Generating Evidence for Clinical Benefit of PET/CT in Diagnosing Cancer Patients
ABSTRACT For diagnostic methods such as PET/CT, not only diagnostic accuracy but also clinical benefit must be demonstrated. However, there is a lack of consensus about how to approach this task. Here we consider 6 clinical scenarios to review some basic approaches to demonstrating the clinical benefit of PET/CT in cancer patients: replacement of an invasive procedure, improved accuracy of initial diagnosis, improved accuracy of staging for curative versus palliative treatment, improved accuracy of staging for radiation versus chemotherapy, response evaluation, and acceleration of clinical decisions. We also develop some guidelines for the evaluation of clinical benefit. First, it should be clarified whether there is a direct benefit of the use of PET/CT or an indirect benefit because of improved diagnostic accuracy. If there is an indirect benefit, then decision modeling should be used initially to assess the benefit expected from the use of PET/CT. Only if decision modeling does not allow definitive conclusions should randomized controlled trials be planned.
SourceAvailable from: Alexander Walter Sauter[Show abstract] [Hide abstract]
ABSTRACT: Non-small-cell lung cancer is the most common type of lung cancer and one of the leading causes of cancer-related death worldwide. For this reason, advances in diagnosis and treatment are urgently needed. With the introduction of new, highly innovative hybrid imaging technologies such as PET/CT, staging and therapy response monitoring in lung cancer patients have substantially evolved. In this review, we discuss the role of FDG PET/CT in the management of lung cancer patients and the importance of new emerging imaging technologies and radiotracer developments on the path to personalized medicine.European journal of nuclear medicine and molecular imaging 01/2015; 42(4). DOI:10.1007/s00259-014-2974-5 · 5.22 Impact Factor
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ABSTRACT: When the efficacy of a new medical drug is compared against that of an established competitor in a randomized controlled trial, the difference in patient-relevant outcomes, such as mortality, is usually measured directly. In diagnostic research, however, the impact of diagnostic procedures is of an indirect nature as test results do influence downstream clinical decisions, but test performance (as characterized by sensitivity, specificity, and the predictive values of a procedure) is, at best, only a surrogate endpoint for patient outcome and does not necessarily translate into it. Not many randomized controlled trials have been conducted so far in diagnostic research, and, hence, we need alternative approaches to close the gap between test characteristics and patient outcomes. Several informal approaches have been suggested in order to close this gap, and decision modeling has been advocated as a means of obtaining formal approaches. Recently, the expected benefit has been proposed as a quantity that allows a simple formal approach, and we take up this suggestion in this paper. We regard the expected benefit as an estimation problem and consider two approaches to statistical inference. Moreover, using data from a previously published study, we illustrate the possible insights to be gained from the application of formal inference techniques to determine the expected benefit. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.Biometrical Journal 03/2015; 57(3). DOI:10.1002/bimj.201400020 · 1.24 Impact Factor
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ABSTRACT: Not only therapeutic procedures, but also diagnostic procedures, have to demonstrate their patient-relevant benefits if they are to be reimbursed by public health insurance. Randomized trials comparing two diagnostic procedures allow us to assess these benefits directly if appropriate outcomes are used. However, owing to the widespread lack of such studies, it is now necessary to use the "linked evidence" approach as well, trying to predict the patient-relevant benefits from the results of comparative accuracy studies. Such a prediction is based on explicitly specifying our expectations with regard to the consequences of a change in diagnosis at the level of a single patient. We discuss the basic properties of these two approaches, which are relevant to the understanding of their possible role in the benefit assessment of diagnostic procedures. We try to predict the future roles of the two approaches and outline some of the issues on which a consensus is required to allow their successful use in benefit assessment. Furthermore, we indicate some of the developments related to the paradigm of individualized care that may influence the use of benefit assessments for diagnostic studies in the future.Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 01/2015; DOI:10.1007/s00103-014-2111-4 · 1.01 Impact Factor