Clinical application of a novel computer-aided detection system based on three-dimensional CT images on pulmonary nodule.

The aim of this study was to investigate the clinical application effects of a novel computer-aided detection (CAD) system based on three-dimensional computed tomography (CT) images on pulmonary nodule. 98 cases with pulmonary nodule (PN) in our hospital from Jun, 2009 to Jun, 2013 were analysed in this study. All cases underwent PN detection both by the simple spiral CT scan and by the computer-aided system based on 3D CT images, respectively. Postoperative pathological results were considered as the "gold standard", for both two checking methods, the diagnostic accuracies for determining benign and malignant PN were calculated. Under simple spiral CT scan method, 63 cases is malignant, including 50 true positive cases and 13 false positive cases from the "gold standard"; 35 cases is benign, 16 true negative case and 19 false negative cases, the Sensitivity 1 (Se1)=0.725, Specificity1 (Sp1)=0.448, Agreement rate1 (Kappa 1)=0.673, J1 (Youden's index 1)=0.173, LR(+)1=1.616, LR(-)1=0.499. Kappa 1=0.673 between the 0.4 and 0.75, has a moderate consistency. Underwent computer-aided detection (CAD) based on 3D CT method, 67cases is malignant, including 62 true positive cases and 7 false positive cases; 31 cases is benign, 24 true negative case and 7 false negative cases, Sensitivity 2 (Se2)=0.899, Specificity2 (Sp2)=0.828, Agreement rate (Kappa 2)=0.877, J2 (Youden's index 2)=0.727, LR(+)2=5.212, LR(-)2=0.123. Kappa 2=0.877 >0.75, has a good consistency. Computer-aided PN detecting system based on 3D CT images has better clinical application value, and can help doctor carry out early diagnosis of lung disease (such as cancer, etc.) through CT images.

[1]  U. Pastorino Lung cancer screening , 2010, British Journal of Cancer.

[2]  S. Armato,et al.  Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. , 2002, Radiology.

[3]  A. Jemal,et al.  Cancer statistics, 2012 , 2012, CA: a cancer journal for clinicians.

[4]  J. Boltax,et al.  Lung Cancer Screening: A Review of Available Data and Current Guidelines , 2011, Hospital practice.

[5]  Ayman El-Baz,et al.  Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans , 2013, Int. J. Biomed. Imaging.

[6]  Khalid Iqbal,et al.  Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images , 2014, Comput. Math. Methods Medicine.

[7]  G. Byrnes,et al.  Screening for lung cancer. , 2004, The Cochrane database of systematic reviews.

[8]  Robert J. Gillies,et al.  Test–Retest Reproducibility Analysis of Lung CT Image Features , 2014, Journal of Digital Imaging.

[9]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[10]  Jan Cornelis,et al.  A novel computer-aided lung nodule detection system for CT images. , 2011, Medical physics.

[11]  Qian Wang,et al.  Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques. , 2009, Academic radiology.

[12]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[13]  Noriyuki Tomiyama,et al.  Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors. , 2010, Lung cancer.

[14]  C. Gatsonis,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[15]  Temesguen Messay,et al.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery , 2010, Medical Image Anal..

[16]  Jie Tian,et al.  Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach , 2013, Pattern Recognit..

[17]  K. Doi,et al.  False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. , 2005, Academic radiology.

[18]  Donato Cascio,et al.  Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models , 2012, Comput. Biol. Medicine.