Program Information

Correlation Between Geometric Information and Dose Fractionation in Lung SBRT Treatment Plans

T Coroller*, J Lewis, R Mak, A Chen, F Hacker, D Kozono, E Mannarino, C Molodowitch, J Killoran, Brigham and Women's Hospital, Dana-Farber Cancer Center, Harvard Medical School, Boston, MA

SU-E-T-694 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: Previously, a database-driven decision support tool to assist clinical staff in assessing and improving lung SBRT treatment plans was developed. Using planning data collected by this software, we determined a simple metric that can be used for early identification of cases for which it will be particularly difficult to achieve desired dosimetric goals. Early detection of these cases will be of clinical value.

Methods: This retrospective IRB-approved study utilizes an existing database of treatment planning data for 121 lung SBRT patients. Patients who had multiple SBRT treatments or more than one lung tumor were excluded. In our department, SBRT patients receive treatment in either 3 fractions of 18Gy or 5 fractions of 10-12Gy. Five fractions are generally used for cases in which planning goals are difficult to achieve in terms of target coverage vs. dose to OAR. Using the specific fractionation as a surrogate for plan difficulty, we examined possible correlations between fractionation and parameters including target size, total prescribed dose, number of fields and distance to OAR.

Results: 84 patients received 3 fractions of treatment and 37 received 5 fractions. Patients with PTVs greater than 70 cm³ in size and ITVs greater than 40 cm³ were always treated with 5 fractions. These patients constituted approximately 20% of the 5 fraction patients. Tumors smaller than this size were sometimes (e.g. when situated close to an OAR) treated with 5 fractions. No correlation was observed between the fractionation and the number of fields.

Conclusion: With large volume targets we can predict a treatment of 5 fractions. However, this represents a minority of patients. More powerful predictions could be possible by combining target size information with other metrics such as distance to nearby OARs. The ability to predict fractionation early in planning would allow for more efficient scheduling of LINAC time.

Funding Support, Disclosures, and Conflict of Interest: Kaye Scholar Grant

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