Blinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography

Georgian-Smith, Dianne; Moore, Richard H.; Halpern, Elkan F.; Yeh, Edmund; Rafferty, Elizabeth A.; D’Alessandro, Helen Anne; Staffa, Mary; Hall, Deborah A.; McCarthy, Kathleen A.; Kopans, Daniel B.

doi:10.2214/ajr.07.2393

Cited by 49 publications

(47 citation statements)

References 20 publications

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“…Many studies have validated CAD techniques in screening populations and found a sensitivity ranging from 73 [2] to 96% [3]. Moreover, the impact of a CAD system on the performance of mammogram observers was evaluated in several studies [2,[4][5][6][7][8][9][10][11][12][13][14], showing that there is insufficient evidence to claim that CAD improves cancer detection rates but that it does increase recall rates in screening programs for breast cancer [15].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

Biggelaar

Kessels

Engelshoven

et al. 2009

Breast Cancer Res Treat

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The purpose of the study was to evaluate the impact of a computer-aided detection (CAD) system on the performance of mammogram readers in interpreting digital mammograms in a clinical population. Furthermore, the ability of a CAD system to detect breast cancer in digital mammography was studied in comparison to the performance of radiologists and technologists as mammogram readers. Digital mammograms of 1,048 consecutive patients were evaluated by a radiologist and three technologists. Abnormalities were recorded and an imaging conclusion was given as a BI-RADS score before and after CAD analysis. Pathology results during 12 months follow up were used as a reference standard for breast cancer. Fifty-one malignancies were found in 50 patients. Sensitivity and specificity were computed before and after CAD analysis and provided with 95% CIs. In order to assess the detection rate of malignancies by CAD and the observers, the pathological locations of these 51 breast cancers were matched with the locations of the CAD marks and the mammographic locations that were considered to be suspicious by the observers. For all observers, the sensitivity rates did not change after application of CAD. A mean sensitivity of 92% was found for all technologists and 84% for the radiologist. For two technologists, the specificity decreased (from 84 to 83% and from 77 to 75%). For the radiologist and one technologist, the application of CAD did not have any impact on the specificity rates (95 and 83%, respectively). CAD detected 78% of all malignancies. Five malignancies were indicated by CAD without being noticed as suspicious by the observers. In conclusion, the results show that systematic application of CAD in a clinical patient population failed to improve the overall sensitivity of mammogram interpretation by the readers and was associated with an increase in false-positive results. However, CAD marked five malignancies that were missed by the different readers.

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Section: Introductionmentioning

confidence: 99%

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

Biggelaar

Kessels

Engelshoven

et al. 2009

Breast Cancer Res Treat

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“…To improve performance of the single radiologist, double reading provides an approach. Several researches demonstrated that independent double interpretation of screening mammograms appears to be a useful tool to ensure the quality of early detection of breast lesions [7][8][9]. Although the double reading method can improve the detection rate of breast cancer, it is not a cost-effective method due to the scarcity of experienced radiologists by compare with the increasing large number of mammograms.…”

Section: Introductionmentioning

confidence: 99%

Similarity in Mammography CAD Using CBIR Approach: A Validation Study

Lan

Ren

Zhang

et al. 2012

2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics

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To provide assistance for radiologists in mammographic screening, many computer-aided detection and diagnosis systems (CAD) have been developed. However, there are a lot of problems which should be addressed in conventional mammographic CAD system, such as the relatively lower performance in detecting malignant masses, especially those subtle masses. The reasons which caused those errors may be the black-box type approach, which only cuing those suspicious masses but it is different to explain the reasoning of the CAD decision-making. Mammographic CAD using content-based image retrieval is another new type of CAD which can provide visual assistance instead of the type of black box method in conventional CAD for radiologists. Unlike those conventional CAD, in content-based image retrieval (CBIR) CAD, several most similar regions of interest (ROIs) are provided to radiologists as well as the decision index (DI) of one ROI which being a positive region. It has been proved that this visual aid tool could improve radiologists' performance. At present, there are two common types of CBIR CAD based on the calculation of similarity between testing ROI and reference ROI, one is the multi-feature based methods, and the other one is pixel-value-based template matching methods. The typical techniques used in these two types of CBIR CAD are multifeature-based K-nearest neighbor (KNN) and template matching based system using mutual information (MI). The objective of this paper is to evaluate the performance of those methods commonly used in CBIR and discuss the approaches to improve CAD performance.

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“…The CAD (Image Checker, version 8.0 R2 Technology) showed the potential to increase the cancer detection rate for both FFDM (full field digital mammography) and screen-film mammography in breast cancer screen performed with independent double reading. Georgian-Smith et al [11] compared the CAD with a blinded human second reader for detection of additional breast cancer not seen by a primary radiologist. The research aimed to compare the practice of a human second reader with a CAD reader for the reduction of the number of false-negative cases resulting from review by a primary radiologist.…”

Section: Introductionmentioning

confidence: 99%

Presenting a Simplified Assistant Tool for Breast Cancer Diagnosis in Mammography to Radiologists

Zhang

Doust

Kumar

2010

Lecture Notes in Computer Science

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Abstract. This paper proposes a method to simplify a computational model from logistic regression for clinical use without computer. The model was built using human interpreted featrues including some BI-RADS standardized features for diagnosing the malignant masses. It was compared with the diagnosis using only assessment categorization from BI-RADS. The research aims at assisting radiologists to diagnose the malignancy of breast cancer in a way without using automated computer aided diagnosis system.

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Blinded Comparison of Computer-Aided Detection with Human Second Reading in Screening Mammography

Cited by 49 publications

References 20 publications

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

Similarity in Mammography CAD Using CBIR Approach: A Validation Study

Presenting a Simplified Assistant Tool for Breast Cancer Diagnosis in Mammography to Radiologists

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