Improved detection of lung nodules by using a temporal subtraction technique

PURPOSE: To evaluate the effect of a temporal subtraction technique for digital chest radiography with regard to the accuracy of detection of lung nodules. MATERIALS AND METHODS: Twenty solitary lung nodules smaller than 30 mm in diameter, including 10 lung cancers and 10 benign nodules, were used. The nodules were grouped subjectively according to their subtlety. For non-nodular cases, 20 nodules without perceptible interval changes were selected. All chest radiographs were obtained by using a computed radiographic system, and temporal subtraction images were produced by using a program developed at the University of Chicago. The effect of the temporal subtraction image was evaluated by using an observer performance study, with use of receiver operating characteristic analysis. RESULTS: Observer performance with temporal subtraction images was substantially improved (A(z) = 0.980 and 0.958), as compared with that without temporal subtraction images (A(z) = 0.920 and 0.825) for the certified radiologists and radiology residents, respectively. The temporal subtraction technique clearly improved diagnostic accuracy for detecting lung nodules, especially subtle cases. CONCLUSION: The temporal subtraction technique is useful for improving detection accuracy for peripheral lung nodules on digital chest radiographs.     

Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies.

Since 1996, computer-aided diagnosis (CAD) schemes have been presented as interactive demonstrations on computer workstations at each scientific assembly of the Radiological Society of North America. The schemes involved (a) detection of pulmonary nodules, (b) temporal subtraction, (c) detection of interstitial lung disease, (d) differential diagnosis of interstitial lung disease, and (e) distinction between benign and malignant pulmonary nodules on chest radiographs. Large-scale observer tests were carried out to examine how radiologists can benefit from CAD systems. Observer performance was evaluated by analysis of receiver operating characteristic (ROC) curves. The statistical significance of the difference between the areas under the ROC curves without and with CAD was analyzed with the Student t test. In all of the tests, the diagnostic accuracy of the radiologists in total improved significantly when CAD was used. This result provides additional evidence that CAD has the potential to improve the performance of radiologists in their decision-making process in interpreting chest radiographs. Copyright RSNA, 2003.     

Temporal subtraction for the detection of hazy pulmonary opacities on chest radiography

OBJECTIVE: The purpose of this study was to evaluate the accuracy of temporal subtraction with a commercially available computer-assisted diagnosis system for the detection of multifocal hazy pulmonary opacities on chest radiographs, which are sometimes difficult to detect directly on chest radiographs. MATERIALS AND METHODS: Thirty healthy patients and 30 patients with new multifocal hazy pulmonary opacities that were confirmed by serial chest CT examinations were evaluated with and without temporal subtraction images. Chest radiographs were taken from either film-screen or digital radiography images and were digitized with a spatial resolution of 0.171 mm per pixel. Temporal subtraction images were produced by an iterative image-warping technique. We designed an observer performance study in which observers (six chest radiologists and four residents) indicated their confidence level for the presence or absence of hazy pulmonary opacities on two sets of images, current and previous radiographs only (set A), and current and previous radiographs with temporal subtraction images (set B). Receiver operating characteristic curves were generated. RESULTS: For chest radiologists, observer performance with set B (with temporal subtraction images; A(z) = 0.947) was superior to that with set A (without temporal subtraction images; A(z) = 0.916) (p < 0.05). For residents, no statistically significant difference was found between sets A and B. CONCLUSION: The temporal subtraction technique clearly improves diagnostic accuracy for the detection of multifocal hazy pulmonary opacities on chest radiographs, especially when the observers are experienced chest radiologists who have sufficient skill to evaluate the patient's condition as revealed on the images.     

