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Denise R Aberle

Publications and source records attributed to Denise R Aberle.

17 recordsLinked to original sources
PubMed2006

Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps.

PURPOSE: To determine whether conventional nodule densitometry or analysis based on contrast enhancement maps of indeterminate lung nodules imaged with contrast-enhanced CT can distinguish benign from malignant lung nodules. MATERIALS AND METHOD: Thin section, contrast-enhanced CT (baseline, and post-contrast series acquired at 45, 90,180, and 360 s) was performed on 29 patients with indeterminate lung nodules (14 benign, 15 malignant). A thoracic radiologist identified the boundary of each nodule using semi-automated contouring to form a 3D region-of-interest (ROI) on each image series. The post-contrast series having the maximum mean enhancement was then volumetrically registered to the baseline series. The two series were subtracted volumetrically and the subtracted voxels were quantized into seven color-coded bins, forming a contrast enhancement map (CEM). Conventional nodule densitometry was performed to obtain the maximum difference in mean enhancement values for each nodule from a circular ROI. Three thoracic radiologists performed visual semi-quantitative analysis of each nodule, scoring each map for: (a) magnitude and (b) heterogeneity of enhancement throughout the entire volume of the nodule on a five-point scale. Receiver operator characteristic (ROC) analysis was conducted on these features to evaluate their diagnostic efficacy. Finally, 14 quantitative texture features were calculated for each map. A statistical analysis was performed to combine the 14 texture features to a single factor. ROC analysis of the derived aggregate factor was done as an indicator of malignancy. All features were analyzed for differences between benign and malignant nodules. RESULTS: Using 15 HU as a threshold, 93% (14/15) of malignant and 79% (11/14) of benign nodules demonstrated enhancement. The ROC curve when higher values of enhancement indicate malignancy was generated and area under the curve (AUC) was 0.76. The visually scored magnitude of enhancement was found to be less effective in distinguishing malignant from benign lesions, with an average AUC of 0.62. The visually scored pattern of enhancement was found to be more effective with an average AUC of 0.79. From the statistical analysis performed to combine the texture features to a single factor, the area under the ROC curve was 0.84. CONCLUSION: The present study suggests that visual semi-quantitative and quantitative characterization of contrast enhancement patterns may potentially enhance the discrimination between benign and malignant nodules. Further studies and correlation with pathologic material will be important to better understand the potential interplay between CT enhancement features, host stromal elements, and neovascularity that may contribute to these patterns.

Adult↗
PubMed2006

Evaluation of lung MDCT nodule annotation across radiologists and methods.

RATIONALE AND OBJECTIVES: Integral to the mission of the National Institutes of Health-sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary. MATERIALS AND METHODS: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists' spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects. RESULTS: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively. CONCLUSION: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume.

Artificial Intelligence↗
PubMed2006

Description and implementation of a quality control program in an imaging-based clinical trial.

RATIONALE AND OBJECTIVES: The American College of Radiology Imaging Network is participating in the National Lung Screening Trial, a large, multicenter, randomized controlled trial, comparing multidetector helical computed tomography (MDCT) versus chest radiography (CXR) in screening for lung cancer. Because the threshold for detection of disease is an inherent function of image quality, and consistent image quality is necessary to track changes in suspicious findings, our purpose was to develop an image quality control (QC) program across all clinical sites for both modalities. MATERIALS AND METHODS: The primary goals of the QC program include standardization of imaging protocols, certification of imaging equipment, and ongoing, periodic evaluation of the equipment calibration and image quality. Minimum standards for equipment and standardized cross-platform acquisition protocols are achieved via radiologist and physicist attestation forms and web-distributed technique charts, respectively. Imaging equipment performance standards are implemented through an initial machine certification process that includes equipment calibration. Ongoing assessment of equipment performance and calibration, as well as adherence to established imaging protocols. is accomplished via periodic submission of calibration records and phantom images. Participant-specific image acquisition parameters are entered into a web-based centralized database and variations from established protocols are automatically flagged for review. Participant radiation dose can be estimated from the image acquisition parameters applied to the imaging equipment calibration measurements. A radiologist visual review committee also evaluates participant images for diagnostic quality. Data are collected from 23 independent centers, representing 14 models of MDCT scanners from four manufacturers, and CXR systems that include film-screen, computed radiography, and direct digital radiography systems. RESULTS: Widespread imaging protocol variation in extant clinical practice-as well as variability in equipment technology, image acquisition parameters, manufacturer terminology, and user interface-have required careful standardization as a prerequisite to trial participation and ongoing image QC. Acceptable ranges for image acquisition parameters have been refined to accommodate continuously evolving equipment platforms and the scope of participant size and body habitus. CONCLUSION: Standardization of imaging protocols is a critical component of image-based clinical trials, predicated on ongoing dialogue between sites and a centralized review committee.

