Session Item

Inter-fraction motion of tumors and dose-limiting OARs can be visualized using MR guidance. Whereas standard treatments deliver the same dose in each fraction, adaptive fractionation (AF) is an approach to exploit inter-fraction motion by increasing the dose on days when the distance of tumor and OAR is large and decreasing the dose on unfavorable days. We develop an algorithm for AF and evaluate the concept for former patients treated at the MR-Linac for abdominal tumors in 5 fractions.

Given daily MR scans and adaptive treatment plans, inter-fractional changes in fraction t are quantified by sparing factors δ_t, which are defined as the ratio of dose delivered to the OAR (D_1cc) and the tumor (D_95%). The key problem of AF is to decide on the dose to deliver in fraction t, given today's δ_t and the dose delivered in previous fractions, but with unknown future δs. This problem can be formulated as a Markov decision problem and solved with a dynamic programming algorithm. The algorithm assumes a normal distribution over δ with mean and variance estimated from previously observed patient-specific δs and a population based prior for the variance. Based on the distribution, the algorithm computes optimal doses that maximize the expected tumor BED₁₀ while staying below the maximum BED₃ of 90 Gy in the OAR (30 Gy in 5 fractions). To evaluate the algorithm, 10 MR-Linac patients were exported in whom tumor dose was compromised due to proximity of bowel, stomach, duodenum or heart. Moreover, 1000 synthetic patients with similar δ distribution have been sampled. AF was compared to a standard plan which delivers 6 Gy to the OAR in each fraction to reach exactly the 90 Gy BED₃ constraint.

In 7 of the 10 patients, AF was equal or increased the tumor BED₁₀, on average by 0.72 Gy (1.2%). Figure 1 shows the δ's and the respective doses based on AF for all patients. The biggest increase of 13.1 Gy (18.4%) was achieved for patient 2 with δ=[0.88,0.8,0.58,0.86,0.77] where AF delivered tumor doses of [4.4,8.7,17,0.9,2.1] and thereby exploited the low δ in fraction 3. Figure 2a) illustrates the optimal policy for fraction 3, i.e. optimal dose to deliver as a function of δ. For the 1000 generated patients, 84.1% of plans were equal or superior to the standard plan with a mean improvement of 1.01 Gy (1.5%) BED₁₀. A distribution of the differences is shown in Figure 2b). With the most extreme plans having a benefit of up to 12 Gy BED₁₀. 

On average, AF provided only a small increase in tumor BED. However, AF may yield substantial benefits for individual patients with large variations in the geometry.