Comparison of LCD and CRT monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic analysis

Soft copy reading of digital images has been practiced commonly in the PACS environment. In this study, we compared liquid-crystal display (LCD) and cathode-ray tube (CRT) monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic (ROC) analysis. Digital chest images with a 1000x1000 matrix size and a 8 bit grayscale were displayed on LCD/CRT monitor with 2M pixels in each observer test. Eight and ten radiologists participated in the observer tests for detection of nodules and interstitial diseases, respectively. In each observer test, radiologists marked their confidence levels for diagnosis of pulmonary nodules or interstitial diseases. The detection performance of radiologists was evaluated by ROC analyses. The average Az values (area under the ROC curve) in detecting pulmonary nodules with LCD and CRT monitors were 0.792 and 0.814, respectively. In addition, the average Az values in detecting interstitial diseases with LCD and CRT monitors were 0.951 and 0.953, respectively. There was no statistically significant difference between LCD and CRT for both detection of pulmonary nodules (P=0.522) and interstitial lung diseases (P=0.869). Therefore, we believe that the LCD monitor instead of the CRT monitor can be used for the diagnosis of pulmonary nodules and interstitial lung diseases in digital chest images.     

Evaluation of improvement of the detection of interstitial lung diseases by using the dual-energy subtraction radiography method of a flat-panel detector system

We investigated and evaluated the detection of simulated lesions in various interstitial lung diseases using the dual-energy subtraction radiography method and flat-panel detector (FPD) images. We obtained a FPD system (GE Revolution XR/d), and employed dual-energy 60 kV and 130 kV exposure techniques. Three types of lung lesions, namely, micro-nodule, ground-glass, and honeycomb patterns were simulated with interstitial lung disease on a chest phantom. Chest images with and without simulated lesions were exposed and compared with standard images and subtraction images. We carried out evaluations with and without subtraction images and performed the analysis by using receiver operating characteristic (ROC) analysis of detection. Results showed that the detection of interstitial lung diseases was significantly improved by the use of subtraction images. The area under the ROC curve (AUC) values of micro-nodule images obtained with and without subtraction images were 0.768 and 0.963, ground-glass images 0.670 and 0.917, and honeycomb images 0.768 and 0.996, respectively. A significant difference of p<0.05 was accepted. The use of dual-energy subtraction radiography with a FPD improved diagnostic accuracy in detecting cases of multiple interstitial lung diseases and was considered useful.     

Effect of temporal subtraction technique on the diagnosis of primary lung cancer with chest radiography

PURPOSE: The purpose of this study was to assess the diagnostic accuracy of the temporal subtraction technique in the detection of primary lung cancers by readers with different levels of experience. METHODS: Previous and current chest radiographs from 40 patients with histologically proven lung cancer and 40 controls were studied. Temporal subtraction images were produced using an automated digital subtraction technique. We evaluated the effect of temporal subtraction images in the diagnosis of lung cancer with chest radiographs via an observer performance study with the use of receiver operating characteristic analysis. Six experienced radiologists and six residents participated as observers. RESULTS: Observer performance for all observers was superior when temporal subtraction images were used (mean Az value increased from 0.764 to 0.836, p=0.0006). Although the average Az value for residents increased significantly, from 0.707 to 0.795 (p=0.0038), the average Az value for experienced radiologists increased only from 0.821 to 0.878 (n.s.). CONCLUSION: In conclusion, the temporal subtraction technique clearly improves diagnostic accuracy for the detection of primary lung cancer. The results indicated that the use of temporal subtraction images was more beneficial for the residents than for the experienced radiologists. This method would compensate to some extent for experience-dependent diagnostic accuracy in the detection of lung cancer.     

Temporal subtraction for detection of solitary pulmonary nodules on chest radiographs: evaluation of a commercially available computer-aided diagnosis system

PURPOSE: To evaluate the usefulness of a commercially available computer-aided diagnosis (CAD) system that incorporates temporal subtraction for the detection of solitary pulmonary nodules on chest radiographs by readers with different levels of experience. MATERIALS AND METHODS: Sixty pairs of chest radiographs in 30 patients with newly detected solitary pulmonary nodules and 30 normal cases, all confirmed with serial chest computed tomography (CT), were obtained from screen-film or digital radiographic systems and were digitized (spatial resolution, 0.171 mm/pixel). Temporal subtraction images were produced with an iterative image-warping technique. Five chest radiologists and five residents evaluated both image sets for solitary nodules: set A, current and prior radiographs with temporal subtraction images, and set B, current and prior radiographs only. Assessment was performed with receiver operating characteristic (ROC) analysis of the images on a monitor (pixel size, 1,280 x 1,024) equipped with the system. The reading time needed by each reader was recorded in each case. RESULTS: For the chest radiologists, no statistically significant difference was found between set A (area under the ROC curve [A(z)] = 0.934) and set B (A(z) = 0.964). For the residents, however, observer performance in set A (A(z) = 0.907) was superior to that in set B (A(z) = 0.855) (P <.05). For both groups, the mean reading time per case for set A (chest radiologists, 16.7 seconds; residents, 15.7 seconds) was significantly (P <.05) shorter than that for set B (chest radiologists, 20.4 seconds; residents, 26.2 seconds). CONCLUSION: For the detection of solitary pulmonary nodules, the CAD system with temporal subtraction can promote efficiency for established chest radiologists and improvement in accuracy for less experienced readers.     

