Flat-Panel Detector Computed Tomography Imaging

Otani, Hideji; Nitta, Norihisa; Ikeda, Mitsuru; Nagatani, Yukihiro; Tanaka, Toyohiko; Kitahara, Hitoshi; Murakami, Yoko; Takahashi, Masashi; Murata, Kiyoshi

doi:10.1097/rti.0b013e31820321e2

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The lungs were divided into twelve compartments for the per-region analysis, such that each lung included three areas of equal height (upper, middle, and lower) and each area was split into equally wide lateral and medial zones (10, 11, 12). Reader performance was calculated using a receiver operating characteristic analysis according to each interpretation session.…”

Section: Methodsmentioning

confidence: 99%

Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times

et al. 2015

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ObjectiveTo evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT).Materials and MethodsWe included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined.ResultsAmong 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, ≤ 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed.ConclusionMost malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

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Section: Methodsmentioning

confidence: 99%

Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times

et al. 2015

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“…The unit of analysis for the ROC AUC analysis was the patient in 23 (45%) studies, an organ in 5, an organ segment in 5, a lesion in 11 (22%), other in 2, and unclear or unrecorded in 6 (12%); one study [34] examined both organ and lesion so there were 52 extractions for this item. Analysis was based on multiple images in 33 (65%) studies, a single image in 16 (31%), multiple modalities in a single study [40] , and unclear in a single study [57] ; no study used videos.…”

Section: Resultsmentioning

confidence: 99%

Multi-Reader Multi-Case Studies Using the Area under the Receiver Operator Characteristic Curve as a Measure of Diagnostic Accuracy: Systematic Review with a Focus on Quality of Data Reporting

et al. 2014

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IntroductionWe examined the design, analysis and reporting in multi-reader multi-case (MRMC) research studies using the area under the receiver-operating curve (ROC AUC) as a measure of diagnostic performance.MethodsWe performed a systematic literature review from 2005 to 2013 inclusive to identify a minimum 50 studies. Articles of diagnostic test accuracy in humans were identified via their citation of key methodological articles dealing with MRMC ROC AUC. Two researchers in consensus then extracted information from primary articles relating to study characteristics and design, methods for reporting study outcomes, model fitting, model assumptions, presentation of results, and interpretation of findings. Results were summarized and presented with a descriptive analysis.ResultsSixty-four full papers were retrieved from 475 identified citations and ultimately 49 articles describing 51 studies were reviewed and extracted. Radiological imaging was the index test in all. Most studies focused on lesion detection vs. characterization and used less than 10 readers. Only 6 (12%) studies trained readers in advance to use the confidence scale used to build the ROC curve. Overall, description of confidence scores, the ROC curve and its analysis was often incomplete. For example, 21 (41%) studies presented no ROC curve and only 3 (6%) described the distribution of confidence scores. Of 30 studies presenting curves, only 4 (13%) presented the data points underlying the curve, thereby allowing assessment of extrapolation. The mean change in AUC was 0.05 (−0.05 to 0.28). Non-significant change in AUC was attributed to underpowering rather than the diagnostic test failing to improve diagnostic accuracy.ConclusionsData reporting in MRMC studies using ROC AUC as an outcome measure is frequently incomplete, hampering understanding of methods and the reliability of results and study conclusions. Authors using this analysis should be encouraged to provide a full description of their methods and results.

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Diagnostic du nodule pulmonaire solitaire

Revel

2014

Feuillets de Radiologie

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Flat-Panel Detector Computed Tomography Imaging

Abstract: The detectability of pulmonary nodules was better on images of FPD-CT than on CXRs.

Cited by 4 publications

References 24 publications

Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times

Digital Tomosynthesis for Evaluating Metastatic Lung Nodules: Nodule Visibility, Learning Curves, and Reading Times

Multi-Reader Multi-Case Studies Using the Area under the Receiver Operator Characteristic Curve as a Measure of Diagnostic Accuracy: Systematic Review with a Focus on Quality of Data Reporting

Diagnostic du nodule pulmonaire solitaire

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