In IMRT treatment plan optimization there are various methods that try to regularize the variation of dose nonuniformity using purely dosimetric measures. However, although these methods can help in finding a good dose distribution, they do not provide any information regarding the expected treatment outcome. When a treatment plan optimization is performed using biological measures, the final goal should be some indication about the expected tumor control or normal tissue complications, which is the primary goal of treatment planning (the association of treatment configurations and dose prescription with the treatment outcome). In this study, this issue is analyzed distinguishing the dose-oriented treatment plan optimization from the response-oriented optimization. Three different dose distributions were obtained by using a dose-based optimization technique, an EUD-based optimization without applying any technique for regularizing the nonuniformity of the dose distribution, and an EUD-based optimization using a variational regularization technique, which controls dose nonuniformity. The clinical effectiveness of the three dose distributions was investigated by calculating the response probabilities of the tumors and organs-at-risk (OARs) involved in two head and neck and prostate cancer cases. The radiobiological models used are the linear-quadratic-Poisson and the Relative Seriality models. Furthermore, the complication-free tumor control probability and the biologically effective uniform dose (D) were used for treatment plan evaluation and comparison. The radiobiological comparison shows that the EUD-based optimization using L-curve regularization gives better results than the EUD-based optimization without regularization and dose-based optimization in both clinical cases. Concluding, it appears that the applied dose nonuniformity regularization technique is expected to improve the effectiveness of the optimized IMRT dose distributions. However, more patient cases are needed to validate the statistical significance of the results and conclusions presented in this paper.
Purpose: Regularization techniques for determining the optimal dose distribution have been proposed because the dose distributions produced by different IMRT treatment planning optimization algorithms are highly non‐uniform in the target volume. In the present work, an analysis is made about the relation of the DVH gradient and the dose to the PTV and normal tissues. Method and Materials: In this study, two head & neck and prostate cancer cases treated with IMRT were employed. Three different dose distributions were obtained by using a dose‐based optimization technique, an EUD‐based optimization without regularization of non‐uniformity and an EUD‐based optimization using a variational regularization technique. The clinical effectiveness of the three dose distributions was investigated by using the complication‐free tumor control probability, P+ and the biologically effective uniform dose. Results: In the head & neck case, for the dose‐based optimization, the P+ value is 32.9%, the total control probability PB is 79.6% and the total complication probability PI is 49.0%. For the EUD‐based no‐reg optimization, the P+ value is 56.4%, the PB value is 71.9% and the PI value is 15.5%. For the EUD‐based reg optimization, the P+ value is 67.3%, the PB value is 87.4% and the PI value is 20.1%. In the prostate case, for the dose‐based optimization, the P+ value is 94.8%, the PB value is 97.8% and the PI value is 3.0%. For the EUD‐based no‐reg optimization, the P+ value is 86.0%, the PB value is 97.3% and the PI value is 11.3%. For the EUD‐based reg optimization, the P+ value is 95.3%, the PB value is 98.4% and the PI value is 3.1%. Conclusion: The radiobiological comparison shows that the EUD‐based optimization with regularization gives better results than the EUD‐based optimization without regularization and dose‐based optimization in both clinical cases, which indicates better clinical effectiveness.
Pre-treatment patient repositioning in highly conformal image-guided radiation therapy modalities is a prerequisite for reducing setup uncertainties. In Helical Tomotherapy (HT) treatment, a megavoltage CT (MVCT) image is usually acquired to evaluate daily changes in the patient's internal anatomy and setup position. This MVCT image is subsequently compared to the kilovoltage CT (kVCT) study that was used for dosimetric planning, by applying a registration process. This study aims at investigating the expected effect of patient setup correction using the Hi-Art tomotherapy system by employing radiobiological measures such as the biologically effective uniform dose (D) and the complication-free tumor control probability (P + ). A new module of the Tomotherapy software (TomoTherapy, Inc, Madison, WI) called Planned Adaptive is employed in this study. In this process the delivered dose can be calculated by using the sinogram for each delivered fraction and the registered MVCT image set that corresponds to the patient's position and anatomical distribution for that fraction. In this study, patients treated for lung, pancreas and prostate carcinomas are evaluated by this method. For each cancer type, a Helical Tomotherapy plan was developed. In each cancer case, two dose distributions were calculated using the MVCT image sets before and after the patient setup correction. The fractional dose distributions were added and renormalized to the total number of fractions planned. The dosimetric and radiobiological differences of the dose distributions with and without patient setup correction were calculated. By using common statistical measures of the dose distributions and the P + and D concepts and plotting the tissue response probabilities vs. D a more comprehensive comparison was performed based on radiobiological measures. For the lung cancer case, at the clinically prescribed dose levels of the dose distributions, with and without patient setup correction, the complication-free tumor control probabilities, P + are 48.5% and 48.9%for a D ITV of 53.3 Gy. The respective total control probabilities, P B are 56.3% and 56.5%, whereas the corresponding total complication probabilities, P I are 7.9% and 7.5%. For the pancreas cancer case, at the prescribed dose levels of the two dose distributions, the P + values are 53.7% and 45.7% for a D ITV of 54.7 Gy and 53.8 Gy, respectively. The respective P B values are 53.7% and 45.8%, whereas the corresponding P I values are ~0.0% and 0.1%. For the prostate cancer case, at the prescribed dose levels of the two dose distributions, the P + values are 10.9% for a D ITV of 75.2 Gy and 11.9% for a D ITV of 75.4 Gy, respectively. The respective P B values are 14.5% and 15.3%, whereas the corresponding P I values are 3.6% and 3.4%. Our analysis showed that the very good daily patient setup and dose delivery were very close to the intended ones. With the exception of the pancreas cancer case, the deviations observed between the dose distributions with and without patient setup correcti...
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