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Multidetector CT images of the state-of-the-art quality are increasingly produced in thoracic
imaging, and in the near future, the CT imaging is expected to substitute chest radiography
especially in outpatient clinic and may be ordered as a routine admission battery
examination.
In MEDLINE or other search engines, writing down several keywords regarding lung diseases
with correct MeSH (Medical Subject Headings) words would promptly bring you several
diseases among which you may choose correct diagnosis of pulmonary diseases in
consideration of most compatible patient symptoms and signs. Likewise, we authors would
like to publish a book that will guide readers to correct diagnosis, when they use correct MeSH
words or keywords for describing lung lesion patterns identifi ed on chest CT images. When
readers encounter similar patterns or distribution of lung abnormalities at CT, they may need
to enumerate many diseases as potential differential diagnoses. This book provides the imaging
algorithms based on patterns and distributions of lung lesions and the most relevant differential
diagnoses. Because all diseases could not be enlisted as potential diagnostic
possibilities, we tried to enumerate as many as common diseases that appear with similar patterns
and distribution. Thus, familiarity with correct glossary of terms of lung lesion description
is basic prerequisite for effective and helpful reading of this book and for making a correct
diagnosis seen on chest CT scans.
After enlisting the diseases showing such pattern and distribution, the text provides the key
points for differential diagnosis based on clinical and imaging features and tables that outline
the classic manifestations of the various diseases. For each disease, succinct description of
histopathology, clinical symptoms and signs, CT–pathology correlation, and patient prognosis
has been given. Thus, this book is expected to be a shepherd for imaging diagnosis of lung
diseases to radiology residents, fellows for thoracic imaging, chest physicians, and general
practitioners. Moreover, as many cases are illustrated with their corresponding pathology, lung
pathologists may also like to read this book. Like a cherishing substance, by keeping this
handy book nearby and by comparing CT images of your patients with illustrated cases shown
on this book, you may narrow differential diagnoses of your cases seen in patients of your
references.
In mobile web and smartphone era, such automatic diagnosis App devices are expected to
be developed such that taking a photograph of representative CT image of your patient CT
might bring you an easy and automatic diagnosis of the lung disease of your patient. This
approach may be feasible by pattern approach using cumulative image database. We wish that
this book would be a cornerstone for such App (application) in the near future.
Last but not least, we should thank Young Joo Moon for her enthusiastic editorial help and
Springer people, Dr. Ute Heilmann and Ms. Lauren Kim, for encouraging us to write this precious
book. |