Patnaikuni Santosh Kumar, Saini Sapan Mohan, Chandola Rakesh Mohan, Chandrakar Pradeep, Chaudhary Vivek
Department of Physics, National Institute of Technology, Raipur, Chhattisgarh, India.
Department of Radiotherapy, Pt. JNM Medical College, Raipur, Chhattisgarh, India.
J Med Phys. 2020 Apr-Jun;45(2):88-97. doi: 10.4103/jmp.JMP_110_19. Epub 2020 Jul 20.
The purpose of present study is to estimate asymmetric margins of prostate target volume based on biological limitations with help of knowledge based fuzzy logic considering the effect of organ motion and setup errors.
A novel application of fuzzy logic modelling technique considering radiotherapy uncertainties including setup, delineation and organ motion was used in this study to derive margins. The new margin was applied in prostate cancer treatment planning and the results compared very well to current techniques Here volumetric modulated arc therapy treatment plans using stepped increments of asymmetric margins of planning target volume (PTV) were performed to calculate the changes in prostate radiobiological indices and results were used to formulate the rule based and membership function for Mamdani-type fuzzy inference system. The optimum fuzzy rules derived from input data, the clinical goals and knowledge-based conditions imposed on the margin limits. The PTV margin obtained using the fuzzy model was compared to the commonly used margin recipe.
For total displacement standard errors ranging from 0 to 5 mm the fuzzy PTV margin was found to be up to 0.5 mm bigger than the vanHerk derived margin, however taking the modelling uncertainty into account results in a good match between the PTV margin calculated using our model and the one based on van Herk formulation for equivalent errors of up to 5 mm standard deviation (s. d.) at this range. When the total displacement standard errors exceed 5 mm s. d., the fuzzy margin remained smaller than the van Herk margin.
The advantage of using knowledge based fuzzy logic is that a practical limitation on the margin size is included in the model for limiting the dose received by the critical organs. It uses both physical and radiobiological data to optimize the required margin as per clinical requirement in real time or adaptive planning, which is an improvement on most margin models which mainly rely on physical data only.
本研究的目的是借助基于知识的模糊逻辑，在考虑器官运动和摆位误差影响的情况下，根据生物学限制来估计前列腺靶区体积的不对称边界。
本研究采用了一种新型的模糊逻辑建模技术应用，该技术考虑了包括摆位、轮廓勾画和器官运动在内的放射治疗不确定性，以得出边界。将新的边界应用于前列腺癌治疗计划中，其结果与当前技术相比非常良好。在此，使用计划靶区（PTV）不对称边界的逐步增量进行容积调强弧形放疗治疗计划，以计算前列腺放射生物学指标的变化，并将结果用于制定基于规则的Mamdani型模糊推理系统的隶属函数。从输入数据、临床目标以及对边界限制施加的基于知识的条件中得出最佳模糊规则。将使用模糊模型获得的PTV边界与常用的边界处方进行比较。
对于总位移标准误差范围从0至5毫米，发现模糊PTV边界比范赫克得出的边界大至多0.5毫米，然而，考虑到建模不确定性，在此范围内对于高达5毫米标准差（s.d.）的等效误差，使用我们的模型计算的PTV边界与基于范赫克公式计算的边界之间实现了良好匹配。当总位移标准误差超过5毫米标准差时，模糊边界仍小于范赫克边界。
使用基于知识的模糊逻辑的优势在于，模型中纳入了对边界大小的实际限制，以限制关键器官所接受的剂量。它利用物理和放射生物学数据，根据实时或自适应计划中的临床需求来优化所需边界，这是对大多数主要仅依赖物理数据的边界模型的一种改进。
