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Trial Title:
The Diagnostic Efficacy of Computer-Aided Detection (CAD) in Full-Field Digital Mammography (FFDM)- A Prospective Study
NCT ID:
NCT00173303
Condition:
Breast Cancer
Breast Neoplasms
Conditions: Official terms:
Breast Neoplasms
Conditions: Keywords:
Breast neoplasms
Breast radiography
Cancer screening
Study type:
Observational
Overall status:
Unknown status
Study design:
Time perspective:
Other
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.
Detailed 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.
Criteria for eligibility:
Criteria:
Inclusion Criteria:
- women for mammographic screening
- women with breast disease
Exclusion Criteria:
- pregnant women
Gender:
Female
Minimum age:
20 Years
Maximum age:
90 Years
Healthy volunteers:
Accepts Healthy Volunteers
Locations:
Facility:
Name:
National Taiwan University Hospital
Address:
City:
Taipei
Country:
Taiwan
Contact:
Last name:
Jane Wang, MD
Phone:
886-2-23123456
Phone ext:
5565
Email:
hstjen@yahoo.com.tw
Investigator:
Last name:
Jane Wang, MD
Email:
Principal Investigator
Start date:
January 2006
Completion date:
January 2008
Lead sponsor:
Agency:
National Taiwan University Hospital
Agency class:
Other
Source:
National Taiwan University Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT00173303
