Imaging Science Lecture 5.docx

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Department
Medical Biophysics
Course
Medical Biophysics 3503G
Professor
James Lacefield
Semester
Winter

Description
3503 Imaging Science Lecture 5 January 23 th Recall Case 1: Low p, High U(TP) • We have a rare disease (low prevalence) • We assigned a high value to the utility of true positives which is meant that we were running the analysis using the point of view of people who would favour maintaining the old screening mammography guidelines • We compared two different imaging systems and got two different expected utilities. One was excellent detection accuracy and one was merely good detection accuracy. We had observed that if you go from the merely good to the excellent system, it would improve the maximum utility that you could get from screening mammography. The exact improvement was from a maximum utility of 3 (merely good) to a maximum utility of 4 (excellent); a factor of 4/3. If we take the point of view from the other side of the debate: point of view of people who are concerned about overdiagnosis. So we must make adjustments of the utilities; make the utility of true positive smaller and utility of false negatives less negative. Overdiagnosis • Overdiagnosis would reduce U(TP) o U(TP) must be weighted average (based on what we think the fractions of each section is) of:  Benefit to patients whose lives are saved (last at least 5 years past the date of diagnosis)  Benefit to patients whose lives are prolonged  Benefit to patients who do not respond to treatment  Harm to over-treated patients o Point of view that there are a lot more over-treated patients that would pull down the overall utility of true positives in the expected utility analysis.  Patient’s age may be important; the benefit to society to saving someone who is 40 is greater than saving someone who is 75, and there may be oncology factors that depend on age • Younger patients in saved subgroup will have very high U(TP) due to individual’s large gain of QALY • Proportion of patients not responding to treatment may depend on age • Evidence for Overdiagnosis o Graph tells the cases of new cancer per 10,000 women per year. Red curve is new cases of breast cancer discovered that were advanced cancers (metastatic), and green curve is new cases of premalignant and early stage cancers. o The early 1980’s correspond to the point at which universal screening mammography started to be pushed aggressively. o You would think that by detecting more cancer at an early stage, this would reduce the detection of cancers at an advanced stage.  What we see is that after screening mammography became widely used the number of premalignant and early staged cancers detected went up, but the rate of metastatic cancers didn’t change. A lot of the patients on this green curve would have never made it in to the red curve. The increase in green that was not balanced by a decrease in red indicates that overdiagnosis is not just theoretically possible but is more common than actual diagnosis. o If we assume that overdiagnosis is really common then, then we want a really dramatic reduction in U(TP) and a really dramatic change in utility of false negatives. Case 2: Low p, Low U(TP) • In comparison to case 1, we still have a rare disease but now we have low utility of true positive indicating potentially that we are concerned about overdiagnosis. • Excellent imaging system on the left and merely good on the right • This reduction in utility of true positive has reduced the maximum utility of excellent system from 4 to 1.1 and reduced maximum utility of merely good system from 3 to 0.25 o Makes sense; if utility of true positive is going down, then maximum utility will go down too • The
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