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The study objective was to determine the relationship between lung resection and the development of postoperative hiatal hernia.Preoperative and postoperative computed tomography imaging from 373 patients from the International Early Lung Cancer Action Program and the Initiative for Early Lung Cancer Research on Treatment were compared at a median of 31.1 months of follow-up after resection of clinical early-stage non-small cell lung cancer. Incidence of new hiatal hernia or changes to preexisting hernias were recorded and evaluated by patient demographics, surgical approach, extent of resection, and resection site.New hiatal hernias were seen in 9.6% of patients after lung resection (5.6% after wedge or segmentectomy and 12.4% after lobectomy;Both open and minimally invasive lung resection for clinical early-stage lung cancer are associated with new or enlarging postoperative hiatal hernia, especially after resections involving the left lower lobe.

Purpose Chronic Obstructive Pulmonary Disease (COPD) includes chronic bronchitis, small airways disease, and emphysema. Diagnosis of COPD requires spirometric evidence and may be normal even when small airways disease or emphysema is present. Emphysema increases the risk of exacerbations, and is associated with all-cause mortality and increased risk of lung cancer. We evaluated the prevalence of emphysema in participants with and without a prior history of COPD. Methods We reviewed a prospective cohort of 52,726 subjects who underwent baseline low dose CT screening for lung cancer from 2003 to 2016 in the International Early Lung Cancer Action Program. Results Of 52,726 participants, 23.8%(12,542) had CT evidence of emphysema. Of these 12,542 participants with emphysema, 76.5%(9595/12,542) had no prior COPD diagnosis even though 23.6% (2258/9595) had moderate or severe emphysema. Among 12,542 participants, significant predictors of no prior COPD diagnosis were: male (OR = 1.47, p Conclusion Emphysema was identified in 23.8% participants undergoing LDCT and was unsuspected in 76.5%. LDCT provides an opportunity to identify emphysema, and recommend smoking cessation.

To determine long-term survival of visceral pleural invasion (VPI) and parenchymal invasion (PAI) (angiolymphatic and/or vascular) on survival of NSCLCs less than 30 mm in maximum diameter.Kaplan-Meier survivals for NSCLCs, with and without VPI and/or PAI, were determined for a prospective cohort of screening participants stratified by pathologic tumor size (≤10 mm, 11-20 mm, and 21-30 mm) and nodule consistency. Log-rank test statistics were calculated.The frequency of PAI versus VPI was significantly lower in patients with subsolid nodules than in those with solid nodules (4.9% versus 27.7% [p0.0001]), and correspondingly, Kaplan-Meier lung cancer survival was significantly higher among patients with subsolid nodules (99.1% versus 91.3% [p = 0.0009]). Multivariable Cox regression found that only tumor diameter (adjusted hazard ratio [HR] =1.07, 95% confidence interval [CI]: 1.01-1.14, p = 0.02) and PAI (adjusted HR = 3.15, 95% CI: 1.25-7.90, p = 0.01) remained significant, whereas VPI was not significant (p = 0.15). When clinical and computed tomography findings were included with the pathologic findings, Cox regression showed that the risk of dying of lung cancer increased 10-fold (HR = 10.06, 95% CI: 1.35-75.30) for NSCLCs in patients with solid nodules and more than twofold (by a factor of 2.27) in patients with moderate to severe emphysema (HR = 2.27, 95% CI: 1.01-5.11), as well as with increasing tumor diameter (HR = 1.06, 95% CI: 1.01-1.13), whereas PAI was no longer significant (p = 0.19).Nodule consistency on computed tomography was a more significant prognostic indicator than either PAI or VPI. We propose that patients with NSCLC with VPI and a maximum tumor diameter of 30 mm or less not be upstaged to T2 without further large, multicenter studies of NSCLCs, stratified by the new T status and that classification be considered separately for patients with subsolid or solid nodules.

Detection of small pulmonary nodules is the goal of lung cancer screening. Computer-aided detection (CAD) systems are recommended to use in lung cancer computed tomography (CT) screening to increase the accuracy of nodule detection. Size and density of lung nodules are primary factors in determining the risk of malignancy. Therefore, purpose of this study is to apply computer-simulated virtual nodules based on the point spread function (PSF) measured in same scanner (maintaining spatial resolution condition) to assess the CAD system performance dependence on nodule size and density. Virtual nodules with density differences between lung background and nodule density (ΔCT) values (200, 300 and 400 HU) and different sizes (4 to 8 mm) were generated and fused on clinical images. CAD detection was performed and free-response receiver operating characteristic (FROC) curves were obtained. Results show that both density and size of virtual nodules can affect detection efficiency. Detailed results are possible to use for quantitative analysis of a CAD system performance. This study suggests that PSF-based virtual nodules could be effectively used to assess the lung cancer CT screening CAD system performance dependence on nodule size and density.

Summary Objectives The identification of early phase interstitial changes may influence the understanding of idiopathic interstitial pneumonitis. This study aimed to clarify its radiological patterns and the association with smoking. Methods The subjects underwent low-dose computed tomography to screen lung cancer. The selected subjects with interstitial changes were monitored for the precise morphology of interstitial changes using a high-resolution computed tomography (HRCT) scan. The subjects were classified into normal and abnormal HRCT subjects. The radiological findings on the HRCT scan, serum Klebs von der Lungen-6 (KL-6), surfactant protein (SP)-A, SP-D, pulmonary function, and computed tomography (CT) scores were analyzed. Abnormal HRCT subjects were classified based on the radiological patterns, and were followed-up over a 4-year period. Results HRCT abnormalities suggesting interstitial changes were identified in 80 of 3079 subjects. Seven subjects with honeycombing and 14 with combined pulmonary fibrosis and emphysema (CPFE) were identified. The frequencies of sex (male) and smoking in the subjects with honeycombing was higher than that of other patterns. The smoking history and the levels of serum KL-6, SP-A, and SP-D in abnormal HRCT subjects were significantly higher than those in normal HRCT subjects. Thirty-two of 73 abnormal HRCT subjects showed a progression of the CT scores in a chest HRCT over a 4-year period. Eighteen abnormal HRCT current smokers were included in the progression of CT scores. Conclusions HRCT patterns, excluding interlobular septal thickening, show the progression of CT scores. Smokers with CT abnormalities may have a tendency to demonstrate worsening interstitial changes.

