Using computer-aided detection in mammography as a decision support

• Published in: European radiology Vol. 20; no. 10; pp. 2323 - 2330
• Main Authors: Samulski, Maurice, Hupse, Rianne, Boetes, Carla, Mus, Roel D. M., den Heeten, Gerard J., Karssemeijer, Nico
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.10.2010; Springer Nature B.V
• Subjects: Algorithms; Breast; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; Computers; Decision making; Decision Support Techniques; Diagnostic Radiology; Early Detection of Cancer; False Positive Reactions; Humans; Image Processing, Computer-Assisted; Imaging; Internal Medicine; Interventional Radiology; Mammography; Mammography - methods; Medical Oncology - methods; Medical screening; Medicine; Medicine & Public Health; Neuroradiology; Observer Variation; Radiology; Radiology - methods; Sensitivity and Specificity; Software; Time Factors; Ultrasound; User-Computer Interface; Work stations; United States > US; Netherlands; Breast; Mammography; Early detection of cancer; Computer-assisted; Decision making; Radiographic image interpretation
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-010-1821-8

Objective: To evaluate an interactive computer-aided detection (CAD) system for reading mammograms to improve decision making. Methods: A dedicated mammographic workstation has been developed in which readers can probe image locations for the presence of CAD information. If present, CAD findings are displayed with the computed malignancy rating. A reader study was conducted in which four screening radiologists and five non-radiologists participated to study the effect of this system on detection performance. The participants read 120 cases of which 40 cases had a malignant mass that was missed at the original screening. The readers read each mammogram both with and without CAD in separate sessions. Each reader reported localized findings and assigned a malignancy score per finding. Mean sensitivity was computed in an interval of false-positive fractions less than 10%. Results: Mean sensitivity was 25.1% in the sessions without CAD and 34.8% in the CAD-assisted sessions. The increase in detection performance was significant ( p  = 0.012). Average reading time was 84.7 ± 61.5 s/case in the unaided sessions and was not significantly higher when interactive CAD was used (85.9 ± 57.8 s/case). Conclusion: Interactive use of CAD in mammography may be more effective than traditional CAD for improving mass detection without affecting reading time.