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Lung Imaging Database Resource for Imaging Research
Mike McNitt-Gray, Ph.D.
University of California, Los Angeles
Grant Number: U01CA091103
The aim of this research is to create a database resource for images that will
be used in analyses related to the detection and characterization of lung
cancer using spiral CT. There has been significant interest in the last few
years in using spiral CT lung scanning for lung cancer screening of patients at
high risk. Early detection and intervention may significantly reduce the
mortality rates of lung cancer and improve patient prognoses. In addition,
there is significant interest in the characterization of solitary or small
multiple nodules detected using lung cancer screening and conventional thoracic
CT exams. This is because the presence of nodules within the lungs is not a
reliable indicator of cancer. In fact, 50-80 percent of nodules detected by
current methods are benign; this percentage may even climb as smaller nodules
are detected with very sensitive screening techniques under consideration.
Therefore, detection of suspicious objects in the lung parenchyma, while a very
necessary step, is not sufficient for patient management. Additional imaging or
processing of the CT images may provide information that is useful in
establishing the diagnosis of the individual patient and determining the next
step in patient management. However, research in this area has been limited by
the difficulties in collecting cases on which image processing algorithms may
be robustly developed and tested. This is because it is difficult to establish
diagnostic truth for such key elements as lesion location and lesion diagnosis.
The establishment of a lung imaging database creates a resource for the
development and evaluation of methods for detecting and characterizing lung
cancer. When made available to researchers all over the world, this resource
would significantly reduce development time because it would allow imaging
researchers to focus on the their areas of expertise without having to focus on
case collection, establishing diagnostic truth and all of the other
infrastructure issues that detract from development. This database would also
allow direct and objective comparisons of techniques because common metrics
would be applied to identical cases. This will allow the image processing field
to move forward and to move from design to clinical implementation much faster.
The specific aims to accomplish this are: SA-1 To develop the necessary
consensus and standards for an image database resource related to the
detection, characterization and evaluation of lung cancer using spiral CT
imaging. SA-2 To construct, populate and test the database of spiral CT lung
image data and ancillary data including the information necessary about
diagnostic truth for each case. SA-3 To provide a means for documentation and
distribution of this database to researchers through the internet.
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