Artificial Intelligence↗
PubMed2005

Pulmonary neovascularity: a distinctive radiographic finding in Eisenmenger syndrome.

BACKGROUND: We sought to characterize the distinctive pulmonary vascular abnormalities seen on chest radiographs and computed tomography (CT) scans in Eisenmenger syndrome. METHODS AND RESULTS: Thoracic CT scans, chest radiographs, and clinical data were reviewed for 24 Eisenmenger syndrome patients subdivided into those with interatrial (pretricuspid) versus interventricular or great arterial (posttricuspid) communications and in 14 acyanotic patients with pulmonary arterial hypertension (PAH) and no congenital heart disease. CT scans were scored blindly by 2 thoracic radiologists for the presence and severity of small, tortuous, intrapulmonary vessels, termed "neovascularity," lobular ground-glass opacification, and systemic perihilar and intercostal vessels. Histopathologic lung sections from 5 patients with Eisenmenger syndrome and from 3 patients with acyanotic PAH were reviewed. Associations between clinical and imaging features were tested by ANOVA and chi2 tests. Kendall's rank-order coefficient and the Kruskal-Wallis test were used to test for significant differences in imaging features between Eisenmenger syndrome and acyanotic PAH. Neovascularity on chest radiographs was more common in Eisenmenger syndrome than acyanotic PAH, but differences were not significant. On CT, neovascularity, lobular ground-glass opacification, and hilar and intercostal systemic collaterals were more prevalent in Eisenmenger syndrome, with severity greater in posttricuspid communications. Three previously undescribed vascular lesions were identified histologically in Eisenmenger syndrome: malformed, dilated, muscular arteries within alveolar septa; congested capillaries within alveolar walls; and congested capillaries within the walls of medium-size, muscular pulmonary arteries. These lesions may correspond to the distinctive vascular abnormalities observed on chest radiographs and CT scans. CONCLUSIONS: Distinctive vascular lesions on chest radiographs and CT scans in Eisenmenger syndrome appear to be correlated histologically with collateral vessels that develop more extensively with posttricuspid communications.

Adult↗
PubMed2005

Solitary pulmonary nodule diagnosis on CT: results of an observer study.

RATIONALE AND OBJECTIVES: To investigate the performance of observers with different levels of experience in distinguishing between benign and malignant solitary pulmonary nodules (SPN) on CT, and to determine the effects on interpretation of three different conditions: image data alone, the addition of clinical data, and the addition of output from a computer-aided diagnosis (CAD) system. MATERIALS AND METHODS: 28 thin-section CT datasets of SPNs with proven diagnoses (15 malignant and 13 benign) were used to measure observer performance. Readers were categorized according to their experience and read the cases in random order. For each case readers were asked to assign a level of confidence on a scale from 0.0-1.0 (0.0 benign, 1.0 malignant) for the diagnosis of the nodule. Each reader scored the cases based on review of image data alone (phase 1), then with limited clinical data (phase 2), and finally with CAD output (phase 3). To assess performance, multiple reader multiple case (MRMC) receiver operating characteristic (ROC) analysis was used. RESULTS: 2 thoracic radiologists, 1 thoracic radiology fellow, 2 nonthoracic radiologists, and 3 radiology residents read the cases. The average area under the ROC curve for all readers (A(z)) at each stage was 0.68, 0.75, and 0.81, for image data alone, with clinical data, and with CAD output respectively. The difference in performance between phases (2 and 3) and (1 and 3) was significantly different (P = 0.018 and P = 0.020). However, the difference between phases (1 and 2) was not significantly different (P = 0.155). CONCLUSION: Diagnostic performance increased significantly with the addition of CAD output. With further validation CAD output may play a significant role in SPN management.

Diagnosis, Computer-Assisted↗
PubMed2005

Computer-aided diagnosis of the solitary pulmonary nodule.

