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Title: Efficient Machine Learning Algorithms for Identifying Risk Factors of Prostate and Breast Cancers among Males and Females.
Creator: Rikhtehgaran, Samaneh, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: One of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest...
Show moreOne of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest Neighbors, and Naïve Bayes has been performed. This research aims to identify the most efficient machine learning algorithms for identifying the most significant risk factors of prostate and breast cancers. For this reason, National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian (PLCO) datasets are used. A comprehensive comparison of risk factors leading to these two crucial cancers can significantly impact early detection and progressive improvement in survival.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013755
Subject Headings: Machine learning, Algorithms, Cancer--Risk factors, Breast--Cancer, Prostate--Cancer
Format: Document (PDF)

Title: Liver Cancer Risk Quantification through an Artificial Neural Network based on Personal Health Data.
Creator: Ataei, Afrouz, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Liver cancer is the sixth most common type of cancer worldwide and is the third leading cause of cancer related mortality. Several types of cancer can form in the liver. Hepatocellular carcinoma (HCC) makes up 75%-85% of all primary liver cancers and it is a malignant disease with limited therapeutic options due to its aggressive progression. While the exact cause of liver cancer may not be known, habits/lifestyle may increase the risk of developing the disease. Several risk prediction models...
Show moreLiver cancer is the sixth most common type of cancer worldwide and is the third leading cause of cancer related mortality. Several types of cancer can form in the liver. Hepatocellular carcinoma (HCC) makes up 75%-85% of all primary liver cancers and it is a malignant disease with limited therapeutic options due to its aggressive progression. While the exact cause of liver cancer may not be known, habits/lifestyle may increase the risk of developing the disease. Several risk prediction models for HCC are available for individuals with hepatitis B and C virus infections who are at high risk but not for general population. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data to predict liver cancer risk. Our results indicate that our ANN can be used to predict liver cancer risk with changes with lifestyle and may provide a novel approach to identify patients at higher risk and can be bene ted from early diagnosis.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013742
Subject Headings: Liver--Cancer, Artificial neural networks, Neural networks (Computer science), Cancer--Risk assessment
Format: Document (PDF)

Title: NON-RADIOACTIVE ELEMENTS FOR PROMPT GAMMA ENHANCEMENT IN PROTON THERAPY.
Creator: Galanakou, Panagiota, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Intensity modulated proton beam scanning therapy allows for highly conformal dose distribution and better sparing of organ-at-risk compared to conventional photon radiotherapy, thanks to the characteristic dose deposition at depth, the Bragg Peak (BP), of protons as a function of depth and energy. However, proton range uncertainties lead to extended clinical margins, at the expense of treatment quality. Prompt Gamma (PG) rays emitted during non- elastic interactions of proton with the matter...
Show moreIntensity modulated proton beam scanning therapy allows for highly conformal dose distribution and better sparing of organ-at-risk compared to conventional photon radiotherapy, thanks to the characteristic dose deposition at depth, the Bragg Peak (BP), of protons as a function of depth and energy. However, proton range uncertainties lead to extended clinical margins, at the expense of treatment quality. Prompt Gamma (PG) rays emitted during non- elastic interactions of proton with the matter have been proposed for in-vivo proton range tracking. Nevertheless, poor PG statistics downgrade the potential of the clinical implementation of the proposed techniques. We study the insertion of the nonradioactive elements 19F, 17O, 127I in a tumor area to enhance the PG production of 4.44 MeV (P1) and 6.15 MeV (P2) PG rays emitted during proton irradiation, both correlated with the distal fall-off of the BP. We developed a novel Monte Carlo (MC) model using the TOPAS MC package. With this model, we simulated incident proton beams with energies of 75 MeV, 100 MeV and 200 MeV in co-centric cylindrical phantoms. The outer cylinder (scorer) was filled with water and the inner cylinder (simulating a tumor region inside water-equivalent body) was filled with water containing 0.1%–20% weight fractions of each of the tested elements.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014222
Subject Headings: Proton Therapy, Monte Carlo method--Simulation, Gamma rays
Format: Document (PDF)

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.
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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)

Title: Development of an Innovative Daily QA System for Pencil-Beam Scanning Proton Therapy.
Creator: Kassel, Maxwell, Shang, Charles, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: In this work, we have developed a robust daily quality assurance (QA) system for pencil-beam scanning (PBS) dosimetry. A novel phantom and multi-PTV PBS plan were used in conjunction with the Sun Nuclear Daily QA3 multichamber detector array to verify output, range, and spot position. The sensitivity to detect change in these parameters with our designed tests was determined empirically. Associated tolerance levels were established based on these sensitivities and guidelines published in...
Show moreIn this work, we have developed a robust daily quality assurance (QA) system for pencil-beam scanning (PBS) dosimetry. A novel phantom and multi-PTV PBS plan were used in conjunction with the Sun Nuclear Daily QA3 multichamber detector array to verify output, range, and spot position. The sensitivity to detect change in these parameters with our designed tests was determined empirically. Associated tolerance levels were established based on these sensitivities and guidelines published in recent American Association of Physics in Medicine (AAPM) task group reports. The output has remained within the 3% tolerance and the range was within ±1mm. Spot position has remained within ±2mm. This daily QA procedure is quick and efficient with the time required for setup and delivery at less than 10 minutes.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013623
Subject Headings: Proton Therapy, Radiation dosimetry, Quality assurance
Format: Document (PDF)