Rationale and Objectives To evaluate the performance of custom-made software designed to quantify volume and weight of focal portions of lung tumors in regions of interest (ROI) and volume of interest on thin-section computed tomography (CT) images by stratum of CT values. Methods and Materials Volume and weight of lung portions were measured three-dimensionally and semiautomatically by CT stratum and compared with standard manual measurements. One each of a partly solid tumor (including initial and follow-up high-resolution CT scans), partly solid tumor with most parts showing solid density, and nonsolid tumors in the mid-zone of the lung were analyzed. The doubling time (DT) of tumor volume and mass were calculated by stratum of CT values. Results The software-measured tumor weight in lung periphery was reproducible, with and without trimming of intervening lung structures between borders of ROIs and tumor. For the lesion in the midlung zone, the fairly thick nearby pulmonary vessels degraded the measurements, with a higher measurement error, indicating the need to trim these structures off the ROI. Software measurements allowed quantification of tumor progress based on increase in tumor mass, without significant increase in tumor volume. The DTs for both tumor volume and mass were different among the four strata of CT values in the partly solid nodule. Conclusion Our semiautomated volumetric method involving measurement by CT stratum is promising for evaluation of lung tumor progress and aggressiveness.

Background and objective: Early detection and treatment of small malignant pulmonary lesions can improve survival; however, screening by CT detects many false positives. This study retrospectively evaluated a protocol for the diagnostic work-up of nodules detected by low-dose CT (LDCT) that are ≤ 10 mm in diameter. Methods: A health screening programme included LDCT. Lesions detected were allocated to one of four categories: negative, semi-negative, positive and semi-positive. Positive and semi-positive categories included non-calcified nodules without a polygonal shape, and these patients had an initial diagnostic HRCT and were then followed up using high-resolution CT (HRCT) at intervals determined by the characteristics of the lesion on screening LDCT and the initial diagnostic HRCT. Results: There were 275 nodules detected on screening LDCT; 84 patients had lesions classified as positive and 99 as semi-positive. Thirteen nodules detected on screening LDCT were only determined to be polygonal and benign following the diagnostic HRCT. The sensitivity and specificity of the screening CT, when compared with diagnostic HRCT, for determining if nodules should be classified as positive were 100% and 97%. The sensitivity and specificity of the initial diagnostic HRCT for being able to predict lung cancer were 87.5% and 91.7% respectively. Conclusions: Following the detection of a pulmonary lesion on screening LDCT, a diagnostic HRCT is necessary to determine the timing of follow-up HRCT. Diagnostic HRCT is needed to rationalize the screening for lung cancer to reduce the frequency of unnecessary follow-up scans.

Individuals concerned about their risk of lung cancer are recommended to talk with their physicians about computed tomographic screening for lung cancer. To provide the necessary information, the survival benefit of the screening, specific to a particular person for a particular round of screening, is needed. The probability of survival gain from the first, baseline, round of screening was addressed as the product of: 1) the screening resulting in a diagnosis of lung cancer; 2) not dying from some other cause for a sufficiently long period of time; and 3) cure resulting from pre-symptomatic treatment of lung cancer. These probabilities were estimated using the International Early Lung Cancer Action Program data on individuals aged 40-85 yrs with a cigarette smoking history of 0-150 pack-yrs. The estimated probability of survival gain ranged from 0.4% for a 60-yr-old with a 10-pack-yr smoking history who quit smoking 20 yrs ago, to 3.1% for a 70-yr-old current smoker with a 100 pack-yr history and 2.0% for an 85-yr-old current smoker with a 150-pack-yr history. When seeking counsel about initiation of screening for lung cancer, an estimate of the probability of survival gain from the first round of computed tomographic screening, specific to the person's age and history of smoking, can be provided.

To retrospectively evaluate whether a difference-image computer-aided detection (CAD) scheme can help radiologists detect peripheral lung cancers missed at low-dose computed tomography (CT).Institutional review board approval and informed patient and observer consent were obtained. Seventeen patients (eight men and nine women; mean age, 60 years) with a missed peripheral lung cancer and 10 control subjects (five men and five women; mean age, 63 years) without cancer at low-dose CT were included in an observer study. Fourteen radiologists were divided into two groups on the basis of different image display formats: Six radiologists (group 1) reviewed CT scans with a multiformat display, and eight radiologists (group 2) reviewed images with a "stacked" cine-mode display. The radiologists, first without and then with the CAD scheme, indicated their confidence level regarding the presence (or absence) of cancer and the most likely position of a lesion on each CT scan. Receiver operating characteristic (ROC) curves were calculated without and with localization to evaluate the observers' performance.With the CAD scheme, the average area under the ROC curve improved from 0.763 to 0.854 for all radiologists (P = .002), from 0.757 to 0.862 for group 1 (P = .04), and from 0.768 to 0.848 for group 2 (P = .01). The average sensitivity in the detection of 17 cancers improved from 52% (124 of 238 observations) to 68% (163 of 238 observations) for all radiologists (P.001), from 49% (50 of 102 observations) to 71% (72 of 102 observations) for group 1 (P = .02), and from 54% (74 of 136 observations) to 67% (91 of 136 observations) for group 2 (P = .006). The localization ROC curve also improved.Lung cancers missed at low-dose CT were very difficult to detect, even in an observer study. The use of CAD, however, can improve radiologists' performance in the detection of these subtle cancers.

