Current Search:  Lungs--Cancer (x)

View All Items

  • CSV Spreadsheet
(1 - 8 of 8)
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)
A novel method to evaluate local control of lung cancer in stereotactic body radiation therapy (SBRT) treatment using 18f-Fdg positron emission tomography (PET)
Title
A novel method to evaluate local control of lung cancer in stereotactic body radiation therapy (SBRT) treatment using 18f-Fdg positron emission tomography (PET).
Creator
Kathriarachchi, Vindu, Shang, Charles, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
An improved method is introduced for prediction of local tumor control following lung stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) patients using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET). A normalized background-corrected tumor maximum Standard Uptake Value (SUVcmax) is introduced using the mean uptake of adjacent aorta (SUVref), instead of the maximum uptake of lung tumor (SUVmax). This method minimizes the variations...
Show moreAn improved method is introduced for prediction of local tumor control following lung stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) patients using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET). A normalized background-corrected tumor maximum Standard Uptake Value (SUVcmax) is introduced using the mean uptake of adjacent aorta (SUVref), instead of the maximum uptake of lung tumor (SUVmax). This method minimizes the variations associated with SUVmax and objectively demonstrates a strong correlation between the low SUVcmax (< 2.5-3.0) and local control of post lung SBRT. The false positive rates of both SUVmax and SUVcmax increase with inclusion of early (<6 months) PET scans, therefore such inclusion is not recommended for assessing local tumor control of post lung SBRT.
Show less
Date Issued
2013
PURL
http://purl.flvc.org/fau/fd/FA0004029
Subject Headings
Cancer -- Radiotherapy, Image guided radiation therapy, Lung cancer -- Treatment, Radiopharmaceuticals, Tomography, Emission
Format
Document (PDF)
DETECTION AND CATEGORIZATION OF LUNG CANCER USING CONVOLUTIONAL NEURAL NETWORK
Title
DETECTION AND CATEGORIZATION OF LUNG CANCER USING CONVOLUTIONAL NEURAL NETWORK.
Creator
Mostafanazhad, Shahabeddin Aslmarand, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description
Medical professionals use CT images to get information about the size, shape, and location of any lung nodules. This information will help radiologist and oncologist to identify the type of cancer and create a treatment plan. However, most of the time, the diagnosis regarding the types of lung cancer is error-prone and time-consuming. One way to address these problems is by using convolutional neural networks. In this Thesis, we developed a convolutional neural network that can detect...
Show moreMedical professionals use CT images to get information about the size, shape, and location of any lung nodules. This information will help radiologist and oncologist to identify the type of cancer and create a treatment plan. However, most of the time, the diagnosis regarding the types of lung cancer is error-prone and time-consuming. One way to address these problems is by using convolutional neural networks. In this Thesis, we developed a convolutional neural network that can detect abnormalities in lung CT scans and further categorize the abnormalities to benign, malignant adenocarcinoma and malignant squamous cell carcinoma. Our network is based on DenseNet, which utilizes dense connections between layers (dense blocks), so that all layers are connected. Because of all layers being connected, different layers can reuse features from previous layers which speeds up the process and make this network computationally efficient. To retrain this network we used CT images for 314 patients (over 1500 CT images) consistent of 42 Lung Adenocarcinoma and 78 Squamous Cell Carcinoma, 118 Non cancer and 76 benign were acquired from the National Lung Screening Trial (NLST). These images were divided to two categories of Training and Validation with 70% being training dataset and 30% as validation dataset. We trained our network on Training dataset and then checked the accuracy of our model using the validation dataset. Our model was able to categorize lung cancer with an accuracy of 88%. Afterwards we calculated the the confusion matrix, Precision (Sensitivity), Recall (Positivity) and F1 score of our model for each category. Our model is able to classify Normal CT images with Normal Accuracy of 89% Precision of 94% and F1 score of 93%. For benign nodules Accuracy was 92% precision of 97% and F1 score 86%, while for Adenocarcinoma and squamous cell cancer the Accuracy was 98% and 93%, Precision 85% and 84% and F1 score 92% and 86.9%. The relatively high accuracy of our model shows that convolutional neural networks can be a valuable tool for the classification of lung cancer, especially in a small city or underdeveloped rural hospital settings and can play a role in achieving healthcare equality.
Show less
Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013965
Subject Headings
Lungs--Cancer, Neural networks (Computer science), Tomography, X-Ray Computed
Format
Document (PDF)
Empirical beam angle optimization for lung cancer intensity modulated radiation therapy
Title
Empirical beam angle optimization for lung cancer intensity modulated radiation therapy.
