Incremental Benefit of Maximum-Intensity-Projection Images on Observer Detection of Small Pulmonary Nodules Revealed by Multidetector CT

Gruden, James F.; Ouanounou, Serge; Tigges, Stefan; Norris, Shannon D.; Klausner, Todd S.

doi:10.2214/ajr.179.1.1790149

Cited by 144 publications

(76 citation statements)

References 23 publications

(51 reference statements)

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“…However, this phenomenon was not observed in our study since only one and three cancers were missed at consensus double reading and single reading, respectively. This can be explained by the fact that multidetector CT performed at low-dose level provides high spatial resolution and attenuation and the use of maximum intensity projections reconstruction algorithms with cine mode further facilitates the identifi cation of abnormalities ( 22 ). Second, our nodule management strategy is an objective, software-driven approach ( 23,25 ), in which the recall is determined only by the volume and the VDT of the nodules detected without further subjective interpretation.…”

Section: Thoracic Imaging: Consensus Double Reading In Ct Lung Cancermentioning

confidence: 99%

“…This observation is in line with previous studies. Gruden et al ( 22 ) explored the interreader variability in a lung cancer CT screening project and showed that the difference between readers could have occurred in lesion detection, characterization of a lesion as a nodule or nonnodule, or lesion measurement. The interobserver agreement was moderate to substantial, and potential for considerable improvement existed.…”

Section: Thoracic Imaging: Consensus Double Reading In Ct Lung Cancermentioning

confidence: 99%

See 1 more Smart Citation

No Benefit for Consensus Double Reading at Baseline Screening for Lung Cancer with the Use of Semiautomated Volumetry Software

Wang¹,

Klaveren²,

Bock³

et al. 2012

Radiology

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Purpose:To retrospectively evaluate the performance of consensus double reading compared with single reading at baseline screening of a lung cancer computed tomography (CT) screening trial. Materials and Methods:The study was approved by the Dutch Minister of Health and ethical committees. Written informed consent was obtained from all participants. The benefi t of consensus double reading was expressed by the percentage change in cancer detection rate, recall rate, number of additional nodules detected, and change in sensitivity and specifi city in 7557 participants. The reference standard was a retrospective analysis of the serial CT scans performed in participants diagnosed with lung cancer during a 2-year period after baseline. Semiautomated volumetric software was used for nodule evaluation. McNemar tests were performed to test statistical signifi cance. In addition, sensitivity, specifi city, positive predictive value (PPV), and negative predictive value (NPV) were calculated and 95% confi dence intervals (CIs) constructed. Results:Seventy-four cases of lung cancer were qualifi ed as detectable at baseline. Compared with single reading, consensus double reading did not increase the cancer detection rate (2.7%; 95% CI: 2 1.0%, 6.4%; P = .50) or change the recall rate (20.6% vs 20.8%, P = . Conclusion:There is no statistically signifi cant benefi t for consensus double reading at baseline screening for lung cancer with the use of a nodule management strategy based solely on semiautomated volumetry.

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Section: Thoracic Imaging: Consensus Double Reading In Ct Lung Cancermentioning

confidence: 99%

Section: Thoracic Imaging: Consensus Double Reading In Ct Lung Cancermentioning

confidence: 99%

No Benefit for Consensus Double Reading at Baseline Screening for Lung Cancer with the Use of Semiautomated Volumetry Software

Wang¹,

Klaveren²,

Bock³

et al. 2012

Radiology

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show abstract

“…Investigators developing automated lung nodule detection methods, for example, require the opinion of an experienced radiologist regarding the location of nodules within the CT scans. More appropriately, a panel of experienced thoracic radiologists would be used to establish the "truth" for the nodule-detection task, since radiologists can miss lung nodules (8,9) and the variability among radiologists in the detection of lung nodules is known to be substantial (10-13) (some investigators even incorporate the output from the CAD system itself into the "truth" assessment (14,15)). Furthermore, the notion of a single "truth" in any particular instance is elusive (16), with differences of opinion in the interpretation of images among even experienced thoracic radiologists a reality (13).…”

Section: Introductionmentioning

confidence: 99%

The Lung Image Database Consortium (LIDC): An Evaluation of Radiologist Variability in the Identification of Lung Nodules on CT Scans

et al. 2007

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Rationale and Objectives-The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on CT scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured.Materials and Methods-Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule ≥ 3mm (diameter)," "nodule < 3mm," or "non-nodule ≥ 3mm." During the subsequent "unblinded read" phase, the blinded read results of all radiologists were revealed to each of the four radiologists, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus.Results-After the initial blinded read phase, a total of 71 lesions received "nodule ≥ 3mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. Following the unblinded reads, a total of 59 lesions were marked as "nodule ≥ 3 mm" by at least one radiologist. 27 (45.8%) of these lesions received such marks from all four radiologists, 3 (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist.

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“…8 A study comparing the use of MIP with volume rendering (VR) image processing techniques found VR to be superior to MIP in detecting small pulmonary nodules. 9 These image processing techniques have been further developed using machine learning methods to facilitate computer-aided detection of pulmonary nodules.…”

Section: Improved Data Analysismentioning

confidence: 99%

Picture the future: emerging imaging modalities

Lee

Gleeson

2014

Clinical Medicine

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The past century has witnessed accelerated development in imaging modalities. Better anatomical visualisation and improved data analysis have improved survival rates. Through emerging functional, molecular and structural imaging modalities, better anatomical visualisation has been extended to cellular and molecular detail, improving diagnosis and management of diseases. This article reviews the advances made in emerging imaging modalities as well as their potential applications in targeted therapy.

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Incremental Benefit of Maximum-Intensity-Projection Images on Observer Detection of Small Pulmonary Nodules Revealed by Multidetector CT

Abstract: MIP processing reduces the number of overlooked small nodules, particularly in the central lung. Observer nodule detection remains imperfect even when lesions are clearly depicted on images.

Cited by 144 publications

References 23 publications

No Benefit for Consensus Double Reading at Baseline Screening for Lung Cancer with the Use of Semiautomated Volumetry Software

No Benefit for Consensus Double Reading at Baseline Screening for Lung Cancer with the Use of Semiautomated Volumetry Software

The Lung Image Database Consortium (LIDC): An Evaluation of Radiologist Variability in the Identification of Lung Nodules on CT Scans

Picture the future: emerging imaging modalities

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