RATIONALE AND OBJECTIVES: We sought to investigate the utility of a computer-aided diagnosis in the task of differentiating malignant nodules from benign nodules based on single thin-section computed tomography image data. MATERIALS AND METHODS: Eighty-one thin-section computed tomography data sets of solitary pulmonary nodules with proven diagnoses (48 malignant and 33 benign) were contoured manually on a single representative slice by a thoracic radiologist (>10 years of experience). Two separate contours were created for each nodule, one including only the solid portion of the nodule and one including any ground-glass components. For each contour 75 features were calculated that measured the attenuation, shape, and texture of the nodule. These features were than input into a feature selection step and four different classifiers to determine if the diagnosis could be predicted from the feature vector. Training and testing was conducted in a resubstitution and leave-one-out fashion and performance was evaluated using ROC techniques. RESULTS: In a leave-one-out testing methodology the classifiers resulted with areas under the ROC curve (A(Z)) that ranged from 0.68 to 0.92. When evaluating with resubstitution the A(Z) ranged from 0.93 to 1.00. CONCLUSION: Computer-aided diagnosis has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients.

Diagnosis, Differential↗
PubMed2005

Computer-aided lung nodule detection in CT: results of large-scale observer test.

RATIONALE AND OBJECTIVES: The objective is to study the incremental effects of using a computer-aided lung nodule detection (CAD) system on the performance of a large pool of observers. MATERIALS AND METHODS: A set of eight thin-section computed tomographic data sets with limited longitudinal coverage, containing a total of 22 lung nodules, was analyzed by using the automated nodule detection system. When applied to all eight cases, the CAD system alone achieved a detection rate of 86.4%, with 2.64 false-positive results per case. This study included 202 observers at a national radiology meeting: 39 thoracic radiologists, 95 non-thoracic radiologists, and 68 non-radiologists. Each participant read from one to eight cases in random order, first without and then with CAD system output available. Observer performance in nodule detection was measured before and after CAD was made available. Differences in performance of groups of observers before and after CAD were tabulated by mean, median, and SD in detection rate and number of false-positive results and tested by using nonparametric methods. RESULTS: In an analysis involving only the first randomly selected case read by all 202 participants, there were statistically significant increases in nodule detection rates and numbers of false-positive results for all types of observers. There was a significant difference in detection rates between radiologists and non-radiologists before CAD, but after CAD, there was no significant difference in detection rates between these observer types. In a second analysis involving 13 participants who read all eight cases, mean detection rates were 64.0% before CAD and 81.9% after CAD. Mean numbers of false-positive results were 0.144 per case before CAD and 0.173 after CAD. CONCLUSION: In a large observer study, use of a CAD system for nodule detection resulted in an incremental increase in detection rate, but also led to an increase in number of false-positive results. Also, CAD appears to be an equalizer of detection rates between observers of different levels of experience.

Clinical Competence↗
PubMed2005

Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features.

RATIONALE AND OBJECTIVES: To investigate the utility of a computer-aided diagnosis (CAD) in the task of differentiating malignant nodules from benign nodules based on quantitative features extracted from volumetric thin section CT image data acquired before and after the injection of contrast media. MATERIALS AND METHODS: 35 volumetric CT datasets of solitary pulmonary nodules (SPN) with proven diagnoses (19 malignant/16 benign) were contoured by a thoracic radiologist. All studies had at least a baseline series obtained without contrast media and at least one series following an intravenous contrast injection at 45, 90, 180, and 360 seconds. Two separate contours were created for each nodule: one including only the solid portion and another including the ground-glass component, if any, of the nodule. For each contour 31 features were calculated that measured the attenuation, shape, and enhancement of the nodule due to the injection of contrast. These features were input into a feature selection step and three different classifiers to determine if the diagnosis could be predicted from the resulting feature vector. In addition, observer input was introduced to two of the classifiers as an a priori probability of malignancy and the resulting performance was compared. Training and testing was conducted in a resubstitution and leave-one-out fashion and performance was evaluated using ROC analysis. RESULTS: In a leave-one-out testing methodology, the classifiers achieved areas under the ROC curves AZ that ranged from 0.69 to 0.92. A classifier based on logistic regression performed the best with an AZ of 0.92 while a classifier based on quadratic discriminant analysis performed the poorest (AZ, 0.69). The AZ increased when using a priori observer input in most cases reaching a maximum of 0.95. CONCLUSION: Based on this initial work with a limited number of nodules in our dataset, it appears that CAD using volumetric and contrast-enhanced data has the potential to assist radiologists in the task of differentiating solitary pulmonary nodules and in the management of these patients. Further studies with an increased number of patients are required to validate these results.

Adult↗
PubMed2005

Database design and implementation for quantitative image analysis research.

