Does Surveillance Imaging After Treatment for Diffuse Large B-Cell Lymphoma Really Work?

The late Dr Jane Weeks, a pioneer in cancer-focused outcomes research, was fond of asking the question “Don’t you want to know if what we are doing really works?” In the report accompanying this editorial, Huntington et al present a rigorous cost-effectiveness analysis addressing such a question in hematologic oncology: Is posttherapy surveillance imaging worthwhile for diffuse large B-cell lymphoma (DLBCL)? Using a decision-analytic Markov model, they calculated quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs) for surveillance strategies versus routine clinical follow-up without imaging for asymptomatic patients with DLBCL in remission. Neither strategy they modeled— biannual computed tomography (CT) scans for 2 years or biannual positron emission tomography (PET)/CT scans for 2 years—was associated with a substantial increase in survival. The potential benefit of surveillance imaging was even less impressive after quality-of-life adjustments, and ICERs were $160,000 per QALY (well beyond current willingness-to-pay thresholds). Although the debate regarding imaging after treatment for aggressive lymphoma is not new, there have been no randomized controlled trials. Several observational studies have suggested that routine imaging can capture a subset of patients with asymptomatic relapsed disease, but none have definitively shown early detection to make a difference in terms of survival. For example, in an analysis of 108 patients with relapsed aggressive non-Hodgkin lymphoma, Liedtke et al found patients to be approximately 4 times more likely to have lower-risk disease when relapse was detected with routine imaging; however, despite an increase of 11% in 5-year median overall survival for relapses detected with routine imaging, the difference was not statistically significant (P .13). In addition, it is possible that differences in survival observed for patients diagnosed through imaging alone may actually reflect lead-time bias, such that they include patients with less aggressive disease who would have lived the same amount of time even if the disease were detected and treated later. The question is compounded when considering the use of surveillance PET/CT scans, which may be better than lone CT scans at capturing relapse but are also more likely to yield false positives. Moreover, waiting for imaging results can be extremely anxiety provoking for patients, and the practice is costly. Surveillance imaging also necessitates potentially harmful repeated radiation exposure and sometimes leads to unnecessary further imaging and/or biopsy. These downsides are justifiable only if relapses are both detected before clinical manifestations and lead to patients living longer because of earlier relapses being more responsive to treatment. Certainly, it is not clear that surveillance really works. Indeed, a recent observational study published in Journal of Clinical Oncology by Thompson et al suggested that surveillance imaging is not highly effective for detecting relapse in DLBCL. This team assessed the utility of postremission imaging using a large cohort of observational data (N 774) from two sites (one in the United States and one in France), finding that routine imaging detected asymptomatic relapse in only 1.6% of patients at one site and 1.8% at the other. There was also no difference in survival for patients whose relapse was detected at a regular visit (presumably by imaging) compared with those detected outside of planned follow-up. Although limited by its reliance on retrospective medical record review, their analysis is compelling and does not support the use of routine surveillance imaging for follow-up in DLBCL. Huntington et al add to the debate by assessing the potential benefit of routine surveillance imaging for DLBCL in the context of its economic impact. In their analysis, it is notable that they provide clear and detailed documentation regarding their underlying sources of data. Such transparency in detail is critical in cost-effectiveness analysis, where baseline assumptions about both the costs and effectiveness of an intervention are paramount. In outcomes research, we sometimes use the modified term “baggage in, baggage out” to reflect the fact that any study conclusions must be viewed in the context of the so-called baggage that accompanies the underlying data. Several of the assumptions of Huntington et al are worthy of mention. First, for effectiveness, the authors assumed that early detection leads to better outcomes. Although it has been shown that patients with lower-risk disease at relapse do better after second-line chemotherapy followed by autologous stem-cell transplantation, their premise that routine imaging identifies such patients better than clinical follow-up alone is based primarily on one retrospective study performed at a single center. Next, for costs, the authors relied primarily on the 2013 Medicare fee schedule. This is problematic because the researchers modeled 55-year-old patient cohorts who would most likely be covered by private insurers. Although private insurer costs are often pegged to Medicare, they are sometimes significantly higher. Importantly, by both potentially overestimating its effectiveness and underestimating its costs, were the above assumptions erroneous, they would likely make the authors’ conclusion that surveillance imaging is not cost effective even stronger. JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 33 NUMBER 13 MAY 1 2015