An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials
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 describ...
Main Authors: | , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2007-01-01
|
Series: | Cancer Informatics |
Subjects: | |
Online Access: | http://la-press.com/article.php?article_id=239 |
id |
doaj-e663f566d37545ad9f9b97960eaa3fe2 |
---|---|
record_format |
Article |
spelling |
doaj-e663f566d37545ad9f9b97960eaa3fe22020-11-25T03:43:17ZengSAGE PublishingCancer Informatics1176-93512007-01-014Imaging2531An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical TrialsDenise R. AberleLien TranJonathan G. GoldinIva PetkovskaMichael F. McNitt-GraySumit ShahMatthew S. BrownRichard PaisPeiyuan QingComputer 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 fi rst 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. http://la-press.com/article.php?article_id=239“Computer-Aided Diagnosis”“Lung Nodules”“CT” |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Denise R. Aberle Lien Tran Jonathan G. Goldin Iva Petkovska Michael F. McNitt-Gray Sumit Shah Matthew S. Brown Richard Pais Peiyuan Qing |
spellingShingle |
Denise R. Aberle Lien Tran Jonathan G. Goldin Iva Petkovska Michael F. McNitt-Gray Sumit Shah Matthew S. Brown Richard Pais Peiyuan Qing An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials Cancer Informatics “Computer-Aided Diagnosis” “Lung Nodules” “CT” |
author_facet |
Denise R. Aberle Lien Tran Jonathan G. Goldin Iva Petkovska Michael F. McNitt-Gray Sumit Shah Matthew S. Brown Richard Pais Peiyuan Qing |
author_sort |
Denise R. Aberle |
title |
An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials |
title_short |
An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials |
title_full |
An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials |
title_fullStr |
An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials |
title_full_unstemmed |
An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials |
title_sort |
architecture for computer-aided detection and radiologic measurement of lung nodules in clinical trials |
publisher |
SAGE Publishing |
series |
Cancer Informatics |
issn |
1176-9351 |
publishDate |
2007-01-01 |
description |
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 fi rst 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. |
topic |
“Computer-Aided Diagnosis” “Lung Nodules” “CT” |
url |
http://la-press.com/article.php?article_id=239 |
work_keys_str_mv |
AT deniseraberle anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT lientran anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT jonathanggoldin anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT ivapetkovska anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT michaelfmcnittgray anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT sumitshah anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT matthewsbrown anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT richardpais anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT peiyuanqing anarchitectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT deniseraberle architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT lientran architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT jonathanggoldin architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT ivapetkovska architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT michaelfmcnittgray architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT sumitshah architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT matthewsbrown architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT richardpais architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials AT peiyuanqing architectureforcomputeraideddetectionandradiologicmeasurementoflungnodulesinclinicaltrials |
_version_ |
1724520832852557824 |