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An architecture for computer-aided detection and radiologic measurement of lung nodules in clinical trials.

Brown MS, Pais R, Qing P, Shah S, McNitt-Gray MF, Goldin JG, Petkovska I, Tran L, Aberle DR - Cancer Inform (2007)

Bottom Line: In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images.Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools.The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. mbrown@mednet.ucla.edu

ABSTRACT
Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation. In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools. We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.

No MeSH data available.


Related in: MedlinePlus

3D rendering of a pulmonary nodule and blood vessels adjacent to the pleural surface.
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Related In: Results  -  Collection

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f3-cin-04-25: 3D rendering of a pulmonary nodule and blood vessels adjacent to the pleural surface.

Mentions: The pattern classifier contains a model of the expected appearance of nodules and vessels in terms of these features. The details are provided in (4,12), but the model reflects that vessels are long, thin tubular structures in 3D and thus have a low sphericity value, whereas nodules have a higher sphericity value. This is illustrated in Figure 3 showing a 3D rendering of a nodule and adjacent vessels. The system uses fuzzy logic to compute a confidence score indicating whether a given ROI has features consistent with a nodule. ROIs with high confidence for a nodule are presented to the user as shown in Figure 2c. The output of the CAD system is written to a row in the Segmentation_Results table. The row is associated with rows in the Image_Series and Segmentation_ Model tables to indicate the image data set and classification model from which the CAD results were derived.


An architecture for computer-aided detection and radiologic measurement of lung nodules in clinical trials.

Brown MS, Pais R, Qing P, Shah S, McNitt-Gray MF, Goldin JG, Petkovska I, Tran L, Aberle DR - Cancer Inform (2007)

3D rendering of a pulmonary nodule and blood vessels adjacent to the pleural surface.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2666948&req=5

f3-cin-04-25: 3D rendering of a pulmonary nodule and blood vessels adjacent to the pleural surface.
Mentions: The pattern classifier contains a model of the expected appearance of nodules and vessels in terms of these features. The details are provided in (4,12), but the model reflects that vessels are long, thin tubular structures in 3D and thus have a low sphericity value, whereas nodules have a higher sphericity value. This is illustrated in Figure 3 showing a 3D rendering of a nodule and adjacent vessels. The system uses fuzzy logic to compute a confidence score indicating whether a given ROI has features consistent with a nodule. ROIs with high confidence for a nodule are presented to the user as shown in Figure 2c. The output of the CAD system is written to a row in the Segmentation_Results table. The row is associated with rows in the Image_Series and Segmentation_ Model tables to indicate the image data set and classification model from which the CAD results were derived.

Bottom Line: In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images.Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools.The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. mbrown@mednet.ucla.edu

ABSTRACT
Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation. In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools. We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.

No MeSH data available.


Related in: MedlinePlus