Low-dose helical computed tomography (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a massive training artificial neural network (MTANN). The MTANN is a trainable, highly nonlinear filter based on an artificial neural network. To distinguish malignant nodules from six different types of benign nodules, we developed multiple MTANNs (multi-MTANN) consisting of six expert MTANNs that are arranged in parallel. Each of the MTANNs was trained by use of input CT images and teaching images containing the estimate of the distribution for the "likelihood of being a malignant nodule," i.e., the teaching image for a malignant nodule contains a two-dimensional Gaussian distribution and that for a benign nodule contains zero. Each MTANN was trained independently with ten typical malignant nodules and ten benign nodules from each of the six types. The outputs of the six MTANNs were combined by use of an integration ANN such that the six types of benign nodules could be distinguished from malignant nodules. After training of the integration ANN, our scheme provided a value related to the "likelihood of malignancy" of a nodule, i.e., a higher value indicates a malignant nodule, and a lower value indicates a benign nodule. Our database consisted of 76 primary lung cancers in 73 patients and 413 benign nodules in 342 patients, which were obtained from a lung cancer screening program on 7847 screenees with LDCT for three years in Nagano, Japan. The performance of our scheme for distinction between benign and malignant nodules was evaluated by use of receiver operating characteristic (ROC) analysis. Our scheme achieved an Az (area under the ROC curve) value of 0.882 in a round-robin test. Our scheme correctly identified 100% (76/76) of malignant nodules as malignant, whereas 48% (200/413) of benign nodules were identified correctly as benign. Therefore, our scheme may be useful in assisting radiologists in the diagnosis of lung nodules in LDCT.

Rationale and Objectives. To evaluate the effect of temporal subtraction images on the radiologists’ detection of early primary lung cancer in computed tomography (CT) scans. Materials and Methods. Fourteen cases with primary lung cancer and 16 normal cases were used for this study from a database of low-dose CT images, which were obtained from a lung cancer screening program in Nagano, Japan. Images were obtained with a single-detector helical CT scanner using 10 mm collimation and 2:1 pitch. Each case had both previous and current CT scans. Temporal subtraction images were obtained by subtracting the warped previous images from the current images. Seven radiologists, including four attendings and three residents, provided their confidence levels for the presence or absence of lung cancers with use of film CT images without and with temporal subtraction images. Receiver operating characteristic analysis was used to compare their performance without and with temporal subtraction images. Results. The mean Az values (area under the receiver operating characteristic curve) of seven observers without and with temporal subtraction images were 0.868 and 0.930, respectively. Diagnostic accuracy was significantly improved by using temporal subtraction images (P .007). Temporal subtraction images were especially useful when a nodule was present near the pulmonary hilum, where radiologists tended to overlook it. Conclusion. The temporal subtraction technique can significantly improve the sensitivity and specificity for detection of lung cancer on CT scans. © AUR, 2004

To evaluate thin-section computed tomographic (CT) characteristics of malignant nodules on the basis of overall appearance (pure ground-glass opacity [GGO], mixed GGO, or solid opacity) in comparison with the appearance of benign nodules.Institutional review board approval and patient consent were obtained. Follow-up diagnostic CT was performed in 747 suspicious pulmonary nodules detected at low-dose CT screening (17 892 examinations). Of 747 nodules, 222 were evaluated at thin-section CT (1-mm collimation), which included 59 cancers and 163 benign nodules (3-20 mm). Thin-section CT findings of malignant versus benign nodules with pure GGO (17 vs 12 lesions), mixed GGO (27 vs 29 lesions), or solid opacity (15 vs 122 lesions) were analyzed. Fisher exact test for independence was used to compare differences in shape, margin, and internal features between benign and malignant nodules. Positive predictive value (PPV) was analyzed when a category was significantly different from the others.Among nodules with pure GGO, a round shape was found more frequently in malignant lesions (11 of 17, 65%) than in benign lesions (two of 12, 17%; P = .02; PPV, 85%); mixed GGO, a subtype with GGO in the periphery and a high-attenuation zone in the center, was seen much more often in malignant lesions (11 of 27, 41%) than in benign lesions (two of 29, 7%; P = .004; PPV, 85%). Among solid nodules, a polygonal shape or a smooth or somewhat smooth margin was present less frequently in malignant than in benign lesions (polygonal shape: 7% vs 38%, P = .02; smooth or somewhat smooth margin: 0% vs 63%, P.001), and 98% (46 of 47) of polygonal nodules and 100% (77 of 77) of nodules with a smooth or somewhat smooth margin were benign.Recognition of certain characteristics at thin-section CT can be helpful in differentiating small malignant nodules from benign nodules.

The purpose of our study was to evaluate whether a computer-aided diagnosis (CAD) scheme can assist radiologists in distinguishing small benign from malignant lung nodules on high-resolution CT (HRCT).We developed an automated computerized scheme for determining the likelihood of malignancy of lung nodules on multiple HRCT slices; the likelihood estimate was obtained from various objective features of the nodules using linear discriminant analysis. The data set used in this observer study consisted of 28 primary lung cancers (6-20 mm) and 28 benign nodules. Cancer cases included nodules with pure ground-glass opacity, mixed ground-glass opacity, and solid opacity. Benign nodules were selected by matching their size and pattern to the malignant nodules. Consecutive region-of-interest images for each nodule on HRCT were displayed for interpretation in stacked mode on a cathode ray tube monitor. The images were presented to 16 radiologists-first without and then with the computer output-who were asked to indicate their confidence level regarding the malignancy of a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis.The area under the ROC curve (Az value) of the CAD scheme alone was 0.831 for distinguishing benign from malignant nodules. The average Az value for radiologists was improved with the aid of the CAD scheme from 0.785 to 0.853 by a statistically significant level (p = 0.016). The radiologists' diagnostic performance with the CAD scheme was more accurate than that of the CAD scheme alone (p0.05) and also that of radiologists alone.CAD has the potential to improve radiologists' diagnostic accuracy in distinguishing small benign nodules from malignant ones on HRCT.

