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- Title
- Dosimetric and Radiobiological Comparison of the Effects of High Definition versus Normal Collimation on Treatment Plans for Stereotactic Lung Cancer Radiation Therapy.
- Creator
- Pudasaini, Mukunda Prasad, Pella, Silvia, Leventouri, Theodora, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
- Abstract/Description
-
Stereotactic Body Radiation Therapy (SBRT) is a modern precision radiation therapy to deliver the dose in 1 to 5 fractions with high target dose conformity, and steep dose gradient towards healthy tissues. The dose delivered is influenced by the leaf width of the MLC, especially in case of SBRT. Treatment plans with high definition (HD) MLC having leaf-width 2.5 mm and normal MLC having leaf-width 5 mm, were compared to quantify dosimetric and radiobiological parameters. Dosimetric parameters...
Show moreStereotactic Body Radiation Therapy (SBRT) is a modern precision radiation therapy to deliver the dose in 1 to 5 fractions with high target dose conformity, and steep dose gradient towards healthy tissues. The dose delivered is influenced by the leaf width of the MLC, especially in case of SBRT. Treatment plans with high definition (HD) MLC having leaf-width 2.5 mm and normal MLC having leaf-width 5 mm, were compared to quantify dosimetric and radiobiological parameters. Dosimetric parameters conformity indices (CI), gradient indices (GI) and heterogeneity indices (HI) were compared. The radiobiological parameters were evaluated by normal tissue complication probability (NTCP) and tumor control probability (TCP) based on the equivalent uniform dose (EUD). The results show that there is dosimetric and radiobiological merit of the HD MLC over the normal MLC. However, the improvement is not consistent with all the plans and thus further research is required prior to conclusion.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013148
- Subject Headings
- Radiosurgery, Lung Neoplasms, Radiation dosimetry, Radiobiology
- Format
- Document (PDF)
- Title
- Prediction of Radiobiological Indices and Equivalent Uniform Dose in Lung Cancer Radiation Therapy using an Artificial Neural Network.
- Creator
- Pudasaini, Mukunda Prasad, Leventouri, Theodora, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
- Abstract/Description
-
In radiotherapy, radiobiological indices tumor control probability (TCP), normal tissue complication probability (NTCP), and equivalent uniform dose (EUD) are computed by analytical models. These models are rarely employed to rank and optimize treatment plans even though radiobiological indices weights more compared to dosimetric indices to reflect treatment goal. The objective of this study is to predict TCP, NTCP and EUDs for lung cancer radiotherapy treatment plans using an artificial...
Show moreIn radiotherapy, radiobiological indices tumor control probability (TCP), normal tissue complication probability (NTCP), and equivalent uniform dose (EUD) are computed by analytical models. These models are rarely employed to rank and optimize treatment plans even though radiobiological indices weights more compared to dosimetric indices to reflect treatment goal. The objective of this study is to predict TCP, NTCP and EUDs for lung cancer radiotherapy treatment plans using an artificial neural network (ANN). A total of 100 lung cancer patients’ treatment plans were selected for this study. Normal tissue complication probability (NTCP) of organs at risk (OARs) i.e., esophagus, spinal cord, heart and contralateral lung and tumor control probability (TCP) of treatment target volume (i.e., tumor) were calculated by the equivalent uniform dose (EUD) model. TCP/NTCP pairing with corresponding EUD are used individually as outputs for the neural network. The inputs for ANN are planning target volume (PTV), treatment modality, tumor location, prescribed dose, number of fractions, mean dose to PTV, gender, age, and mean doses to the OARs. The ANN is based on Levenberg-Marquardt algorithm with one hidden layer having 13 inputs and 2 outputs. 70% of the data was used for training, 15% for validation and 15% for testing the ANN. Our ANN model predicted TCP and EUD with correlation coefficient of 0.99 for training, 0.96 for validation, and 0.94 for testing. In NTCP and EUD prediction, averages of correlation coefficients are 0.94 for training, 0.89 for validation and 0.84 for testing. The maximum mean squared error (MSE) for the ANN is 0.025 in predicting the NTCP and EUD of heart. Our results show that an ANN model can be used with high discriminatory power to predict the radiobiological indices for lung cancer treatment plans.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014064
- Subject Headings
- Lungs--Cancer--Radiotherapy, Radiobiology, Neural networks (Computer science)
- Format
- Document (PDF)