Quantitative image analysis (QIA) goes beyond subjective visual assessment to provide computer measurements of the image content, typically following image segmentation to identify anatomical regions of interest (ROIs). Commercially available picture archiving and communication systems focus on storage of image data. They are not well suited to efficient storage and mining of new types of quantitative data. In this paper, we present a system that integrates image segmentation, quantitation, and characterization with database and data mining facilities. The paper includes generic process and data models for QIA in medicine and describes their practical use. The data model is based upon the Digital Imaging and Communications in Medicine (DICOM) data hierarchy, which is augmented with tables to store segmentation results (ROIs) and quantitative data from multiple experiments. Data mining for statistical analysis of the quantitative data is described along with example queries. The database is implemented in PostgreSQL on a UNIX server. Database requirements and capabilities are illustrated through two quantitative imaging experiments related to lung cancer screening and assessment of emphysema lung disease. The system can manage the large amounts of quantitative data necessary for research, development, and deployment of computer-aided diagnosis tools.

Algorithms↗
PubMed2005

Imaging and cancer: research strategy of the American College of Radiology Imaging Network.

The American College of Radiology Imaging Network (ACRIN) is a cooperative group funded by the National Cancer Institute and dedicated to developing and conducting clinical trials of diagnostic imaging and image-guided treatment technologies. ACRIN's six disease site committees are responsible for developing scientific strategies and resultant trials within the framework of ACRIN's five key hypotheses: (a) Screening and early detection with imaging can reduce cancer-specific mortality. (b) Less invasive image-guided therapeutic methods can reduce the mortality and morbidity associated with treating cancer. (c) Molecular-based physiologic and functional imaging can improve the diagnosis and staging of cancer, thus improving treatment. (d) Functional imaging can portray the effectiveness of treatment earlier and more accurately, thus reducing mortality and improving the likelihood of a cure. (e) Informatics and other "smart systems" can improve the evaluation of patients with cancer, thus leading to better and more effective treatments. This article details ACRIN's research strategy according to disease site through the year 2007.

Breast Neoplasms↗
PubMed2004

Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography.

RATIONALE AND OBJECTIVES: To study the agreement in treatment response classifications between unidimensional (1D), bidimensional (2D), and volumetric (3D) methods of measuring metastatic lung nodules on chest computed tomography (CT). MATERIALS AND METHODS: Chest CT scans of 15 patients undergoing treatment for metastatic colorectal, renal cell, or breast carcinoma to the lungs were analyzed. CT images were acquired with 3 mm collimation and contiguous reconstruction. Two or three lung lesions were selected for each patient. Lesions were analyzed at baseline and two follow-up intervals of 1-4 months. 1D and 2D measurements were made with electronic calipers, while nodule volume was measured using a semiautomated segmentation system. Following the World Health Organization and RECIST (Response Evaluation Criteria in Solid Tumors) criteria, patients were categorized into four treatment response classifications. Volumetric criteria were used to classify response based on 3D measurements. RESULTS: Thirty-two lesions from 15 patients were analyzed. Because each patient had a baseline and two follow-up scans, this yielded 30 response classifications for each measurement technique. The 1D, 2D, and 3D measurements were concordant in 21 of 30 classifications. The 1D and 3D measurements were concordant in 29 of 30 classifications, while the 2D and 3D measurements were concordant in 23 of 30 classifications. Level of agreement among the three methods was measured using a kappa statistic (K). For 1D compared with 3D, K = 0.739 +/- 0.345 (visits 1, 2) and 0.273 +/- 0.323 (visits 2, 3). For 2D compared with 3D, K = 0.655 +/- 0.325 (visits 1, 2) and 0.200 +/- 0.208 (visits 2, 3). Agreement among the methods for round and ovoid nodules was also fair to poor. CONCLUSION: The three methods of tumor measurement show fair to poor agreement in treatment response classification. These findings have negative implications for the accuracy in which patients are classified under the World Health Organization or RECIST criteria and managed under cancer treatment protocols.

Breast Neoplasms↗
PubMed2004

Effect of an imaging-based streamlined electronic healthcare process on quality and costs.