A computer-aided diagnosis (CAD) scheme for determination of the likelihood of malignancy of 244 nodules on high-resolution CT (HRCT) was developed. The performance (Az) for 16 radiologists was improved from 0.785 to 0.853 (P=0.02) with the aid of the CAD scheme by use of 56 nodules, including 28 cancerous and 28 benign nodules which were matched in size and pattern to the cancers. Our purpose in this study was to investigate further whether a CAD scheme can assist radiologists in distinguishing benign from malignant nodules in different groups. The results indicated that Az values for radiologists without and with the CAD scheme were improved from 0.770 to 0.855 for general radiologists (P=0.01) and from 0.805 to 0.850 for chest radiologists (P=0.12); from 0.717 to 0.821 for nodules at 6–10 mm (P=0.04) and from 0.837 to 0.901 for nodules at 11–20 mm (P=0.04); and from 0.812 to 0.892 for nodules with pure ground-glass opacity (GGO) (P=0.149), from 0.819 to 0.863 for nodules with mixed GGO (P=0.196), and from 0.784 to 0.844 for solid nodules (P=0.334). CAD has the potential to improve the diagnostic accuracy in distinguishing benign nodules from malignant ones in different groups on HRCT.

Rationale and objectives A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed. Materials and methods Our scheme is based on a difference-image technique for enhancing the lung nodules and suppressing the majority of background normal structures. The difference image for each computed tomography image was obtained by subtracting the nodule-suppressed image processed with a ring average filter from the nodule-enhanced image with a matched filter. The initial nodule candidates were identified by applying a multiple-gray level thresholding technique to the difference image, where most nodules were well enhanced. A number of false-positives were removed first in entire lung regions and second in divided lung regions by use of the two rule-based schemes on the localized image features related to morphology and gray levels. Some of the remaining false-positives were eliminated by use of a multiple massive training artificial neural network trained for reduction of various types of false-positives. This computerized scheme was applied to a confirmed cancer database of 106 low-dose computed tomography scans with 109 cancer lesions for 73 patients obtained from a lung cancer screening program in Nagano, Japan. Results This computed-aided diagnosis scheme provided a sensitivity of 83% (91/109) for all cancers with 5.8 false-positives per scan, which included 84% (32/38) for missed cancers with 5.9 false-positives per scan. Conclusion This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.

We have been developing a computerized scheme to assist radiologists in improving the diagnostic accuracy for lung cancers on low-dose computed tomography (LDCT) scans by use of similar images for malignant nodules and benign nodules. A database of 415 LDCT scans including 73 cases with 76 confirmed cancers and 342 cases with 413 confirmed benign nodules was first collected in an LDCT screening program for early detection of lung cancers in Nagano, Japan. An observer study by use of receiver operating characteristics analysis was first conducted with five radiologists to demonstrate that presenting similar images for malignant nodules and benign nodules can significantly improve radiologists' performance in the diagnosis of unknown nodules. Another observer study was then conducted for obtaining reliable data on subjective similarity ratings by 10 radiologists. Based on the subjective similarity ratings, three important features were selected from a number of nodule features, and four different techniques for the determination of similarity measures, namely, a feature-based technique, a pixel-value-difference based technique, a cross-correlation-based technique, and a neural-network-based technique, were investigated and evaluated in terms of the correlation coefficient with the subjective similarity ratings. The experimental results in this study indicated that the neural-network-based technique can provide a reliable psychophysical similarity measure which is comparable to the subjective similarity ratings for a single radiologist when evaluated by use of correlation with the average similarity ratings for the other nine radiologists.

Rationale and Objectives. To report the detection rate for lung cancers in computed tomography (CT) screening in Japanese adults, and to analyze differences in the appearance of the cancers in non-smokers versus smokers. Materials and Methods. Subjects consisted of 7,847 Japanese adults who received low-dose CT screening at least once in a 3-year period. The detection rate of lung cancers and the correlation of imaging, clinical, and pathologic findings of cancers in non-smokers versus smokers were examined. Results. The detection rate for lung cancer was 1.1% for both non-smokers (45 of 4,251) and smokers (39 of 3,596). The prevalence of well-differentiated adenocarcinomas was greater in non-smokers (88%; 22 of 25) than in smokers (29%; 4 of 14) ( P P P P Conclusion. Most of the lung cancers in non-smokers were slow-growing adenocarcinomas appearing as faint ground-glass opacities on CT, whereas rapidly growing cancers appearing as solid nodules were more commonly seen in smokers.

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists in the early detection of lung cancer in radiographs and computed tomography (CT) images. In order to improve sensitivity for nodule detection, many researchers have employed a filter as a preprocessing step for enhancement of nodules. However, these filters enhance not only nodules, but also other anatomic structures such as ribs, blood vessels, and airway walls. Therefore, nodules are often detected together with a large number of false positives caused by these normal anatomic structures. In this study, we developed three selective enhancement filters for dot, line, and plane which can simultaneously enhance objects of a specific shape (for example, dot-like nodules) and suppress objects of other shapes (for example, line-like vessels). Therefore, as preprocessing steps, these filters would be useful for improving the sensitivity of nodule detection and for reducing the number of false positives. We applied our enhancement filters to synthesized images to demonstrate that they can selectively enhance a specific shape and suppress other shapes. We also applied our enhancement filters to real two-dimensional (2D) and three-dimensional (3D) CT images to show their effectiveness in the enhancement of specific objects in real medical images. We believe that the three enhancement filters developed in this study would be useful in the computerized detection of cancer in 2D and 3D medical images.

In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).

We have evaluated the performance of an automated classifier applied to the task of differentiating malignant and benign lung nodules in low-dose helical computed tomography (CT) scans acquired as part of a lung cancer screening program. The nodules classified in this manner were initially identified by our automated lung nodule detection method, so that the output of automated lung nodule detection was used as input to automated lung nodule classification. This study begins to narrow the distinction between the "detection task" and the "classification task." Automated lung nodule detection is based on two- and three-dimensional analyses of the CT image data. Gray-level-thresholding techniques are used to identify initial lung nodule candidates, for which morphological and gray-level features are computed. A rule-based approach is applied to reduce the number of nodule candidates that correspond to non-nodules, and the features of remaining candidates are merged through linear discriminant analysis to obtain final detection results. Automated lung nodule classification merges the features of the lung nodule candidates identified by the detection algorithm that correspond to actual nodules through another linear discriminant classifier to distinguish between malignant and benign nodules. The automated classification method was applied to the computerized detection results obtained from a database of 393 low-dose thoracic CT scans containing 470 confirmed lung nodules (69 malignant and 401 benign nodules). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate between nodule candidates that correspond to malignant nodules and nodule candidates that correspond to benign lesions. The area under the ROC curve for this classification task attained a value of 0.79 during a leave-one-out evaluation.