Detectability of lung nodules using flat panel detector with dual energy subtraction by two shot method: evaluation by ROC method

The aim of this study was to evaluate the effectiveness of dual-exposure dual energy subtraction technique in flat-panel chest radiography for lung nodules detection. Chest radiographs were acquired in 100 patients (57 men and 43 women; mean age, 60.2 years; range, 18-89 years) using a flat-panel digital chest system. These images were evaluated by seven radiologists. A continuous rating scale of 0-100 was used to represent each observer's confidence level regarding the presence or absence of lung nodules. Observer performance for detection of lung nodules with subtraction images was tested by using receiver operating characteristic (ROC) analysis of individual and averaged reader data. The average area under the ROC curve (Az value) significantly increased with subtraction images (Az=0.79 in standard radiographs versus Az=0.84 with subtraction images, p<0.05). In conclusion, the two-exposure dual-energy subtraction chest radiography significantly would improve detection of lung nodules.     

Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs

OBJECTIVE: We developed a new method to distinguish between various interstitial lung diseases that uses an artificial neural network. This network is based on features extracted from chest radiographs and clinical parameters. The aim of our study was to evaluate the effect of the output from the artificial neural network on radiologists' diagnostic accuracy. MATERIALS AND METHODS: The artificial neural network was designed to differentiate among 11 interstitial lung diseases using 10 clinical parameters and 16 radiologic findings. Thirty-three clinical cases (three cases for each lung disease) were selected. In the observer test, chest radiographs were viewed by eight radiologists (four attending physicians and four residents) with and without network output, which indicated the likelihood of each of the 11 possible diagnoses in each case. The radiologists' performance in distinguishing among the 11 interstitial lung diseases was evaluated by receiver operating characteristic (ROC) analysis with a continuous rating scale. RESULTS: When chest radiographs were viewed in conjunction with network output, a statistically significant improvement in diagnostic accuracy was achieved (p < .0001). The average area under the ROC curve was .826 without network output and .911 with network output. CONCLUSION: An artificial neural network can provide a useful "second opinion" to assist radiologists in the differential diagnosis of interstitial lung disease using chest radiographs.     

Computer-aided diagnosis and artificial intelligence in clinical imaging

Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and communication systems and will become a standard of care for diagnostic examinations in daily clinical work.     

Improved detection of lung cancer arising in diffuse lung diseases on chest radiographs using temporal subtraction

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate the usefulness of temporal subtraction for the detection of lung cancer arising in pneumoconiosis, idiopathic pulmonary fibrosis, and pulmonary emphysema. MATERIALS AND METHODS: Fifteen cases of lung cancer arising in diffuse lung diseases, including three cases of pneumoconiosis, six of idiopathic pulmonary fibrosis, and six of pulmonary emphysema, were evaluated. Pathologic proof was obtained by surgery or transbronchial lung biopsy. The average interval between previous and current radiographs was 356 days (range, 31-947 days). All chest radiographs were obtained with a computed radiography system, and temporal subtraction images were produced by subtracting of a previous image from a current one with a nonlinear image-warping technique. The effect of the temporal subtraction image was evaluated by observer performance study with receiver operating characteristic analysis. RESULTS: The average observer performance with temporal subtraction was significantly improved (Az = 0.935) compared with that without temporal subtraction (Az = 0.857, P < .0001). CONCLUSION: The temporal subtraction technique is useful for the detection of lung cancer arising in pneumoconiosis, idiopathic pulmonary fibrosis, and pulmonary emphysema.     

