Suhong Yu1,2, Huijun Xu3, Yin Zhang4, Xin Zhang1, Michael A Dyer1, Ariel E Hirsch1, Minh Tam Truong1, Heming Zhen1. 1. Department of Radiation Oncology, Boston Medical Center, Boston University school of Medicine, Boston, MA, USA. 2. Department of Radiation Oncology, University of Massachusetts Medical School, Worcester, MA, USA. 3. Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA. 4. Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Abstract
PURPOSE: To develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT). METHODS: Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (req_PTV ) and the equivalent radius of volume that receives a set of prescription dose (req_Vi , where Vi = V10% , V20% … V120% ). To evaluate the model's predictability, a fourfold cross-validation was performed for dosimetric indices such as gradient measure (GM) and brain V50% . The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., ΔGM = GMpred - GMclin and fractional ΔV50% = (V50%pred - V50%clin )/V50%clin ) and a coefficient of determination, R2 . Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency. RESULTS: Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 ± 0.06 mm for ΔGM, and 0.12 ± 0.08 for ΔV50% . The R2 for the linear fit between req_PTV and req_vi was 0.985 ± 0.019 for isodose volumes ranging from V10% to V120% ; particularly, R2 = 0.995 for V50% and R2 = 0.997 for V100% . Compared to the training plans, our KBP test plan nCI was improved from 1.31 ± 0.15 to 1.15 ± 0.08 (P < 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner's intervention. CONCLUSION: We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency.
PURPOSE: To develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT). METHODS: Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (req_PTV ) and the equivalent radius of volume that receives a set of prescription dose (req_Vi , where Vi = V10% , V20% … V120% ). To evaluate the model's predictability, a fourfold cross-validation was performed for dosimetric indices such as gradient measure (GM) and brain V50% . The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., ΔGM = GMpred - GMclin and fractional ΔV50% = (V50%pred - V50%clin )/V50%clin ) and a coefficient of determination, R2 . Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency. RESULTS: Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 ± 0.06 mm for ΔGM, and 0.12 ± 0.08 for ΔV50% . The R2 for the linear fit between req_PTV and req_vi was 0.985 ± 0.019 for isodose volumes ranging from V10% to V120% ; particularly, R2 = 0.995 for V50% and R2 = 0.997 for V100% . Compared to the training plans, our KBP test plan nCI was improved from 1.31 ± 0.15 to 1.15 ± 0.08 (P < 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner's intervention. CONCLUSION: We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency.
Authors: J S Tsai; B A Buck; G K Svensson; E Alexander; C W Cheng; E G Mannarino; J S Loeffler Journal: Int J Radiat Oncol Biol Phys Date: 1991-08 Impact factor: 7.038
Authors: Edward R Laws; Jason P Sheehan; Jonas M Sheehan; Jay Jagnathan; John A Jane; Rod Oskouian Journal: J Neurooncol Date: 2004 Aug-Sep Impact factor: 4.130