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Reader Study of DeltaView™ Chest Radiograph Software

This study has been completed.
Riverain Medical Group, Miamisburg, OH
BioStat Solutions, Inc., Mt. Airy, MD
Information provided by (Responsible Party):
Matthew T. Freedman, MD, Georgetown University Identifier:
First received: December 14, 2010
Last updated: November 4, 2015
Last verified: November 2015
A new software product takes two chest radiographs, aligns them, and then subtracts one image from the other. The resulting image represents an image showing any differences between them. The study is to determine whether radiologists using this new software perform better with it than when they do not use it.

Lung Neoplasm

Study Type: Observational
Official Title: Reader Study to Demonstrate That Use of DeltaView™ is Superior to the Use Standard Prior and Current Antero/Posterior (AP/PA) X-ray Image Pair

Resource links provided by NLM:

Further study details as provided by Matthew T. Freedman, MD, Georgetown University:

Primary Outcome Measures:
  • Localized Receiver Operating Characteristic (LROC) Comparison [ Time Frame: 1 day ]
    The area under the LROC curve will be compared for the chest radiograph interpretations done without the new software and those done with the new software. Improvement will be demonstrated if the improvement with the new software is statistically significant at the p=<0.05. There were 422 cases in the total study. 20 of these were inserted as "noise" cases, not to be analyzed. Thus there were 402 cases to be analyzed. There were 120 cases with nodules and 282 without a nodule. LROC is a method for measuring the success or failure of a method where there is a tradeoff between the detection of lung nodules that are there (true positives) and the detection that the radiologist considers to be a nodule where no nodule is present (false positive). It yields a single number that done not have a unit of measurement.

Secondary Outcome Measures:
  • Sensitivity and Specificity [ Time Frame: 1 day ]
    Sensitivity and specificity will be measured. If the radiologists using the new software have higher sensitivity, statistically significant at the p=< 0.05, the use of the new software will be considered to have resulted in improvement. A decrease in specificity is expected.

  • False Positive Decisions of Radiologists [ Time Frame: 1 day ]
    This is a comparison of the radiologists working without and with the software. The false positive rate is the percentage of cases in which the radiologists identified a lesions/location suspected of being cancer at a location where cancer was not present. . A false positive represents a location selected on a chest image without cancer and, also, a mark on a chest image where cancer was present, but a different location, one without cancer, was marked.The radiologists could mark up to five locations on an image and had to provide a confidence rating for each. This analysis is of the single mark with the highest confidence level.

Enrollment: 15
Study Start Date: November 2010
Study Completion Date: December 2012
Primary Completion Date: December 2011 (Final data collection date for primary outcome measure)

Ages Eligible for Study:   35 Years to 100 Years   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Radiologists in community practice of radiology chest radiographs of individuals with or without a lung nodule

Inclusion Criteria:

For Radiologists: American Board of Radiology Certification and live within the Baltimore, MD-Washington, DC Metropolitan areas

For chest radiographs, evidence of the presence or absence of lung nodule confirmed by expert panel; adequate image quality

Exclusion Criteria:

Radiologists who assisted by providing cases for review

For chest radiographs: poor image quality

  Contacts and Locations
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Please refer to this study by its identifier: NCT01261507

United States, District of Columbia
ISIS Research Center, Georgetown University Medical Center
Washington, District of Columbia, United States, 20007
Sponsors and Collaborators
Georgetown University
Riverain Medical Group, Miamisburg, OH
BioStat Solutions, Inc., Mt. Airy, MD
Principal Investigator: Matthew T Freedman, MD, MBA Georgetown University
Principal Investigator: S.-C. Ben Lo, PhD Georgetown University
  More Information

Responsible Party: Matthew T. Freedman, MD, Associate Professor Oncology, Georgetown University Identifier: NCT01261507     History of Changes
Other Study ID Numbers: DHF-183-0531A
Study First Received: December 14, 2010
Results First Received: April 5, 2013
Last Updated: November 4, 2015

Keywords provided by Matthew T. Freedman, MD, Georgetown University:
lung neoplasm nodule image processing computer-assisted

Additional relevant MeSH terms:
Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Lung Diseases
Respiratory Tract Diseases processed this record on August 17, 2017