断NSCLC的敏感性、特异性和准确性分别为72.9、96和80.8。结论：TumorM2-PK可作为NSCLC的一个诊断标志物。运用Logistic回归和ROC曲线综合分析可提高NSCLC诊断的准确性。     

The simplification of the muscle force prediction using sensitivity analyses.

Today artificial neural networks can be trained to solve problems that are difficult for conventional computers or human beings. The big advantage of an artificial neural network is results obtained without knowledge of the algorithm procedure or without full and exact information. Therefore an artificial neural network was used to predict the muscle forces. The aim of the study was to simplify prediction of muscle forces which are difficult to determine, because many muscles act cooperatively. However, orthopeadists, biomechanical engineers and physical therapists need to take muscle forces into consideration because joint contact forces, as well as muscle forces, need to be estimated in order to understand the joint and bone loading. In terms of sensitivity of the muscle parameters to the results from the proposed neural network object, the muscle force prediction was simplified.     

Serum lemur tyrosine kinase-3: a novel biomarker for screening primary non-small cell lung cancer and predicting cancer progression

Purpose: We aimed to determine the expression level of serum soluble lemur tyrosine kinase-3 (sLMTK3) in human non-small cell lung cancer (NSCLC), and to examine whether the s sLMTK3 level could be used as a biomarker to screen primary NSCLC and to predict lung cancer progression. Methods: Serum levels of sLMTK3 in 67 patients with primary NSCLC, 28 patients with lung benign lesion, and 53 healthy volunteers were measured by sandwich ELISA. LMTK3 protein expression in NSCLC tissues and normal lung tissues was also detected by using immunohistochemical staining. Receiver operating characteristic (ROC) curve was selected to evaluate the sensitivity and the specificity of serum sLMTK3 level. Results: The mean concentration of sLMTK3 in NSCLC group was significantly higher than in the lung benign lesion group (P < 0.001) and the healthy control group (P < 0.001). Higher sLMTK3 level was correlated with age (P = 0.013), tumor-node-metastasis (TNM) stage (P < 0.001), and lymph node metastasis (P < 0.001) of NSCLC. In contrast to the normal lung tissues, increased LMTK3 expression was found in the NSCLC tissues, and was mainly located on the cytoplasm and the nuclei of cancer cells. For separating NSCLC from control group, the corresponding areas under the ROC curve (AUC) were 0.947 for sLMTK3 and 0.804 for CEA. With cutoffs of 10.05 ng/ml for sLMTK3 and 5.0 ng/ml for CEA respectively, the sensitivity and the specificity of sLMTK3 and CEA were, 80.60% and 97.53%, 35.82% and 96.30%, respectively, indicating better diagnostic value of sLMTK3. Conclusions: The sLMTK3 level was significantly increased in human NSCLC, and could be used as a potential and valuable biomarker for screening primary NSCLC and for predicting the progression of patients with this malignancy.