We studied the role of the first follow-up diagnostic CT for differentiating benign and malignant lesions in indeterminate solitary pulmonary nodules revealed at CT screening for lung cancer in which a total of 13,786 CT examinations (46% in women and 54% in men; 46% were smokers; mean age, 62 years) were performed.We reviewed thin-section CT findings on the initial diagnostic CT (lesion size; percentages of ground-glass-opacity areas of lesion; and presence or absence of lobulation, spiculation, air bronchogram, cavity, satellite lesions, pleural tag, concave margins, polygonal shape, and peripheral subpleural lesion) in 80 pulmonary nodules (36 malignancies and 44 benign lesions) of 80 patients. We evaluated changes in size (regression, no change, or growth) on the first follow-up CT performed 42-120 days (mean, 93 days) after the initial CT.The greatest accuracy (81%) with 89% sensitivity and 75% specificity for determining malignancy was attained with a combined criterion of growth of lesions or predominantly ground-glass-opacity lesions. Of all criteria that were specific to malignancy, the greatest sensitivity (50%) was achieved with a combination of growth or no change in size of lesions and predominantly ground-glass opacity and no concave margins. Of all criteria that were specific to benign lesions, the greatest sensitivity (45%) was attained with a combination of lesion regression or polygonal shape.Follow-up CT findings were useful, and a combination of findings on initial CT and follow-up CT was optimal for differentiating benign and malignant pulmonary nodules.

The objective of this study was to measure the image exploration activity of physicians, and thereby contribute to the development of a support system for CRT image interpretation in thoracic CT screening. In this study, we examined how the pupil diameters of five physicians changes over time during interpretation of a large quantity of CT images on a CRT monitor, and how this might be related to the accuracy of diagnosis. The study showed that, when a large quantity of CT images were viewed through a CRT monitor in a dimly lit room, the pupil diameter decreased during the second half of the long interpretation procedure in three of the five physicians. Furthermore, the pupil diameter frequently became approximately zero because the physician became drowsy. However, when the relationship between these phenomena and the accuracy of diagnosis was analyzed in one of the physicians, proof that such phenomena might lead to statistically significant false negatives or false positives was not found. Despite such results, the potential risk of misdiagnosis cannot be ignored. It may be necessary to devise both equipment and work conditions that will not cause the pupil diameter to become approximately zero during interpretation of images on a CRT monitor.

We investigated the imaging findings and progression of replacement lung neoplasms that were revealed on thin-section CT and serial CT.We evaluated the age of patients and thin-section CT findings (lesion size; percentage of ground-glass opacity areas; and presence or absence of solid portions, lobulation, coarse spiculation, air bronchogram, cavity, multiplicity, and pleural tags) in 73 lesions (11 atypical adenomatous hyperplasias, 17 type A [Noguchi's classification], 18 type B, and 27 type C small peripheral adenocarcinomas). We compared the serial findings of 48 of 73 lesions on low-dose screening CT (n = 21) or thin-section CT (n = 27) obtained at a mean interval of 450 days (range, 85-951 days). Progression from atypical adenomatous hyperplasia through type A to type B and then to type C tumor was studied using trend tests.A significant linear trend was seen for lesion size (r = 0.55; p0.001), percentages of ground-glass opacity areas (r = 0.75; p0.001), and the prevalence of lobulation (p0.001), spiculation (p = 0.001), air bronchogram (p = 0.023), cavity (p = 0.046), pleural tag (p0.001), and solid portions (p0.001). In general from serial CT assessment, lesions were recognized first as a ground-glass opacity nodule (56% of 48 lesions) with subsequent increase in size (75%), then solid portions appeared in the nodule (17%), and finally solid portions increased (23%) with occasional augmentation of tissue contraction (6%).CT analysis revealed stepwise progression of replacement-type lung neoplasms.

In this study, we investigated a pattern-classification technique which can be trained with a small number of cases with a massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network (ANN), which is capable of operating on image data directly. The MTANN is trained by use of a large number of sub-regions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning of an input image with the MTANN. In the MTANN, the distinction between nodules and non-nodules is treated as an image-processing task, in other words, as a highly nonlinear filter that performs both nodule enhancement and non-nodule suppression. This allows us to train the MTANN not on a case basis, but on a sub-region basis. Therefore, the MTANN can be trained with a very small number of cases. Our database consisted of 101 LDCT scans acquired from 71 patients in a lung cancer screening program. The scans consisted of 2,822 sections, and contained 121 nodules including 104 nodules representing confirmed primary cancers. With our current CAD scheme, a sensitivity of 81.0% (98/121 nodules) with 0.99 false positives per section (2,804/2,822) was achieved. By use of the MTANN trained with a small number of training cases (n=10), i.e., five pairs of nodules and non-nodules, we were able to remove 55.8% of false positives without a reduction in the number of true positives, i.e., a classification sensitivity of 100%. Thus, the false-positive rate of our current CAD scheme was reduced from 0.99 to 0.44 false positive per section, while the current sensitivity (81.0%) was maintained.

