核医学与放射治疗

Predicting Isocenter Shift due to Prostate Motion and Selecting Patient Specific Posterior Margin for IGRT of Prostate

Abstract

Chee-Wai Cheng, Zhanrong Gao, Brandon Langley, Scott Merrick and James Wong

Purposes/Objective: Image-Guided Radiation Therapy has been shown to significantly decrease setup errors and correct for organ motions (by patient repositioning, referred to as shift here), thus allowing the use of a tight treatment margin. The objective of the present work is to show that our evidenced-based patient positioning technique (isocenter shift) can effectively reduce the overall setup error for the majority of prostate patients.

Methods and Materials: We reviewed and analyzed the pre-treatment CT scans of 87 prostate patients treated from 2005-2007. Each patient received 10-15 image-guided fractions in the first phase of the treatment course. By systematically analyzed the imaging data and comparing to the planning CT, the isocenter positioning in both the left-right and anterior-posterior directions in the second phase of the treatment course can be predicted, along with the selection of a patient specific posterior margin.

Results: For 90% of the patients, the isocenter correction can be predicted to within 95% confidence. 90% of the patients in the study have a posterior margin in the range 5-8 mm for the second phase of treatment. The outliers in the frequency distributions of the iso-shifts for both the left-right and anterior-posterior directions seems to indicate that more frequent image-guided sessions are required in order to improve the setup accuracy.

Conclusions: An adequate number of image-guided treatments provide a semi-pattern recognition approach for patient repositioning. This, together with the inclusion of a quasi-adaptive margin can accommodate the daily variance of the prostate positions and affords a 95% confidence limit for tumor coverage. Our evidence-based method can effectively reduce the systematic setup error which potentially could modify the cumulative dose distribution. The use of adaptive strategy as proposed in this work reduces the overall setup error.