Department of Radiation Oncology,Peking University Cancer Hospital&
Institute,Key laboratory of Carcinogenesis and Translational Research,Ministry of Education/Beijing,Beijing 100142,China (Jiang F,Wu H,Zhang J,Zhang YB);
National Institute of Metrology,Beijing 100029,China (Wang K,Zhang H)
Objective To evaluate the dosimetric errors of organs-at-risk (OARs) induced by the optimal auto-segmentation using Mim Maestro based on dose calculation and measurement. Methods The Mim atlas library composed of 240 nasopharyngeal carcinoma, breast cancer, and rectal cancer patients that were retrospectively selected was used for the auto-segmentation of OARs on the CT images of corresponding regions in 76 patients. Relative to the manual contouring, one optimal case was selected from each site based on conformity index (CI), mean distance to conformity (MDC), relative volume difference (Dv%), DICE, sensitivity index (Se. Idx), and inclusion index (Inc. Idx). Treatment plans were made to satisfy the DVH constraints of OARs based on auto-contours, and then the dose errors to the actual organs were evaluated in terms of calculation and measurement. The paired t-test (normal distribution) or rank sum test (non-normal distribution). Results Significant differences were observed in the 76 patients between the manual and automated segmentation (P<0.05). For the optimal cases, the DICE index of various OARs ranged from 0.43 to 0.98,and 73%(16/22) of DICE values were higher than 0.70. The calculated dose errors to various OARs were (-1.15±15.94)%(95% CI:-8.21% to 5.92%)(mean dose) and (-6.53±21.13)%(95% CI:-15.90% to 2.84%)(maximum dose). The measured dose errors were (-2.43±24.52)%(95% CI:-13.30% to 8.44%)(mean dose) and (-3.38±20.87)%(95% CI:-12.63% to 5.87%)(maximum dose). Conclusion Without human interference, even the optimal auto-segmentation Results are not clinically acceptable for treatment planning.
Jiang Fan,Wu Hao,Zhang Jian et al. A dosimetric evaluation of treatment planning based on optimal auto-segmentation[J]. Chinese Journal of Radiation Oncology, 2017, 26(4): 423-428.
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