Current Search:  Lungs--Cancer--Radiotherapy (x)

View All Items

  • CSV Spreadsheet
(1 - 2 of 2)
Prediction of Radiobiological Indices and Equivalent Uniform Dose in Lung Cancer Radiation Therapy using an Artificial Neural Network
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)
Comparison of treatment plans calculated using ray tracing and Monte Carlo algorithms for lung cancer patients having undergone radiotherapy with cyberknife
Title
Comparison of treatment plans calculated using ray tracing and Monte Carlo algorithms for lung cancer patients having undergone radiotherapy with cyberknife.
Creator
Pennington, Andreea, Selvaraj, Raj, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
The purpose of this research is to determine the feasibility of introducing the Monte Carlo (MC) dose calculation algorithm into the clinical practice. Unlike the Ray Tracing (RT) algorithm, the MC algorithm is not affected by the tissue inhomogeneities, which are significant inside the chest cavity. A retrospective study was completed for 102 plans calculated using both the RT and MC algorithms. The D95 of the PTV was 26% lower for the MC calculation. The first parameter of conformality, as...
Show moreThe purpose of this research is to determine the feasibility of introducing the Monte Carlo (MC) dose calculation algorithm into the clinical practice. Unlike the Ray Tracing (RT) algorithm, the MC algorithm is not affected by the tissue inhomogeneities, which are significant inside the chest cavity. A retrospective study was completed for 102 plans calculated using both the RT and MC algorithms. The D95 of the PTV was 26% lower for the MC calculation. The first parameter of conformality, as defined as the ratio of the Prescription Isodose Volume to the PTV Volume was on average 1.27 for RT and 0.67 for MC. The results confirm that the RT algorithm significantly overestimates the dosages delivered confirming previous analyses. Correlations indicate that these overestimates are largest for small PTV and/or when the ratio of the volume of lung tissue to the PTV approaches 1.
Show less
Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004316
Subject Headings
Computer graphics, Diagnostic imaging, Image guided radiation therapy, Lung cancer -- Treatment, Lungs -- Cancer -- Radiotherapy, Monte Carlo method
Format
Document (PDF)