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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

Report from CAD measurement system showing diameter and volume measurements and percentage changes from baseline. From these changes disease stability or progression is determined.
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f4-cin-04-25: Report from CAD measurement system showing diameter and volume measurements and percentage changes from baseline. From these changes disease stability or progression is determined.

Mentions: The data model described in Section 2.1 supports queries to retrieve serial nodule volumes from a given subject in a clinical trial. Given a subject ID, the Patient table can be queried to retrieve all CT image series associated with the subject, and then all nodule ROIs associated with the series. From each ROI, the system can compute volume and generate a report of the change in nodule volume between exams. The architecture supports web-based reporting of these results for a given patient. The report includes volumes and diameters, and applies World Health Organization (WHO) and Response Evaluation Criteria in Solid Tumors (RECIST) criteria (see Figure 4). The architecture also supports 3D visualization of nodule changes (Figure 3).


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)

Report from CAD measurement system showing diameter and volume measurements and percentage changes from baseline. From these changes disease stability or progression is determined.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4-cin-04-25: Report from CAD measurement system showing diameter and volume measurements and percentage changes from baseline. From these changes disease stability or progression is determined.
Mentions: The data model described in Section 2.1 supports queries to retrieve serial nodule volumes from a given subject in a clinical trial. Given a subject ID, the Patient table can be queried to retrieve all CT image series associated with the subject, and then all nodule ROIs associated with the series. From each ROI, the system can compute volume and generate a report of the change in nodule volume between exams. The architecture supports web-based reporting of these results for a given patient. The report includes volumes and diameters, and applies World Health Organization (WHO) and Response Evaluation Criteria in Solid Tumors (RECIST) criteria (see Figure 4). The architecture also supports 3D visualization of nodule changes (Figure 3).

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