Computer-aided diagnosis in chest radiography.

We have developed computer-aided diagnosis (CAD) schemes for the detection of lung nodules, interstitial lung diseases, interval changes, and asymmetric opacities, and also for the differential diagnosis of lung nodules and interstitial lung diseases on chest radiographs. Observer performance studies indicate clearly that radiologists' diagnostic accuracy was improved significantly when radiologists used a computer output in their interpretations of chest radiographs. In addition, the automated recognition methods for the patient and the projection view by use of chest radiographs were useful for integrating the chest CAD schemes into the picture-archiving and communication system (PACS).     

Detection of lung nodules on digital chest radiographs: potential usefulness of a new contralateral subtraction technique

PURPOSE: To evaluate the potential usefulness of a contralateral subtraction technique developed for radiologists' performance in the detection of subtle lung nodules on chest radiographs. MATERIALS AND METHODS: Fifty chest radiographs (25 normal and 25 abnormal with a subtle lung nodule) that were digitized with a 0.175-mm pixel size and 4,096 gray levels were used. Twelve radiologists (10 attending and two residents) participated in observer tests and read both original and contralateral subtraction images with a sequential testing method. Radiologists' performance was evaluated by means of receiver operating characteristic analysis with use of a continuous rating scale. The beneficial and detrimental effects of the contralateral subtraction technique on the radiologists' performance were also evaluated. RESULTS: The area under the receiver operating characteristic curve values obtained without and with contralateral subtraction images were 0.926 and 0.962, respectively. Results indicated that the contralateral subtraction images significantly (P <.05) improved diagnostic accuracy, particularly for radiologists with limited experience. CONCLUSION: The contralateral subtraction technique can assist radiologists in the correct identification of subtle lung nodules on chest radiographs.     

Computer-aided diagnosis in the detection of temporal changes in sequential chest radiographs

To aid radiologists in the diagnosis of screening chest radiographs, a temporal subtraction technique using digital image processing was developed. The accurate image registration of two sequential images enables us to detect even subtle changes in the "difference image" between them. In this report, a new method based on matching "lung markings" is introduced. Twenty-nine pairs of sequential posteroanterior chest radiographs with and without temporal changes were selected from cases examined with the computed radiography system. Image registration was employed, with the local matching of "lung markings" in previous and current radiographs. Observer performance tests were carried out by eight radiologists, with and without the "difference image." Observer performance tests with the temporal subtraction image showed that six of eight observers diagnosed them with higher sensitivity (mean, 43.9% vs. 55.3%) and a comparable false positive response. Mean area under the AFROC (alternative free-response receiver operating characteristics) curve also improved from 0.596 to 0.647, a statistically significant difference. The subtraction image using this registration technique improved diagnostic accuracy for subtle temporal changes.     

Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a "second opinion" and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists' accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.     

Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease: results of a simulation test with actual clinical cases

RATIONALE AND OBJECTIVES: To evaluate the performance of an artificial neural network (ANN) scheme with use of consecutive clinical cases and its effect on radiologists with an observer test. MATERIALS AND METHODS: Artificial neural networks were designed to distinguish among 11 interstitial lung diseases on the basis of 26 inputs (16 radiologic findings, 10 clinical parameters). Chest radiographs of 96 consecutive cases with interstitial lung disease were used. Five radiologists independently rated their radiologic findings on the 96 chest radiographs. Based on their ratings of radiologic findings and clinical parameters obtained from the hospital information system, the output values indicating the likelihood of each of the 11 interstitial lung diseases were determined. Subsequently, 30 cases were selected from these 96 cases for an observer test. Five radiologists marked their confidence levels for diagnosis of 11 possible diseases in each case without and with ANN output. The performance of ANNs and radiologists was evaluated by receiver operating characteristic analysis based on their outputs and on confidence levels, respectively. RESULTS; The average Az value (area under the receiver operating characteristic curve) indicating ANN performance for the 96 consecutive cases was 0.85 +/- 0.03. The average Az values indicating radiologists' performance without and with ANN outputs were 0.81 +/- 0.11 and 0.87 +/- 0.06, respectively. The diagnostic accuracy was improved significantly when radiologists read chest radiographs with ANN outputs (P < .05). CONCLUSION: Artificial neural networks for differential diagnosis of interstitial lung disease may be useful in clinical situations, and radiologists may be able to utilize the ANN output to their advantage in the differential diagnosis of interstitial lung disease on chest radiographs.     

