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The first level of evaluation can be called
. The appropriate research question to ask in this phase, while the
technology is just beginning to diffuse into clinical practice or when a substantive advance in the technology occurs, is ?How
well does the new technology detect specific disease conditions?? The measures of effectiveness are sensitivity, specificity, positive
and negative predictive value (given a certain prevalence of disease in the study population, the likelihood that a positive or
negative test result means that the patient does or does not have the disease), and receiver-operating characteristic curve analysis.
The test has diagnostic efficacy if the test classifies the patient in the correct category. The correct category is called the diagnosis.
Not all diagnostic techniques aim to properly classify the patients, but rather to predict either the outcome, or the best therapy
to obtain the desired outcome (measurements of plasma cholesterol do not provide a diagnosis but a prognosis, staging is not
diagnostic, but predictive). In view of this, validation can be based on the following approaches:
Outcome studies measure effectiveness rather than efficacy. The analysis of those data is complicated by
the concatenation of increased detection (an increase in incidence) and the fact that early detection may not always predict
progression. For breast cancer and mammography it did take a long time to show the beneficial outcome.
A taxonomic exact diagnosis may not be predictive. Consider that in some diseases the median survival
time is n years: fifty percent die earlier, 50% later. The prognosis is not necessarily well defined by the diagnosis.
Predicting the taxonomy:
The most relevant aspects of this approach are 1) that at some point there has to be a defining test
2) that a ground truth is assumed to exist and be known. The major problem is verification bias.
The classic approach is to take 2 groups, equivalent in many aspects, classified either genetically or
prospectively by evolution, and see if in the absence of clear symptoms they can be differentiated.
Mainly in Image processing or acquisition.
Regions of Interest:
Information extraction. Fourier analysis. Parametric imaging. Reconstruction. Multi-slice CT
The equivalence design is not altogether valueless since the new procedure may globally decrease the cost
(expenses in material and personnel, pain and danger). Essentially however the cost due to an improvement cannot be
demonstrated to increase. What can be demonstrated is an improvement in reproducibility of the interpretation if the
method is automated or quantitative.
Michael L. Goris received his M.D. from the Katholieke Universiteit Leuven (Belgium) in 1967 and his Ph.D. in Medical Physics from UC Berkeley
in 1972. He was board certified in Nuclear Medicine in 1972. In 1973 he became assistant professor in Radiology (Nuclear Medicine) at Stanford
University School of Medicine. He became a full professor in 1984. He has published more than 125 papers in reputed journals. He is serving as
Chief Editor of the OJMI. He is the author of 4 books on Image Processing, Diagnostic Characteristics, Nuclear Cardiology and the Mathematical
basis of Nuclear Medicine procedures.
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