Creator
Doozan, Brian, Pella, Silvia, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Empirical methods of beam angle optimization (BAO) are tested against the BAO that is currently employed in Eclipse treatment planning software. Creating an improved BAO can decrease the amount of time a dosimetrist spends on making a treatment plan, improve the treatment quality and enhance the tools an inexperienced dosimetrist can use to develop planning techniques. Using empirical data created by experienced dosimetrists from 69 patients treated for lung cancer, the most frequently used...
Show moreEmpirical methods of beam angle optimization (BAO) are tested against the BAO that is currently employed in Eclipse treatment planning software. Creating an improved BAO can decrease the amount of time a dosimetrist spends on making a treatment plan, improve the treatment quality and enhance the tools an inexperienced dosimetrist can use to develop planning techniques. Using empirical data created by experienced dosimetrists from 69 patients treated for lung cancer, the most frequently used gantry angles were applied to four different regions in each lung to gather an optimal set of fields that could be used to treat future lung cancer patients. This method, given the moniker FAU BAO, is compared in 7 plans created with the Eclipse BAO choosing 5 fields and 9 fields. The results show that the conformality index improved by 30% or 3% when using the 5 and 9 fields. The conformation number was better by 12% from the 5 fields and 9% from the 9 fields. The organs at risk (OAR) were overall more protected to produce fewer nonstochastic effects from the radiation treatment with the FAU BAO.
Show less
Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004280, http://purl.flvc.org/fau/fd/FA00004280
Subject Headings
Cancer -- Radiotherapy, Image guided radiation therapy, Lung cancer -- Treatment, Medical physics, Medical radiology -- Data processing, Medicine -- Mathematical models
Format
Document (PDF)
Exploring appropriate offset values for pencil beam and Monte Carlo dose optimization in lung stereotactic body radiotherapy encompassing the effects of respiration and tumor location
Title
Exploring appropriate offset values for pencil beam and Monte Carlo dose optimization in lung stereotactic body radiotherapy encompassing the effects of respiration and tumor location.
Creator
Evans, Grant, Shang, Charles, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Evaluation of dose optimization using the Pencil Beam (PB) and Monte Carlo (MC) algorithms may allow physicists to apply dosimetric offsets to account for inaccuracies of the PB algorithm for lung cancer treatment with Stereotactic Body Radiotherapy (SBRT). 20 cases of Non-Small Cell Lung Cancer (NSCLC) were selected. Treatment plans were created with Brainlab iPlanDose® 4.1.2. The D97 of the Planning Target Volume (PTV) was normalized to 50 Gy on the Average Intensity Projection (AIP) using...
Show moreEvaluation of dose optimization using the Pencil Beam (PB) and Monte Carlo (MC) algorithms may allow physicists to apply dosimetric offsets to account for inaccuracies of the PB algorithm for lung cancer treatment with Stereotactic Body Radiotherapy (SBRT). 20 cases of Non-Small Cell Lung Cancer (NSCLC) were selected. Treatment plans were created with Brainlab iPlanDose® 4.1.2. The D97 of the Planning Target Volume (PTV) was normalized to 50 Gy on the Average Intensity Projection (AIP) using the fast PB and compared with MC. This exact plan with the same beam Monitor Units (MUs) was recalculated over each respiratory phase. The results show that the PB algorithm has a 2.3-2.4% less overestimation at the maximum exhalation phase than the maximum inhalation phase when compared to MC. Significantly smaller dose difference between PB and MC is also shown in plans for peripheral lesions (7.7 ± 0.7%) versus central lesions (12.7±0.8%)(p< 0.01).
Show less
Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004105, http://purl.flvc.org/fau/fd/FA00004105
Subject Headings
Drug development -- Computer simulation, Image guided radiation therapy, Lung cancer -- Treatment, Monte Carlo method, Proton beams, Transport theory
Format
Document (PDF)
A dosimetric study of a heterogeneous phantom for lung stereotactic body radiation therapy comparing Monte Carlo and pencil beam calculations to dose distributions measured with a 2-d diode array
Title
A dosimetric study of a heterogeneous phantom for lung stereotactic body radiation therapy comparing Monte Carlo and pencil beam calculations to dose distributions measured with a 2-d diode array.
Creator
Curley, Casey Michael, Ouhib, Zoubir, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Monte Carlo (MC) and Pencil Beam (PB) calculations are compared to their measured planar dose distributions using a 2-D diode array for lung Stereotactic Body Radiation Therapy (SBRT). The planar dose distributions were studied for two different phantom types: an in-house heterogeneous phantom and a homogeneous phantom. The motivation is to mimic the human anatomy during a lung SBRT treatment and incorporate heterogeneities into the pre-treatment Quality Assurance process, where measured and...
