Disease volumes as a marker for patient response in malignant pleural mesothelioma.

BACKGROUND: The goal of this study was to create a comprehensive model for malignant pleural mesothelioma patient survival utilizing continuous, time-varying estimates of disease volume from computed tomography (CT) imaging in conjunction with clinical covariates. PATIENTS AND METHODS: Serial CT scans were obtained during the course of clinically standard chemotherapy for 81 patients. The pleural disease volume was segmented for each of the 281 CT scans, and relative changes in disease volume from the baseline scan were tracked over the course of serial follow-up imaging. A prognostic model was built using time-varying disease volume measurements in conjunction with clinical covariates. RESULTS: Over the course of treatment, disease volume decreased by an average of 19%, and median patient survival was 12.6 months from baseline. In a multivariate survival model, changes in disease volume were significantly associated with patient survival along with disease histology, Eastern Cooperative Oncology Group performance status, and presence of dyspnea. CONCLUSIONS: Analysis of the trajectories of disease volumes during chemotherapy for patients with mesothelioma indicates that increasing disease volume was significantly and independently associated with poor patient prognosis in both univariate and multivariate survival models.

The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.

Critical to the clinical evaluation of effective novel therapies for lung cancer is the early and accurate determination of tumor response, which requires an understanding of the sources of uncertainty in tumor measurement and subsequent attempts to minimize their effects on the assessment of the therapeutic agent. The Reference Image Database to Evaluate Response (RIDER) project seeks to develop a consensus approach to the optimization and benchmarking of software tools for the assessment of tumor response to therapy and to provide a publicly available database of serial images acquired during lung cancer drug and radiation therapy trials. Images of phantoms and patient images acquired under situations in which tumor size or biology is known to be unchanged also will be provided. The RIDER project will create standardized methods for benchmarking software tools to reduce sources of uncertainty in vital clinical assessments such as whether a specific tumor is responding to therapy.

Automated detection of lung nodules in CT scans: preliminary results.

We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two-dimensional and three-dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section-by-section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray-level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18-point connectivity scheme is used to identify contiguous three-dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray-level features are computed for each nodule candidate. After a rule-based approach is applied to greatly reduce the number of nodule candidates that corresponds to nonnodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false-positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave-one-out-by-case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false-positive detections per section when applied to the complete 43-case database. A corresponding nodule detection sensitivity of 89% with an average of 1.3 false-positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case.

Automated registration of frontal and lateral radionuclide lung scans with digital chest radiographs.

RATIONALE AND OBJECTIVES: The purpose of this study was to develop and evaluate a fully automated method that spatially registers anterior, posterior, and lateral ventilation/perfusion (V/Q) images with posteroanterior and lateral digital chest radiographs to retrospectively combine the physiologic information contained in the V/Q scans with the anatomic detail in the chest radiographs. MATERIALS AND METHODS: Gray-level thresholding techniques were used to segment the aerated lung regions in the radiographic images. A variable-thresholding technique combined with an analysis of image noise was used to segment the adequately perfused or ventilated lung regions in the scintigraphic images. The physical dimensions of the segmented lung regions in images from both modalities were used to properly scale the radiographic images relative to the radionuclide images. Computer-determined locations of anatomic landmarks were then used to rotate and translate the images to achieve registration. Pairs of corresponding radionuclide and radiographic images were enhanced with color and then merged to create superimposed images. RESULTS: Five observers used a five-point rating scale to subjectively evaluate four image combinations for each of 50 cases. Of these ratings, 95.5% reflected very good, good, or fair registration. CONCLUSION: The automated method for the registration of radionuclide lung scans with digital chest radiographs to produce images that combine functional and structural information should benefit nuclear medicine physicians and radiologists, who must visually correlate images that differ greatly in physical size, resolution properties, and information content.

Computerized detection of pulmonary nodules on CT scans.

