Hiroyuki Nishiyama

Screening for Lung Cancer With Low-Dose Helical Computed Tomography: Anti-Lung Cancer Association Project

PURPOSEnBecause efficacy of lung cancer screening using chest x-ray is controversial and insufficient, other screening modalities need to be developed. To provide data on screening performance of low-dose helical computed tomography (CT) scanning and its efficacy in terms of survival, a one-arm longitudinal screening project was conducted.nnnPATIENTS AND METHODSnA total of 1,611 asymptomatic patients aged 40 to 79 years, 86% with smoking history, were screened by low-dose helical CT scan, chest x-ray, and 3-day pooled sputum cytology with a 6-month interval.nnnRESULTSnAt initial screening, the proportions of positive tests were 11.5%, 3.4%, and 0.8% with low-dose helical CT scan, chest x-ray, and sputum cytology, respectively. In 1,611 participants, 14 (0.87%) cases of lung cancer were detected, with 71% being stage IA disease and a mean tumor diameter of 19.8 mm. At repeated screening, the proportions of positive tests were 9.1%, 2.6%, and 0.7% with low-dose helical CT, chest x-ray, and sputum cytology, respectively. In 7,891 examinations, 22 (0.28%) cases of lung cancer were detected, with 82% being stage IA disease and a mean tumor diameter of 14.6 mm. The 5-year survival rate for screen-detected lung cancer was 76.2% and 64.9% for initial and repeated screening, respectively.nnnCONCLUSIONnScreening with low-dose helical CT has potential to improve screening efficacy in terms of reducing lung cancer mortality. An evaluation of efficacy using appropriate methods is urgently required.

Progression of focal pure ground-glass opacity detected by low-dose helical computed tomography screening for lung cancer.

Objective: To clarify the progression of focal pure ground-glass opacity (pGGO) detected by low-dose helical computed tomography (CT) screening for lung cancer. Methods: A total of 15,938 low-dose helical CT examinations were performed in 2052 participants in the screening project, and 1566 of them were judged to have yielded abnormal findings requiring further examination. Patients with peripheral nodules exhibiting pGGO at the time of the first thin-section CT examination and confirmed histologically by thin-section CT after follow-up of more than 6 months were enrolled in the current study. Progression was classified based on the follow-up thin-section CT findings. Results: The progression of the 8 cases was classified into 3 types: increasing size (n = 5: bronchioloalveolar carcinoma [BAC]), decreasing size and the appearance of a solid component (n = 2: BAC, n = 1; adenocarcinoma with mixed subtype [Ad], n = 1), and stable size and increasing density (n = 1: BAC). In addition, the decreasing size group was further divided into 2 subtypes: a rapid-decreasing type (Ad: n = 1) and a slow-decreasing type (BAC: n = 1). The mean period between the first thin-section CT and surgery was 18 months (range: 7–38 months). All but one of the follow-up cases of lung cancer were noninvasive whereas the remaining GGO with a solid component was minimally invasive. Conclusions: The pGGOs of lung cancer nodules do not only increase in size or density, but may also decrease rapidly or slowly with the appearance of solid components. Close follow-up until the appearance of a solid component may be a valid option for the management of pGGO.

Automatic extraction of pulmonary fissures from multidetector-row CT images

The paper describes the extraction of pulmonary major and minor fissures from three-dimensional (3D) chest multidetector-row computed tomography (MDCT) images. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. We have proposed (see Kubo, M. et al, IEEE Trans. Nucl. Sci, vol.46, p.2128-33, 1999) an automatic fissures extraction method using thin-section CT images with much noise. The present study proposes a simpler algorithm to extract fissures using MDCT images with little noise. The new proposed algorithm consists of the highlight method using the VanderBrug operator and the extraction method using morphology filters. We applied the proposed algorithm to one patient. Our method could accurately extract fissures.

Computerized analysis of 3-D pulmonary nodule images in surrounding and internal structure feature spaces

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in three-dimensional (3-D) thoracic CT images. Surrounding structure features were designed to characterize the relationships between nodules and their surrounding structures such as vessel, bronchi, and pleura. Internal structure features were derived from CT density and 3-D curvatures to characterize the inhomogeneous of CT density distribution inside the nodule. The stepwise linear discriminant classifier was used to select the best feature subset from multidimensional feature spaces. The discriminant scores output from the classifier were analyzed by the receiver operating characteristic (ROC) method and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 248 pulmonary nodules in this study. The internal structure features (Az=0.88) were more effective than the surrounding structure features (Az=0.69) in distinguishing malignant and benign nodules. The highest classification accuracy (Az=0.94) was obtained in the combined internal and surrounding structure feature space. The improvement was statistically significant in comparison to classification in either the internal structure or the surrounding structure feature space alone. The results of this study indicate the potential of using combined internal and surrounding structure features for computer-aided classification of pulmonary nodules.

Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images

We are developing a computerized approach to characterize pulmonary nodules through quantitative analysis between sequential 3-D thoracic images. In this approach the registration procedure of sequential 3-D pulmonary images consisted of two transformation steps: the rigid transformation step between two sequential 3-D thoracic CT images and the affine transformation step between two sequential region-of-interest (ROI) images including the pulmonary nodule. In both transformation step, the normalized mutual information was used as a voxel-based similarity measure. After the registration procedure, the 3-D pulmonary nodule image was segmented from the ROI image by a deformable surface method. The curvatures of each voxel in the nodule were computed directly from the gray-level 3-D image. Through curvatures a local description of the lesion was obtained by using shape index, curvedness, and CT value. Based on this local description of the nodule, the evolution of geometrical parameters was tracked through the time interval. Additionally, to characterize globally the evolution of the local description, the shape and the curvedness spectra were introduced. The interval changes of the lesion were traced in the feature spaces. The application results of our method to the sequence of 3-D thoracic images demonstrated that the interval changes of pulmonary nodules could be made visible.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Computer aided differential diagnosis of pulmonary nodules using curvature based analysis

This paper focuses on characterizing the internal intensity structure of pulmonary nodules in thin-section CT images for classification between benign and malignant nodules. This approach makes use of shape index curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. From the distribution of shape index, curvedness, and CT density over the 3D pulmonary nodule image a set of histogram features, and 3D texture features is computed to classify benign and malignant nodules. Linear discriminant analysis is used for classification and a receiver operating characteristic (ROC) analysis is used to evaluate the classification accuracy. The potential usefulness of the curvature-based features in the computer-aided differential diagnosis is demonstrated by using ROC curves as the performance measure.

Nodule detection algorithm based on multislice CT images for lung cancer screening

Recently, the development of multi-row multi-slice CT scanner proves precise measure of whole lung area in short time period. The CT scanner improves spatial resolution along z-axis and time resolution. Therefore, this CT image is effective for diagnosis of lung cancer as well as the other lung lesion, and leads the early detection. The development of a diagnosis support system is expected to diagnose these images. So far, we have developed a computer-aided diagnosis (CAD) system to automatically detect suspicious regions based on helical CT image. However, the algorithm isnt enough in multi-slice CT images because of two-dimensional algorithm and un-recognizing of the chest structure. This paper presents an algorithm of nodules detection using the three-dimensional (3-D) algorithm and recognizing of the chest structure based on multi-slice CT images, and we show the validity of detection algorithm of isolated nodules using 286 data sets.

Extraction of pulmonary fissures from thin-section CT images using calculation of surface-curvatures and morphology filters

This paper present an automatic extraction algorithm of the pulmonary major and minor fissures from three-dimensional (3-D) chest thin-section computed tomography (CT) images of helical CT. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. The proposed algorithm improves on the previous extraction method using the surface-curvatures calculation for density profile and morphological filters. The proposed method can extract the major and minor fissures in contact with the nodule and the chest walls. We apply the proposed algorithm to 12 patients. The results of our method are more accuracy to extract fissures around pulmonary lesions than by the previous method. The warped fissures extracted by our method show that lesions near fissures are malignant. Extracted fissures will aid in the diagnosis of lung cancer and in the analysis of automatic pulmonary conformation by using a computer.

ROI extraction of chest CT images using adaptive opening filter

We have already developed a prototype of computer-aided diagnosis (CAD) system that can automatically detect suspicious shadows from Chest CT images. But the CAD system cannot detect Ground-Grass-Attenuation perfectly. In many cases, this reason depends on the inaccurate extraction of the region of interests (ROI) that CAD system analyzes, so we need to improve it. In this paper, we propose a method of an accurate extraction of the ROI, and compare proposed method to ordinary method that have used in CAD system. Proposed Method is performed by application of the three steps. Firstly we extract lung area using threshold. Secondly we remove the slowly varying bias field using flexible Opening Filter. This Opening Filter is calculated by the combination of the ordinary opening value and the distribution which CT value and contrast follow. Finally we extract Region of Interest using fuzzy clustering. When we applied proposal method to Chest CT images, we got a good result in which ordinary method cannot achieve. In this study we used the Helical CT images that are obtained under the following measurement: 10mm beam width; 20mm/sec table speed; 120kV tube voltage; 50mA tube current; 10mm reconstruction interval.

Detection algorithm of lung cancer candidate nodules on multislice CT images

Recently, multi-slice helical CT technology was developed. Unlike the conventional helical CT, we can obtain CT images of two or more slices with 1 time of scan. Therefore, we can get many pictures with a clear contrast images and thin slice images in one time of scanning. The nodule is expected to be picture more clearly, and it is expected an high diagnostic ability of screening by the expert physicians. Multi-slice CT is z-axial high-contrast resolution, but the number of images is 10 times the single-slice helical CT. Therefore, the development of a diagnosis support system is expected to diagnose these images. We have developed a computer aided diagnosis (CAD) system to detect the lung cancer from multi-slice CT images. Using the conventional algorithm, it was difficult to detect the ground glass shadow and the nodules in contact with the blood vessel. The purpose of this study is to develop a detection algorithm using the 3-D filter by orientation map of gradient vectors and the 3-D distance transformation.