Show moreMonte Carlo (MC) and Pencil Beam (PB) calculations are compared to their measured planar dose distributions using a 2-D diode array for lung Stereotactic Body Radiation Therapy (SBRT). The planar dose distributions were studied for two different phantom types: an in-house heterogeneous phantom and a homogeneous phantom. The motivation is to mimic the human anatomy during a lung SBRT treatment and incorporate heterogeneities into the pre-treatment Quality Assurance process, where measured and calculated planar dose distributions are compared before the radiation treatment. Individual and combined field dosimetry has been performed for both fixed gantry angle (anterior to posterior) and planned gantry angle delivery. A gamma analysis has been performed for all beam arrangements. The measurements were obtained using the 2-D diode array MapCHECK 2™.
Show less
Date Issued
2015
PURL
http://purl.flvc.org/fau/fd/FA00004360
Subject Headings
Cancer -- Radiotherapy, Drug development -- Computer simulation, Image guided radiation therapy, Ion bombardment, Lung cancer -- Treatment, Medical physics, Monte Carlo method, Proton beams
Format
Document (PDF)
Phantom Study Incorporating A Diode Array Into The Treatment Planning System For Patient-Specific Quality Assurance
Title
Phantom Study Incorporating A Diode Array Into The Treatment Planning System For Patient-Specific Quality Assurance.
Creator
Curley, Casey Michael, Leventouri, Theodora, Ouhib, Zoubir, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
The purpose of this research is to accurately match the calculation environment, i.e. the treatment planning system (TPS) with the measurement environment (using a 2-D diode array) for lung Stereotactic Body Radiation Therapy (SBRT) patient-specific quality assurance (QA). Furthermore, a new phantom was studied in which the 2-D array and heterogeneities were incorporated into the patient-specific QA process for lung SBRT. Dual source dual energy computerized tomography (DSCT) and single...
Show moreThe purpose of this research is to accurately match the calculation environment, i.e. the treatment planning system (TPS) with the measurement environment (using a 2-D diode array) for lung Stereotactic Body Radiation Therapy (SBRT) patient-specific quality assurance (QA). Furthermore, a new phantom was studied in which the 2-D array and heterogeneities were incorporated into the patient-specific QA process for lung SBRT. Dual source dual energy computerized tomography (DSCT) and single energy computerized tomography (SECT) were used to model phantoms incorporating a 2-D diode array into the TPS. A water-equivalent and a heterogeneous phantom (simulating the thoracic region of a patient) were studied. Monte Carlo and pencil beam dose distributions were compared to the measured distributions. Composite and individual fields were analyzed for normally incident and planned gantry angle deliveries. The distributions were compared using γ-analysis for criteria 3% 3mm, 2% 2mm, and 1% 1mm. The Monte Carlo calculations for the DSCT modeled phantoms (incorporating the array) showed an increase in the passing percentage magnitude for 46.4 % of the fields at 3% 3mm, 85.7% at 2% 2mm, and 92.9% at 1% 1mm. The Monte Carlo calculations gave no agreement for the same γ-analysis criteria using the SECT. Pencil beam calculations resulted in lower passing percentages when the diode array was incorporated in the TPS. The DSCT modeled phantoms (incorporating the array) exhibited decrease in the passing percentage magnitude for 85.7% of the fields at 3% 3mm, 82.1% at 2% 2mm, and 71.4% at 1% 1mm. In SECT modeled phantoms (incorporating the array), a decrease in passing percentage magnitude were found for 92.9% of the fields at 3% 3mm, 89.3% at 2% 2mm, and 82.1% at 1% 1mm. In conclusion, this study demonstrates that including the diode array in the TPS results in increased passing percentages when using a DSCT system with a Monte Carlo algorithm for patient-specific lung SBRT QA. Furthermore, as recommended by task groups (e.g. TG 65, TG 101, TG 244) of the American Association of Physicists in Medicine (AAPM), pencil beam algorithms should be avoided in the presence of heterogeneous materials, including a diode array.
Show less
Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004744, http://purl.flvc.org/fau/fd/FA00004744
Subject Headings
Cancer--Radiotherapy., Lungs--Cancer--Treatment., Monte Carlo method., Proton beams., Image-guided radiation therapy., Ion bombardment., Medical physics.
Format
Document (PDF)