Biomedical subjects

Anthony P Reeves

Publications and source records attributed to Anthony P Reeves.

6 recordsLinked to original sources

PubMed2006

On measuring the change in size of pulmonary nodules.

The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p = 0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.

Algorithms↗

PubMed2004

Computer-aided diagnostics.

The computer can be used in a number of ways to aid the physician to interpret CT lung images. Commercial tools are becoming available to assist the radiologist in growth rate determination, hence cancer diagnosis. Computer algorithms are in development that will permit lung health evaluation, including nodule detection. Finally, the results of such efforts will probably produce more detailed visualizations of the lung region, including depictions of the location and state of lung abnormalities. While computer methods have found a first application with the radiologist, these methods should also provide a valuable aid to surgery and pathology.

Diagnosis, Computer-Assisted↗

PubMed2004

Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT.

PURPOSE: To determine reproducibility of volume measurements of small pulmonary nodules on computed tomographic (CT) scans and to estimate critical time to follow-up CT. MATERIALS AND METHODS: One hundred fifteen pulmonary nodules for which two thin-section small-field-of-view CT scans were obtained and which were stable during 2-year observation were evaluated. A standard group of 94 nodules (with no or minimal artifact) and an expanded group of 105 nodules (including those with moderate artifacts) were examined. Percentage volume change (PVC) and monthly volumetric growth index (MVGI) were computed for each nodule pair. By using estimates of the variation in PVC in stable nodules as a function of initial diameter, critical time to follow-up CT was estimated; this time is the earliest point at which growth in a nodule of a given size can be reliably identified with repeat CT. RESULTS: The SD of PVC decreased with increasing nodule size from 18.5% in 2-5-mm nodules to 10.6% in 5-8-mm nodules and to 7.47% in 8-10-mm nodules. Inclusion of cases with moderate motion artifacts increased the SD of PVC to 27.4% in 2-5-mm nodules, to 17.1% in 5-8-mm nodules, and to 19.3% in 8-10-mm nodules. Critical time to follow-up CT for nodules detected at baseline screening was 12, 5, and 3 months and 1 month for those with initial sizes of 2, 5, 8, and 10 mm, respectively. For nodules detected at annual repeat screening, it was 4 and 3 months and 1 month for nodules that were 3, 4, and 5 mm or larger in size, respectively. Mean MVGI in 94 standard cases was 0.06%, and standard error was 0.21%. CONCLUSION: Factors that affect reproducibility of nodule volume measurements and critical time to follow-up CT include nodule size at detection, type of scan (baseline or annual repeat) on which the nodule is detected, and presence of patient-induced artifacts.

Artifacts↗

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↗

PubMed2003

Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images.

Small pulmonary nodules are a common radiographic finding that presents an important diagnostic challenge in contemporary medicine. While pulmonary nodules are the major radiographic indicator of lung cancer, they may also be signs of a variety of benign conditions. Measurement of nodule growth rate over time has been shown to be the most promising tool in distinguishing malignant from nonmalignant pulmonary nodules. In this paper, we describe three-dimensional (3-D) methods for the segmentation, analysis, and characterization of small pulmonary nodules imaged using computed tomography (CT). Methods for the isotropic resampling of anisotropic CT data are discussed. 3-D intensity and morphology-based segmentation algorithms are discussed for several classes of nodules. New models and methods for volumetric growth characterization based on longitudinal CT studies are developed. The results of segmentation and growth characterization methods based on in vivo studies are described. The methods presented are promising in their ability to distinguish malignant from nonmalignant pulmonary nodules and represent the first such system in clinical use.

Algorithms↗

PubMedDate not supplied

CT screening for lung cancer: significance of diagnoses in its baseline cycle.

PURPOSE: The aim of this study was to assess the significance of Stage I diagnoses of lung cancer in the baseline cycle of screening for this disease, with special reference to the potential for overdiagnosis. METHODS: We reviewed all 69 cases of Stage I lung cancer diagnosis resulting from our baseline CT screening. Among these 69 cases of lung cancer, 24 presented as solid, 30 as part-solid, and 15 as nonsolid nodules. The extent to which these represented genuine malignancy was assessed by a panel of experts on lung pathology, and the "aggressiveness" of these cases was addressed by the criterion of the tumor's volume doubling time being less than 400 days. RESULTS: The expert panel confirmed all 69 cases as representing genuine malignancy. Among the 69 cases without evidence of metastases, the proportion that satisfied the aggressiveness criterion was 60/69=87%. The corresponding proportions by presentation as solid, part-solid, and nonsolid nodule were 23/24 (96%), 27/30 (90%), and 10/15 (67%), respectively. CONCLUSIONS: In baseline CT screening for lung cancer, overdiagnosis of the disease is uncommon, with cases presenting as a nonsolid nodule a possible exception to this.

False Positive Reactions↗