Proliferation of type II pneumocytes may be the most sensitive pathological indicator of alveolitis, and the density of type II pneumocytes reflecting the severity of the recent alveolar damage. Twenty-nine patients were divided into three groups by the severity of ground-glass opacities on the CT scans; the severe (acute), the moderate (subacute) and the mild (chronic) groups. We compared the density of type II pneumocytes in the transbronchial lung biopsy specimens and cell analysis of bronchoalveolar lavage (BAL) fluid with the ground-glass opacities. Clinical and laboratory findings and BAL fluid analysis also corresponded well with this grouping. Type II pneumocytes were selectively stained with an antibody against Thomsen-Friedenreich and the density of the type II pneumocytes was expressed as the number per 1 mm alveolar septal length. The densities of the type II pneumocytes in the severe, the moderate, the mild, and the control groups were 21.2+/-1.1, 14.4+/-7.2, 11.0+/-3.3 and 7.5+/-0.9/mm, respectively. There were significant differences between the acute group and the other three groups, and between the control and chronic groups. The density of type II pneumocytes is a useful index for evaluating alveolar damage even in mild alveolitis.

Objective: We studied the prognostic importance of high-resolution CT (HRCT) findings in lung adenocarcinomas. Patients and methods: HRCT findings (lesion size, percentage of ground-glass opacity (GGO) areas of lesion, and presence or absence of lobulation, coarse spiculation, air space, pleural tag, and multiplicity of lesion), clinical data (age and surgical method), and pathologic findings (tumor subtypes and presence or absence of nodal metastasis) in 64 consecutive patients with 64 peripheral adenocarcinomas of 20 mm or less (mean, 13 mm), including 36 women and 28 men with a mean age of 64 years were analyzed and correlated with survival of the patients using Kaplan–Meier method and stepwise Cox proportional hazards modeling. Follow-up periods of the patients ranged from 6 to 45 months (mean, 22 months). Tumors were classified into six subtypes (types A–F) according to tumor growth patterns defined by Noguchi et al. Results: Six (9%) of the 64 patients died of lung cancer. In univariate analyses, a significant difference was noted for lesion size (P=0.043), the percentage of GGO areas (P=0.005), and tumor subtypes (P=0.006). Lesion size of 57% (n=36), and type A (n=16) or type B adenocarcinomas (n=16) indicated a significantly better survival. In multivariate analyses using these three parameters as independent variables, the percentage of GGO areas was the only significant independent factor for survival (P=0.044, relative risk=0.95). Conclusion: GGO areas measured on HRCT may have an independent prognostic significance of small adenocarcinomas of the lung.

We studied the usefulness of thin-section CT in discriminating two categories of adenocarcinoma in the lung. Thin-section CT findings, such as, lesion size, ground-glass opacity (GGO) areas of lesion and presence or absence of lobulation, coarse spiculation, air bronchogram, small air space, or pleural tag of lesion in 62 consecutive patients with 62 adenocarcinomas (35 type A or B tumors (Noguchi's classification) and 27 type C tumors) ofor =20 mm, including 36 women and 26 men with a mean age of 64 years were analyzed. We performed stepwise logistic modeling using all the CT findings as independent variables to estimate the significant factors for discriminating type C from type A or B tumor. Lesion size in type C tumors was significantly (P0.001) greater than that in type A or B tumors. GGO areas in type C tumors were significantly (P0.001) smaller than that in type A or B tumors. The prevalence of coarse spiculation, air bronchogram, and pleural tag in type C tumors was significantly greater (P=0.001, 0.010, and0.001, respectively) than that in type A or B tumors. Logistic modeling revealed that the GGO area was the only significant factor for discriminating two categories (P0.001). Using the percentage of GGO areas for predicting type C tumor, 40% or less showed the highest accuracy of 85% with 70% sensitivity and 97% specificity. GGO areas of 30% or less had no false-positive diagnosis (100% specificity) with 81% accuracy but its sensitivity was low (56%). Thin-section CT was useful in discriminating two categories of adenocarcinoma in the lung.

We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using low-dose CT.The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n = 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (ΔCT) were inserted into clinical images (n = 10) and submitted for CAD analysis.In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient = 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a ΔCT 220 HU.Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy.

The purpose of this study was to determine the correlation between high-resolution CT morphologic features of small peripheral lung adenocarcinomas and tumor growth patterns.We examined high-resolution CT morphologic features of 59 small, surgically resected peripheral lung adenocarcinomas (diameter, 6-20 mm) that were detected on screening for lung cancer using low-dose helical CT. Among these adenocarcinomas, 14 (24%) were visible and 45 (76%) were invisible on conventional chest radiography. The correlation between high-resolution CT morphologic features and tumor growth patterns was analyzed.Sixteen (94%) of 17 type A (Noguchi's classification) adenocarcinomas appeared as nodules of pure ground-glass attenuation (high-resolution CT type I). Ten (71%) of 14 type B tumors appeared as heterogeneous, low-attenuation nodules (type II). Seven (29%) of 24 type C tumors appeared as nodules with ground-glass attenuation in the periphery and a high-density central zone (type III), and 12 (50%) of 24 type C tumors appeared as homogeneous nodules with soft-tissue density (type IV). Among tumors with a replacement growth pattern, the size and CT values of type C tumors were larger than those of type A or type B tumors (p0.05), whereas the percentage of ground-glass attenuation and retained air space in type C tumors was smaller than those in type A or type B tumors (p0.01). All (100%) four type D tumors appeared to be homogeneous nodules with soft-tissue density (type IV).Small peripheral lung adenocarcinomas shown on CT exhibit four high-resolution CT patterns that corresponded to the histopathologic findings of different tumor growth patterns.

Primary leiomyosarcoma of the thyroid gland is extremely rare and radiological information on this tumor is scant. We presented radiological findings on primary thyroid leiomyosarcoma in a 66-year-old woman in which anaplastic carcinoma was suspected based on clinical and cytological features and discussed the radiological clues to distinguish between the two diseases. Ultrasonography showed an ill-defined hypoechoic mass without halo in the left lobe and the isthmus of the thyroid gland. Computed tomography depicted a low-density mass with calcification and necrosis, which invaded the thyroid cartilage. No lymphadenopathy was seen. The tumor was demonstrated as an isointense mass on T1-weighted MR images and a mass of intermediate signal on T2-weighted images. The tumor showed a fair enhancement on gadolinium-enhanced T1-weighted images. Although the radiological picture was nonspecific, primary thyroid leiomyosarcoma appeared less invasive and far less frequent in developing nodal metastasis than anaplastic carcinoma in light of the literature.

