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. 2015 Jan;26(1):69-73.
doi: 10.1016/j.jvir.2014.09.013. Epub 2014 Nov 4.

Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma

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Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma

F Edward Boas et al. J Vasc Interv Radiol. 2015 Jan.

Abstract

Purpose: To optimize surveillance schedules for the detection of recurrent hepatocellular carcinoma (HCC) after liver-directed therapy.

Materials and methods: New methods have emerged that allow quantitative analysis and optimization of surveillance schedules for diseases with substantial rates of recurrence such as HCC. These methods were applied to 1,766 consecutive chemoembolization, radioembolization, and radiofrequency ablation procedures performed on 910 patients between 2006 and 2011. Computed tomography or magnetic resonance imaging performed just before repeat therapy was set as the time of "recurrence," which included residual and locally recurrent tumor as well as new liver tumors. Time-to-recurrence distribution was estimated by Kaplan-Meier method. Average diagnostic delay (time between recurrence and detection) was calculated for each proposed surveillance schedule using the time-to-recurrence distribution. An optimized surveillance schedule could then be derived to minimize the average diagnostic delay.

Results: Recurrence is 6.5 times more likely in the first year after treatment than in the second. Therefore, screening should be much more frequent in the first year. For eight time points in the first 2 years of follow-up, the optimal schedule is 2, 4, 6, 8, 11, 14, 18, and 24 months. This schedule reduces diagnostic delay compared with published schedules and is cost-effective.

Conclusions: The calculated optimal surveillance schedules include shorter-interval follow-up when there is a higher probability of recurrence and longer-interval follow-up when there is a lower probability. Cost can be optimized for a specified acceptable diagnostic delay or diagnostic delay can be optimized within a specified acceptable cost.

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