RATIONALE AND OBJECTIVES: A streamlined process of care supported by technology and imaging may be effective in managing the overall healthcare process and costs. This study examined the effect of an imaging-based electronic process of care on costs and rates of hospitalization, emergency room (ER) visits, specialist diagnostic referrals, and patient satisfaction. MATERIALS AND METHODS: A healthcare process was implemented for an employer group, highlighting improved patient access to primary care plus routine use of imaging and teleconsultation with diagnostic specialists. An electronic infrastructure supported patient access to physicians and communication among healthcare providers. The employer group, a self-insured company, manages a healthcare plan for its employees and their dependents: 4,072 employees were enrolled in the test group, and 7,639 in the control group. Outcome measures for expenses and frequency of hospitalizations, ER visits, traditional specialist referrals, primary care visits, and imaging utilization rates were measured using claims data over 1 year. Homogeneity tests of proportions were performed with a chi-square statistic, mean differences were tested by two-sample t-tests. Patient satisfaction with access to healthcare was gauged using results from an independent firm. RESULTS: Overall per member/per month costs post-implementation were lower in the enrolled population (126 dollars vs 160 dollars), even though occurrence of chronic/expensive diseases was higher in the enrolled group (18.8% vs 12.2%). Lower per member/per month costs were seen for inpatient (33.29 dollars vs 35.59 dollars); specialist referrals (21.36 dollars vs 26.84 dollars); and ER visits (3.68 dollars vs 5.22 dollars). Moreover, the utilization rate for hospital admissions, ER visits, and traditional specialist referrals were significantly lower in the enrolled group, although primary care and imaging utilization were higher. Comparison to similar employer groups showed that the company's costs were lower than national averages (119.24 dollars vs 146.32 dollars), indicating that the observed result was not attributable to normalization effects. Patient satisfaction with access to healthcare ranked in the top 21st percentile. CONCLUSION: A streamlined healthcare process supported by technology resulted in higher patient satisfaction and cost savings despite improved access to primary care and higher utilization of imaging.

Electronics, Medical↗
PubMed2004

Lung image database consortium: developing a resource for the medical imaging research community.

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.

Biomedical Research↗
PubMed2004

Automated medical problem list generation: towards a patient timeline.

The problem-oriented electronic medical record has been investigated as an alternative to source-oriented organization of patient data. At the core of a problem-oriented view is the medical problem list. Maintenance of the medical problem list is often manual, making it highly user dependent. We detail the beginnings of an automated medical problem list generator based on ICD-9: given a set of ICD-9 codes associated with a patient record, the system maps the codes (and related data) to an anatomy-centric hierarchy. 1 million patient encounters from an outpatient setting were reviewed to generate a unique set of 7,890 ICD-9 codes. Natural language processing of the ICD-9 string descriptions identified 1,981 anatomical terms, which were subsequently mapped to one of 21 anatomical categories. The output of the medical problem list generator was then used to create a problem-oriented, gestalt view of a patient's medical record. Preliminary evaluation of the generator revealed 100% recall, but only 60% precision. This initial work has highlighted several issues in defining a medical problem list, including questions of granularity and performance trade-offs.

Electronic Data Processing↗
PubMed2003

Lung micronodules: automated method for detection at thin-section CT--initial experience.

An automated system was developed for detecting lung micronodules on thin-section computed tomographic images and was applied to data from 15 subjects with 77 lung nodules. The automated system, without user interaction, achieved a sensitivity of 100% for nodules (>3 mm in diameter) and 70% for micronodules (<or=3 mm). With the same images, a radiologist detected nodules and micronodules with sensitivities of 91% and 51%, respectively, without system input. With assistance from the automated system, these sensitivities increased to 95% and 74%, respectively. Preliminary results indicate that the automated system considerably improved the radiologist's performance in micronodule detection.

Aged↗
PubMed2002

Evidence-based radiology: requirements for electronic access.

RATIONALE AND OBJECTIVES: The purpose of this study was to determine the electronic requirements for supporting evidence-based radiology in today's medical environment. MATERIALS AND METHODS: A software engineering technique, use case modeling, was performed for several clinical settings to determine the use of imaging and its role in evidence-based practice, with particular attention to issues relating to data access and the usage of clinical information. From this basic understanding, the analysis was extended to encompass evidence-based radiologic research and teaching. RESULTS: The analysis showed that a system supporting evidence-based radiology must (a) provide a single point of access to multiple clinical data sources so that patient data can be readily used and incorporated into comprehensive radiologic consults and (b) provide quick access to external evidence in the way of similar patient cases and published medical literature, thus supporting evidence-based practice. CONCLUSION: Information infrastructures that aim to support evidence-based radiology not only must address issues related to the integration of clinical data from heterogeneous databases, but must facilitate access and filtering of patient data in order to improve radiologic consultation.

Evidence-Based Medicine↗