Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD false-positive locations on radiologists' behavior

Freedman, Matthew T.; Lo, Shih-Chung Benedict; Osicka, Teresa; Lure, Fleming; Xu, Xinwei; Lin, Jesse; Zhao, Hui; Ron, Zhang

doi:10.1117/12.467092

Cited by 16 publications

(10 citation statements)

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“…We could find only three reported studies that met these requirements. The RS-2000 system has been tested on a database of similar difficulty as the JSRT database (Freedman et al, 2002) (observers obtained an area A z score of 0.833 for the JSRT database Shiraishi et al (2000) and 0.835 for the database in Freedman et al (2002)). In that particular study RS-2000 detects 66% of the nodules with on average 5 false positives per image.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…This suggests that from an observerÕs point of view the databases are comparable. In that study it is reported that RS-2000 detects 66% of the nodules with on average 5 false positives per image (Freedman et al, 2002). Wei et al (2002) reported a sensitivity of 80% at 5.4 false positives per image for the JSRT database.…”

Section: Related Workmentioning

confidence: 97%

“…We could identify only a few studies that make use of the same image database that we use (or a database that might be comparable), and that do report a CAD performance by sensitivity at an average number of false positives per image. Freedman et al (2002) describe a study with RS-2000 using an image database for which observers obtained an area A z under the ROC curve (Swets, 1997) which is similar to what is obtained by the observers of the JSRT database: respectively, A z = 0.835 and A z = 0.833. This suggests that from an observerÕs point of view the databases are comparable.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database

Schilham

Ginneken

Loog

2006

Medical Image Analysis

143

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database

Schilham

Ginneken

Loog

2006

Medical Image Analysis

143

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“…Computer-aided detection of lung nodules has been shown to be an effective method for detecting lung nodules and lung cancers. [6][7][8][9][10][11][12][13]15 In one study by Kakeda et al, 17 a radiologist using a chest radiographs CAD device was able to find more lung nodules than without use of a CAD device. Similar findings have also been reported in patients with metastatic disease of the chest.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity and Specificity of a CAD Solution for Lung Nodule Detection on Chest Radiograph with CTA Correlation

Moore

Ripton-Snyder

et al. 2010

J Digit Imaging

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The objective of this research was to determine the sensitivity and specificity of a commercially available computer-aided detection (CAD) system for detection of lung nodule on posterior-anterior (PA) chest radiograph in a varied patient population who are referred to computed tomographic angiogram (CTA) of the chest as a reference standard. Patients who had a PA chest radiograph with concomitant CTA of the chest were included in this retrospective study. The PA chest radiograph was analyzed by a CAD device, and results were recorded. A qualitative assessment of the CAD results was performed using a 5-point Likert scale. The CTA was then reviewed to determine if there were correlative nodules. The presence of a correlative nodule between 0.5 cm and 1.5 cm was considered a positive result. The baseline sensitivity of the system was determined to be 0.707 (95% CI=0.52-0.86), with a specificity of 0.50 (95% CI=0.38-0.76). Positive predictive value was 0.30 (95% CI=0.24-0.49), with a negative predictive value of 0.858 (95% CI=0.82-0.95), and accuracy of 0.555 (95% CI=0.40-0.66). When excluding nodules that were qualitatively determined by a thoracic radiologist to be false positives, the specificity was 0.781 (95% CI=0.764-0.839), the positive predictive value was 0.564 (95% CI=0.491-0.654), the negative predictive value was 0.829 (95% CI=0.819-0.878), and the accuracy was 0.737 (95% CI=0.721-0.801). The use of CAD for lung nodule detection on chest radiograph, when used in conjunction with an experienced radiologist, has a very good sensitivity, specificity, and accuracy.

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“…Computer-aided diagnosis (CAD) for the detection of lung nodules on chest images has been developed (8,9) and has become available for clinical practice (10)(11)(12). However, CAD methods employed only posterior-anterior (PA) views of chest images, even if both PA and lateral views for the same patient were available.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis for Improved Detection of Lung Nodules by Use of Posterior-Anterior and Lateral Chest Radiographs

Shiraishi

Doi

2007

Academic Radiology

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Rationale and Objectives-We developed a computerized scheme for detection of lung nodules in the lateral views of chest radiographs, in order to improve the overall performance in combination with the computer-aided diagnostic (CAD) scheme for posterior-anterior (PA) views.Materials and Methods-We used 106 pairs of PA and lateral views of chest radiographs (122 lung nodules) for development of the CAD scheme. In the CAD scheme for lateral views, initial candidates of lung nodules were identified by use of a nodule enhancement filter based on the edge gradients. Thirty four image features extracted from the original and the nodule-enhanced images were employed for the rule-based scheme and for artificial neural networks (ANNs) for removal of some false-positive candidates. The computer performance was evaluated with a leave-one-case-out test method for ANNs. For PA views, we used the existing CAD scheme, which was trained with one half of 924 chest images and then tested with the remaining images.Results-When the CAD scheme was applied only to PA views, the sensitivity in the detection of lung nodules was 70.5%, with 4.9 false positives per image. Although the performance of the computerized scheme for lateral views was relatively low (60.7% sensitivity with 1.7 false positives per image), the overall sensitivity (86.9%) was improved (6.6 false positives per two views), because 20 (16.4%) of the 122 nodules were detected only on lateral views. Conclusions-The CAD scheme by use of lateral-view images has the potential to improve the overall performance for detection of lung nodules on chest radiographs when combined with a conventional CAD scheme for standard PA views.

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Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD false-positive locations on radiologists' behavior

Cited by 16 publications

References 0 publications

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database

Sensitivity and Specificity of a CAD Solution for Lung Nodule Detection on Chest Radiograph with CTA Correlation

Computer-Aided Diagnosis for Improved Detection of Lung Nodules by Use of Posterior-Anterior and Lateral Chest Radiographs

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