CT has recently been used in mass screening for lung cancer. Small cancers have been identified but the growth characteristics of these lesions are not fully understood. We identified 82 primary cancers in our 3-year mass CT screening programme, of which 61 were examined in the present study. The volume doubling time (VDT) was calculated based on the exponential model using successive annual CT images or follow-up CT images. All cases were also examined in the hospital by high resolution CT (HRCT). Lesions were divided into three types based on HRCT characteristics: type G (n = 19), ground glass opacity (GGO); type GS (n = 19), focal GGO with a solid central component; and type S (n = 23), solid nodule. 18 (95%) lesions of type G, 18 (95%) of type GS and 7 (30%) of type S were invisible on conventional chest radiographs. The mean size of the tumour was 10 mm, 11 mm and 16 mm for type G, type GS and type S, respectively. Most tumours (80%) were adenocarcinomas; 78% of these were GGO (type G and GS). Mean VDT values were 813 days, 457 days and 149 days for type G, type GS and type S, respectively; these are significantly different from each other (p < 0.05). Our results show that annual mass screening CT for 3 successive years resulted in the identification of a large number of slowly growing adenocarcinomas that were not visible on chest radiographs.

Computed tomography scans, including thin-section high-resolution computed tomography (HRCT), occasionally fail to differentiate between small non-cancerous nodules from lung cancers. We describe nine such lesions ( < 20 mm in diameter) initially identified through our screening program for lung cancer using CT scanning. Pathological diagnoses included nodular fibrosis (n = 4), granuloma (n = 1), cryptococcoma (n = 1), localised organising pneumonia (n = 1), inflammatory pseudo-tumour (n = 1) and sclerosing haemangioma (n = 1). High-resolution CT findings, together with MRI findings with contrast-enhanced dynamic studies, were retrospectively evaluated. Additional cases should be identified and radiologically characterised in order to reduce the number of non-cancerous tumours that are treated by unnecessary surgery.

12 peripheral small lung cancers (< 20 mm) of rapid growth (volume doubling time < 150 days), detected by repeated low dose CT screening, were evaluated to examine their CT features and to correlate such features with histopathological findings. Each patient's CT images, including follow-up and thin section CT images, were studied retrospectively to determine tumour growth rate and CT morphological features. Nine of the tumours exhibited a solid tumour growth pattern: seven of these showed a well defined, homogeneous, soft tissue density with spicular or lobulated margin. These seven tumours included small cell lung cancer (n = 3), moderately differentiated adenocarcinoma (n = 2), poorly differentiated adenocarcinoma (n = 1) and squamous cell carcinoma (n = 1). The other two tumours, a moderately differentiated adenocarcinoma and a well differentiated adenocarcinoma, appeared as irregular, soft tissue density nodules with poorly defined margins. The latter exhibited an air bronchogram pattern and a small cavity. The remaining three tumours exhibited a lepidic tumour growth pattern. They showed ground glass opacity or ground glass opacity with a higher density central zone on CT images and were well differentiated adenocarcinomas. In conclusion, most peripheral small lung cancers of rapid growth were adenocarcinomas. They also included small cell lung cancer and squamous cell carcinoma. The majority showed solid tumour growth pattern and lacked an air bronchogram and/or small air spaces in the nodule. Some well differentiated adenocarcinomas with lepidic tumour growth pattern also showed rapid growth.

We present a case of primary thyroid lymphoma coexisting with Hashimoto's thyroiditis in a 75-year-old woman in whom B-cell lymphoma was substantiated based on the findings of immunophenotyping and polymerase chain reaction (PCR) gene rearrangement in specimens that had been obtained by ultrasound (US)-guided fine-needle aspiration biopsy (FNAB). The immunophenotyping technique showed A light chain restriction, and PCR-based assays showed a discrete narrow band, which was diagnostic for clonal B-cell proliferation. Analyses of PCR gene rearrangement in US-guided FNAB may be a useful ancillary technique to pathological findings for diagnosis of primary thyroid lymphoma, especially for differentiation between low-grade B-cell lymphomas and Hashimoto's thyroiditis.

Purpose: To test the effect of digital compression of CT images on the detection of small linear or spotted high attenuation lesions such as coronary artery calcification (CAC). Material and Methods: Fifty cases with and 50 without CAC were randomly selected from a population that had undergone spiral CT of the thorax for screening lung cancer. CT image data were compressed using JPEG (Joint Photographic Experts Group) or wavelet algorithms at ratios of 10:1, 20:1 or 40:1. Five radiologists reviewed the uncompressed and compressed images on a cathode-ray-tube. Observer performance was evaluated with receiver operating characteristic analysis. Results: CT images compressed at a ratio as high as 20:1 were acceptable for primary diagnosis of CAC. There was no significant difference in the detection accuracy for CAC between JPEG and wavelet algorithms at the compression ratios up to 20:1. CT images were more vulnerable to image blurring on the wavelet compression at relatively lower ratios, and "blocking" artifacts occurred on the JPEG compression at relatively higher ratios. Conclusion: JPEG and wavelet algorithms allow compression of CT images without compromising their diagnostic value at ratios up to 20:1 in detecting small linear or spotted high attenuation lesions such as CAC, and there was no difference between the two algorithms in diagnostic accuracy.

Background: Spontaneous pneumomediastinum (SPM) usually occurs in young people without an apparent precipitating factor or disease. Although there have been many studies focused on the clinical features and standard chest X-ray (CXR) findings of SPM, few have reviewed the chest computed-tomographic (CT) findings. Objectives: We assessed SPM using CXR and CT, and the relation between them. Methods: We evaluated 33 patients (26 males) diagnosed with SPM on the basis of symptoms and chest radiological findings. Results: Three patients showed normal CXR but a diagnostic CT scan. Seven showed mild pneumomediastinum on CXR. In these 10 patients, pneumomediastinum was easily detected by chest CT. Moderate and severe SPM were easily detected by both CXR and CT. Conclusions: These findings suggested that CXR alone poorly detected approximately 30% of SPM and that chest CT scan was needed to make the diagnosis in these cases. It seems likely that SPM is underdiagnosed by 30% or more in clinical practice.