Helical computed tomography (CT) is the most sensitive imaging modality for detection of pulmonary nodules. However, a single CT examination produces a large quantity of image data. Therefore, a computerized scheme has been developed to automatically detect pulmonary nodules on CT images. This scheme includes both two- and three-dimensional analyses. Within each section, gray-level thresholding methods are used to segment the thorax from the background and then the lungs from the thorax. A rolling ball algorithm is applied to the lung segmentation contours to avoid the loss of juxtapleural nodules. Multiple gray-level thresholds are applied to the volumetric lung regions to identify nodule candidates. These candidates represent both nodules and normal pulmonary structures. For each candidate, two- and three-dimensional geometric and gray-level features are computed. These features are merged with linear discriminant analysis to reduce the number of candidates that correspond to normal structures. This method was applied to a 17-case database. Receiver operating characteristic (ROC) analysis was used to evaluate the automated classifier. Results yielded an area under the ROC curve of 0.93 in the task of classifying candidates detected during thresholding as nodules or nonnodules.

Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal a priori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computer-aided diagnostic scheme.

Automated lung segmentation in digital lateral chest radiographs.

We are developing a fully automated computerized scheme for segmenting the lung fields in digital lateral chest radiographs. Existing computer-aided diagnostic (CAD) schemes and automated lung segmentation methods have focused exclusively on the posteroanterior view, despite the diagnostic importance of the lateral view. Information from the lateral radiograph is routinely incorporated by radiologists in their decision-making process, and thus computer analysis of lateral images may potentially add another dimension to current CAD schemes. Automated analysis of the lung fields in lateral images will necessarily require accurate segmentation. Our scheme employs an initial procedure to eliminate external and subcutaneous pixels. Global gray-level histogram analysis then allows for the identification of a range of gray-level thresholds. An iterative gray-level thresholding scheme is implemented using this range of thresholds to construct a series of binary images in which contiguous regions are identified and geometrically analyzed. Regions determined to be outside the lungs are prevented from contributing to binary images at later iterations. Adaptive local gray-level thresholding is applied along the initial contour that results from the global thresholding procedure to extend the contour closer to the true lung borders. This local thresholding method uses regions of interest of various dimensions, depending on the enclosed anatomy. Smoothing and polynomial curve fitting complete the segmentation. A database of 100 normal and 100 abnormal lateral images was analyzed. Quantitative comparison of computer-segmented lung regions with lung regions manually delineated by two radiologists indicated that 83% and 84% of normal and abnormal images, respectively, displayed segmentation contours within three standard deviations of the mean inter-radiologist contour degree-of-overlap value.

Computerized delineation and analysis of costophrenic angles in digital chest radiographs.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for delineating the costophrenic angles in digital posteroanterior chest radiographs to derive quantitative information that allows for detection of abnormal blunting of the costophrenic angle. MATERIALS AND METHODS: An automated lung-segmentation scheme was used, and small regions of interest were placed in the approximate position of the costophrenic angles in 600 clinical posteroanterior chest radiographs to define a subimage for further analysis. The diaphragmatic aspect of the costophrenic angle was delineated based on column-wise contrast information, and the costal aspect was delineated based on row-wise gray-level maxima. The angle formed by the convergence of these two aspects provided the basis for assessing abnormality. Curve fitting was then performed on these segments to form a continuous costophrenic angle delineation. RESULTS: The computer-determined angles for 1,166 hemithoraces were compared with independent diagnostic assessments by a radiologist. An encouraging level of agreement was found between these two measurements, with the area under the receiver operating characteristic curve attaining a value of 0.83. CONCLUSION: This delineation method enhances the automated lung-segmentation scheme. Quantitative information obtained from the costophrenic angles can be used for automatic evaluation of the presence of costophrenic angle blunting, which may indicate the presence of pleural effusion.

Automated lung segmentation in digitized posteroanterior chest radiographs.