ROC Analysis of Detection of Metastatic Pulmonary Nodules on Digital Chest Radiographs with Temporal Subtraction

RATIONALE AND OBJECTIVES: The authors' purpose was to evaluate the effect of temporal subtraction on digital chest radiographs in the detection of metastatic pulmonary nodules. MATERIALS AND METHODS: The study included 21 cases with metastatic pulmonary nodule and 21 cases without metastatic nodule. Eleven radiologists, including eight residents and three certified radiologists, provided their confidence levels for the presence or absence of pulmonary nodules without and with temporal subtraction. Their performances without and with temporal subtraction were evaluated by means of receiver operating characteristic analysis with both independent and sequential tests. RESULTS: For the independent test, the radiologists' Az (area under the receiver operating characteristic curve) values were 0.871 without and 0.954 with temporal subtraction, compared with 0.882 and 0.955, respectively, for the sequential test. Diagnosis accuracy was significantly improved with the use of temporal subtraction. There was no significant difference in Az values between the independent and sequential tests. CONCLUSION: Temporal subtraction is useful in the detection of metastatic pulmonary nodules, and this technique augments the value of digital chest radiography.     

Assessment of the severity of interstitial lung diseases in chest radiographs using a computer-aided analysis system. A comparison with X-ray CT]

A new computer-aided system to assess the severity of interstitial lung diseases in chest radiographs using proposed parameters, radiographic indices, was developed. The radiographic index is the normalized percent area of shadows in a ROI selected in the lung fields of chest radiographs. In the right lungs of 82 patients, 164 ROIs were processed by computerized methods such as filtering, binarization, determination and subtraction of linear shadows. In subtraction method, the image processed with determination of linear shadows is subtracted from the image filtered by 4-directional Laplacian-Gaussian filters. Then, the radiographic indices, Dbin (binarization), Dlin (linear shadow determination) and Dsub (subtraction), were obtained for the corresponding images. The combined radiographic index "Dcom" was calculated from "Dlin" and "Dsub". Values of these indices of the ROIs were compared with the grade (0-4) of the severity of interstitial involvement evaluated by CT. The results were obtained as follows: 1) Dbin, Dlin and Dcom provided good correlation with the severity throughout 5 grades, while Dsub did not. 2) As a parameter for detection of interstitial lung diseases, Dcom was significantly superior to the other indices (p less than .05). 3) As a parameter for assessment of severity of the diseases, Dlin showed the best performance and Dcom was the second best. Dlin and Dcom were superior to Dbin, and Dsub was inferior to Dbin. These results indicate that a new system is useful for assessment of the severity of interstitial lung diseases in chest radiographs.     

Clinical evaluation of pulmonary nodules with dual-exposure dual-energy subtraction chest radiography

PURPOSE: The purpose of this study was to assess the effect of dual-exposure dual-energy (DE) subtraction chest radiography with flat-panel detector. MATERIALS AND METHODS: One hundred patients underwent dual-exposure DE subtraction chest radiography and chest CT for evaluation of pulmonary nodules. Fifty-two patients with pulmonary nodules and 48 patients with normal lungs were selected for receiver operating characteristic (ROC) curve analysis. Ten radiologists who were unaware of the CT results evaluated chest radiography alone and chest radiography with DE subtraction images in the detection of pulmonary nodules. For each radiologist, we calculated the areas under the ROC curve (Az) for chest radiography alone and chest radiography with DE subtraction images. RESULTS: The average detectability of dual-exposure DE subtraction chest radiography was statistically significantly higher than that of chest radiography without subtraction images (mean Az value increased from 0.784 to 0.815, p<0.001). CONCLUSION: Dual-exposure DE subtraction chest radiography improves diagnostic accuracy of pulmonary nodules.     

Effect of temporal subtraction images on radiologists' detection of lung cancer on CT: results of the observer performance study with use of film computed tomography images

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.