The Diagnostic Efficacy of Computer-Aided Detection (CAD) in Full-Field Digital Mammography (FFDM)- A Prospective Study

Breast Cancer Clinical Trial

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT00173303
• Other study ID #: 9461700528
• Status: Not yet recruiting
• Phase: N/A
• First received: September 12, 2005
• Last updated: September 12, 2005
• Start date: January 2006
• Est. completion date: January 2008

Study information

• Verified date: June 2005
• Source: National Taiwan University Hospital
• Contact: Jane Wang, MD
• Phone: 886-2-23123456
• Email: hstjen[@]yahoo.com.tw
• Is FDA regulated: No
• Health authority: Taiwan: Department of Health
• Study type: Observational

Clinical Trial Summary

The purpose of this study is to evalute whether CAD (computer-aided detection) in FFDM (full-field digital mammography) can facilitate the detection rate of breast cancer on mammography compared with FFDM without CAD.

Description:

Mammography is currently the only documented effective imaging tool for breast cancer screening. However, the sensitivity of mammography may be reduced in dense breasts, and sometimes it is difficult to even perceive a very subtle cancer which presents as a small stellate lesion, or very faint microcalcifications, missed diagnosed thus occurs. Herein, some researchers in Western countries developed computer-aided detection (CAD) system to help radiologists detect subtle, easily overlooked findings to facilitate early breast cancer detection, and most of the research regarding CAD was used in screen-film mammography (SFM) system. Ikeda, et al, worked on the retrospective CAD usage of those negative mammograms which later developed breast cancers. CAD could correctly mark 40% of the areas on these mammograms reported negative previously that later developed evident cancers. However, 80% of these are only nonspecific findings, and do not warrant recall for additional workup even at retrospective unblinded review by well-trained mammographers. The other research concluded that CAD could improve early cancer detection rate of mammography, with the sensitivity of 92% in detection of breast cancer size smaller than 5mm, 94% for cancer size 11-15mm. CAD can detect more microcalcifications than masses (sensitivity for microcalcifications 98%, masses 84%, mass with microcalcifications 92%). CAD could mark an average of 1.3 false positive marks per mammographic exam.

Full-field digital mammography (FFDM) is a new approved technology for breast cancer detection after SFM era since 2000. The diagnostic accuracy of FFDM versus SFM is still under clinical trials, and it is believed the sensitivity and accuracy of FFDM for screening population is relatively equivalent to SFM. However, there are very few reports regarding the CAD application in FFDM, since FFDM can offer the post-acquisition processing on high-resolution review workstation for interpretation. Nevertheless, the spatial resolution of soft-copy reading on monitors for FFDM is slightly inferior to but the contrast resolution is slightly superior to that of conventional SFM. Herein, the diagnostic efficacy and role of CAD in FFDM are still unclear. Therefore, the goal of our study is to explore the sensitivity, false-negative (FN) and false-positive (FP) rates of combination usage of CAD in FFDM system, in comparison with the sensitivity, FN and FP rates of interpretation based on FFDM without CAD combination. We are also about to evaluate the efficacy, additional time spent in adjunct application CAD in FFDM interpretation, in order to assess the feasibility of CAD in FFDM.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 3000
• Est. completion date: January 2008
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 20 Years to 90 Years

Eligibility

Inclusion Criteria:

• women for mammographic screening

• women with breast disease

Exclusion Criteria:

• pregnant women

Study Design

Observational Model: Defined Population, Primary Purpose: Screening, Time Perspective: Cross-Sectional

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Locations

Country	Name	City	State
Taiwan	National Taiwan University Hospital	Taipei

Sponsors (1)

Lead Sponsor	Collaborator
National Taiwan University Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (1)

Brem RF, Hoffmeister JW, Zisman G, DeSimio MP, Rogers SK. A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size. AJR Am J Roentgenol. 2005 Mar;184(3):893-6. — View Citation