RATIONALE AND OBJECTIVES: The authors developed and tested a gray-level thresholding-based approach to automated lung segmentation in digitized posteroanterior chest radiographs. MATERIALS AND METHODS: Gray-level histogram analysis was initially performed to establish a range of thresholds for use during an iterative global gray-level thresholding technique. Local gray-level threshold analysis was then performed on the output of global thresholding. The resulting contours were subjected to several smoothing processes, including a rolling-ball technique. The final contours closely approximated the boundaries of the aerated lung regions. The method was applied to a database of 600 posteroanterior chest images. Radiologists rated the accuracy and completeness of the contours with a five-point scale. RESULTS: Results of the subjective rating evaluation indicated that this method was accurate, with 79% of the assigned ratings reflecting moderately or highly accurate segmentation and only 8% of the ratings indicating moderately or highly inaccurate segmentation. CONCLUSION: This gray-level thresholding-based approach provides accurate automated lung segmentation in digital posteroanterior chest radiographs.

Automated registration of ventilation-perfusion images with digital chest radiographs.

RATIONALE AND OBJECTIVES: The authors have developed an automated computerized technique for registering radionuclide lung scan images with digital chest radiographs. METHODS: Threshold analysis was used to construct contours around the high-activity regions of radionuclide ventilation-perfusion images. Analogous contours were constructed around the lung regions of the corresponding digitized radiographs. Contour dimensions and anatomic landmark locations were then used to superimpose the radiographic, ventilation, and perfusion images. RESULTS: Evaluation of 25 sets of images indicated that the scheme provided adequate to excellent registration in 91% of the pairwise combinations. CONCLUSION: This automated scheme for registering ventilation-perfusion images with digital chest radiographs has the potential to aid radiologists in the interpretation of these images.

Digital chest radiography: effect of temporal subtraction images on detection accuracy.

PURPOSE: To improve early detection of disease in chest radiographs, the authors developed a digital processing technique that geometrically warps and subtracts a previous radiograph from a current radiograph to produce a temporal subtraction image. An observer test was performed to evaluate the effects of the temporal subtraction image technique on detection of interval change. MATERIALS AND METHODS: Fifty pairs of chest radiographs, including a baseline examination and a subsequent radiograph, were selected (25 cases in which potentially important new abnormalities had developed, and 25 in which there was no interval change). The baseline examination was chosen from multiple prior radiographs to minimize initial misregistration. By means of receiver operating characteristic (ROC) analysis, the ability of 11 observers to detect pathologic change when viewing the paired digitized baseline and subsequent radiographs was compared with their ability when viewing the same paired radiographs together with temporal subtraction images. Positive cases demonstrated focal new abnormalities that were greater than 1 cm in diameter. RESULTS: The mean area (Az) under the ROC curves increased from 0.89 without to 0.98 with the temporal subtraction images. When the paired digitized previous and current chest radiographs were viewed in conjunction with the temporal subtraction images, a significant improvement in detection of new abnormalities was achieved (P = .00004), whereas the mean interpretation time was reduced by 19.3% (from 52 to 42 seconds, including the time to record the score and to move to the next case) (P = .0019). CONCLUSION: The temporal subtraction technique can significantly improve sensitivity and specificity for detection of interval change in chest radiographs.

Computerized detection of abnormal asymmetry in digital chest radiographs.

A technique for automated detection of abnormal asymmetry in digital chest radiographs is being developed. Such a method could be used to prescreen chest radiographs to bring obviously abnormal cases to the immediate attention of a radiologist. In addition, this technique may be used to detect large-area abnormalities which may cause other, more lesion-specific computer algorithms to fail. Asymmetric abnormalities are detected by multiple stages of gray-level thresholding. Lung contours are determined, and after a centroid test is used to eliminate contours external to the lungs, the areas of remaining contours are calculated. The present scheme, applied to a database of 70 chest images, yielded a sensitivity of 91% and a specificity of 80%.