Pulmonary Nodules on Multi–Detector Row CT Scans: Performance Comparison of Radiologists and Computer-aided Detection

Rubin, Geoffrey D.; Lyo, John; Paik, David S.; Sherbondy, Anthony J.; Chow, Laurence C.; Leung, Ann N.; Mindelzun, R E; Schraedley-Desmond, Pamela; Zinck, Steven E.; Naidich, David P.; Napel, Sandy

doi:10.1148/radiol.2341040589

Cited by 245 publications

(149 citation statements)

References 42 publications

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“…11,12,[21][22][23][24][25][26][27] Caution should always be exercised when comparing sensitivities between nodule detection studies, as differences in the derivation and stringency of the reference standard (as indicated in Table 4) 28 and in the types of patients undergoing CT examinations (e.g. patients with multiple metastases vs lung screening studies) may profoundly affect sensitivity.…”

Section: Discussionmentioning

confidence: 99%

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

Nair

Gartland

Barton

et al. 2016

BJR

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Section: Discussionmentioning

confidence: 99%

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

Nair

Gartland

Barton

et al. 2016

BJR

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“…9 These image processing techniques have been further developed using machine learning methods to facilitate computer-aided detection of pulmonary nodules. 10,11 New diagnostic algorithms have been developed using improved data analysis techniques to improve disease diagnosis and management. By incorporating imaging features, such as size and solidity of the nodules, and clinical and social data such as smoking history, a comprehensive algorithm was developed to stratify patients in the diagnosis and management of solitary 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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“…28 Single object-based shape description has also been successfully employed in several medical CAD applications such as in CT colonography 29,30 and lung nodule detection. 31 A shape-based representation of the image content in the form of point sets, contours, curves, regions, or surfaces should be available for the computation of shape-based features. Such representations are not usually available in the data directly.…”

Section: Image Features/descriptorsmentioning

confidence: 99%

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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Pulmonary Nodules on Multi–Detector Row CT Scans: Performance Comparison of Radiologists and Computer-aided Detection

Abstract: With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.

Cited by 245 publications

References 42 publications

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

Picture the future: emerging imaging modalities

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

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