To predict the natural history of pulmonary Mycobacterium avium-intracellulare (MAI) infection with nodular bronchiectasis, we retrospectively evaluated clinical manifestations, laboratory data, and bronchoalveolar lavage fluid (BALF) findings in 57 patients. The patients received follow-up chest computed tomographic scans and testing for sputum bacteriology between intervals of at least 12 mo. They were divided into two groups after observation for 28 +/- 13 mo: deteriorated (n = 34) and not-deteriorated (n = 23). There were no patients with spontaneous improvement. At the start of observation, the mean age was greater in the deteriorated group (69 +/- 9 yr) than in the not-deteriorated group (57 +/- 9 yr). The mean body-mass index was lower in the deteriorated group (19.2 +/- 3.1 kg/m(2)) than in the not-deteriorated group (21.5 +/- 1.5 kg/m(2)). C-reactive protein, erythrocyte sedimentation rate, and carbohydrate antigen 19-9 were significantly elevated in the deteriorated group. The BALF findings of the deteriorated group showed that the neutrophil cell counts were significantly increased. Thirty-four of 57 patients with pulmonary MAI infection with nodular bronchiectasis had progressive clinical and/or radiographic disease. The older and thinner patients tended to become worse. Neutrophil-related inflammation associated with a decrease in CD4+ lymphocyte might reflect the progression of pulmonary MAI infection with nodular bronchiectasis.

Purpose: To determine in which cases radiotherapy of the chest wall following mastectomy is indicated, based on the local recurrent rate in patients with locally advanced breast cancer. Methods and Materials: From 1984 until 1994, 105 patients who had four or more histopathologically confirmed axillary nodes metastases, or T3-4Nany, were subjected to mastectomy and were administered radiotherapy postoperatively using the hockey-stick field, which included the ipsilateral supraclavicular fossa and internal mammary nodes, except the chest wall. Median age was 51 years old (range, 23 to 82 years old). Eighty-five patients underwent radical mastectomy, 18 modified radical mastectomy, and 2 extended radical mastectomy. Fraction size was 2 Gy/day, the weekly fraction size was 10 Gy and the total dose ranged from 44 Gy to 54 Gy (median 50 Gy). Seventy-four patients were administered adjuvant chemotherapy, and 61 patients were administered hormone therapy. Results: The 5-year disease-free survival rates of the whole study population were 66%. The 5-year chest wall recurrence rates were 10%. The 5-year chest wall recurrence rates of the patients who had no vascular invasion ( n = 19) and the patients who had definite vascular invasion ( n = 38) were 0% and 24%, respectively ( p = 0.036). All the patients who presented chest wall recurrence had four or more axillary nodes metastases. Nine of the 10 patients who presented chest wall recurrence had definite vascular invasion, while there was no information about vascular invasion for the remaining patient. Factors such as age, pathological subtypes, tumor location, estrogen receptors, extent of resection, chemotherapy, and hormone therapy did not influence the development of chest wall recurrence. Conclusion: Among patients with breast cancer who have four or more positive axillary nodes or T3-4Nany, those who have no vascular invasion or less than 4 axillary nodes metastases do not need to be subjected to chest wall irradiation after radical mastectomy.

CTによる住民検診で肺癌が疑われ, 当院で薄層CTを用いた精密検査を受けた719例のうち, 経過観察中に病巣が縮小した非癌103例 (108病巣) のX線学的特徴を検討した. 検診時のCT所見を, Type 1 (限局性スリガラス陰影, n=54), Type 2 (軟部組織濃度結節, n=27), Type 3 (不整形陰影, n=27) に分類した. 20mm以下のものが多く, Type 1 (56%) とType 2 (70%) は両側下葉, Type 3 (52%) は右上葉の底部に多かった. 85%(92/108) は検診後3月以内に行われた初回CT精検時に消失あるいは縮小していた. 残りの16病巣の69%(11/16) は次の3月後, 19%(3/16) が更に次の3月後, 13%(2/16) が更に次の6月後に縮小していた. 16病巣中の88%(14/16) は薄層CT像で二次小葉性あるいは細葉性陰影を示し, 辺縁は直線的あるいはやや陥凹していた. 末梢肺野で細い気管支に支配されて分布していた. Type 2やType 3の吸収はType 1より遅かった.

我々は, 長野県下においてスパイラルCT検診車を用いて, 肺癌に対する一次検診を96年5月から実施している. 96年度は6,341名, 97年度は6,003名の受診者があり, 多くの小さい肺癌が発見された. 3年目の検診を現在継続中であるが, 97年度までの2年間の具体的データを飯沼らの提唱する肺癌検診における費用効果についてのモデルに適用して費用効果関係の分析を行った. その結果, 受診者一人当りの必要経費は約5,000円と計算された. 費用効果比については, 5年生存率など今後実証的なデータを求めるべき部分が残っており, 最終的な数値は提示できないが, 現時点における推定では, CTによる肺癌検診は費用効果比において従来法による検診と同等もしくはより優れており, CT検診は経済的にも有効であるといえる.

We describe a case of small adenocarcinoma which showed a distinctive MR finding consisting of two different zones of MR signal intensities (SI), central intermediate- and peripheral high-SI on T2-weighted MR imaging, due to different histologic subtypes. The central zone consisted of poorly differentiated adenocarcinoma and the peripheral zone consisted of well differentiated adenocarcinoma, with abundant clear cytoplasm. CT images showed a low density-, nearly homogeneous-nodule, which did not allow recognition of the presence of different histologic subtypes in the tumor. Contrast-enhanced CT and MR imagings showed slight enhancement. T2-weighted MR imaging was most effective in demonstrating uncommon findings of internal structures in the mass.