Title: Development of a Monte Carlo Simulation Model for Varian ProBeam Compact Single-Room Proton Therapy System using GEANT4.
Creator: String, Shawn, Muhammad, Wazir, Shang, Charles, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Proton therapy with pencil beam scanning technique is a novel technique to treat cancer patients due to its unique biophysical properties. However, a small error in dose calculation may lead towards undesired greater uncertainties in planed doses. This project aims to create a simulation model of Varian ProBeam Compact using the GEANT4 Monte Carlo simulation tool kit. Experimental data from the first clinical ProBeam Compact system at South Florida Proton Therapy Institute was used to...
Show moreProton therapy with pencil beam scanning technique is a novel technique to treat cancer patients due to its unique biophysical properties. However, a small error in dose calculation may lead towards undesired greater uncertainties in planed doses. This project aims to create a simulation model of Varian ProBeam Compact using the GEANT4 Monte Carlo simulation tool kit. Experimental data from the first clinical ProBeam Compact system at South Florida Proton Therapy Institute was used to validate the simulation model. A comparison was made between the experimental and simulated Integrated Depth-Dose curves using a 2%/2mm gamma index test with 100% of points passing. The beam spot standard deviation sizes (s!) were compared using percent deviation. All simulated s! matched the experimental s! within 2.5%, except 70 and 80 MeV. The model can be used to develop a more comprehensive model as an independent dose verification tool and further investigate dose distribution.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013547
Subject Headings: Proton Therapy, Monte-Carlo-Simulation, Radiotherapy Dosage
Format: Document (PDF)

Title: Improved Methodology of Static HDMLC Virtual Cone based Rapid Arcs for Stereotactic Ablative Radiotherapy.
Creator: Stevens, Ryan, Shang, Charles, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Physical cones equipped on GammaKnife, Cyberknife, and C-arm linacs have been the standard practice in Stereotactic Ablative Radiotherapy (SART) for small intracranial lesions, such as treating trigeminal or glossopharyngeal neuralgia targets. The advancement of high-definition multi-leaf collimators (HDMLC), treatment planning systems, and small field dosimetry now allows for treatment without the need for an auxiliary mounted physical cone. This treatment type uses the “virtual cone”, a...
Show morePhysical cones equipped on GammaKnife, Cyberknife, and C-arm linacs have been the standard practice in Stereotactic Ablative Radiotherapy (SART) for small intracranial lesions, such as treating trigeminal or glossopharyngeal neuralgia targets. The advancement of high-definition multi-leaf collimators (HDMLC), treatment planning systems, and small field dosimetry now allows for treatment without the need for an auxiliary mounted physical cone. This treatment type uses the “virtual cone”, a permanent high-definition MLC, arrangement to deliver “very small fields” with comparable spherical dose distributions to physical cones. The virtual cone therapy, on a Varian Edge™ linac using multiple non-coplanar arcs with static HDMLCs, is a comparable technique that can be used to treat small intracranial neuralgia or other small lesions. In this investigation, two flattening filter free (FFF) photon beams, 6MV FFF and 10MV FFF, were tested for optimal delivery and safety conditions for treating intracranial lesions. The virtual cone method on a Varian Edge™ Linear accelerator using rapid arc stereotactic radiosurgery was used to treat cranial neuralgia for chronic pain for six patients. Absolute dose, relative dose measurements, and monitor units were the main characteristics that were examined to decide which energy was the best for treatment. Source-to-axis distances (SAD) of 100cm measurements were taken at depths of 10cm and 5cm, respectively.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013630
Subject Headings: Radiotherapy, Radiation dosimetry, Stereotaxic Techniques
Format: Document (PDF)

Title: Nuclear Halo Effect and Field Size Factor for Pencil-Beam Scanning Proton Therapy.
Creator: Beqiri, Atdhe, Shang, Charles, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: In proton therapy systems with pencil-beam scanning, output of Halo effect is not necessarily included in Treatment Planning System (TPS). Halo effect (low-intensity tail) can significantly affect a patient’s dose distribution. The output of this dose depends on the field size being irradiated. Although much research has been made to investigate such relation to the field size, the number of reports on dose calculations including the halo effect is small. In this work we have investigated the...
Show moreIn proton therapy systems with pencil-beam scanning, output of Halo effect is not necessarily included in Treatment Planning System (TPS). Halo effect (low-intensity tail) can significantly affect a patient’s dose distribution. The output of this dose depends on the field size being irradiated. Although much research has been made to investigate such relation to the field size, the number of reports on dose calculations including the halo effect is small. In this work we have investigated the Halo effect, including field size factor, target depth factor, and air gaps with a range shifter for a Varian ProBeam. Dose calculations created on the Eclipse Treatment Planning System (vs15.6 TPS) are compared with plane-parallel ionization chambers (PTW Octavius 1500) measurements using PCS and AcurosPT MC model in different isocenters: 5cm, 10cm, and 20cm. We find that in AcurosPT algorithm deviations range between -7.53% (for 2cm field in 25cm air gap with range shifter) up to +7.40% (for 20cm field in 15cm air gap with range shifter). Whereas, in PCS algorithm the deviations are -2.07% (for 20x20cm field in open conditions) to -6.29% (for 20x20cm field in 25cm air gap with range shifter).
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013788
Subject Headings: Proton Therapy, Proton beams, Radiotherapy
Format: Document (PDF)

Title: Commissioning of 360⁰ Rotational Single Room ProBeam Compact™ (Varian Medical) Pencil Beam Scanning Proton Therapy System.
Creator: Fathallah, Shreen Mohamed, Shang, Charles, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: A clinical commissioning of the first 360 rotational compact Varian ProBeam scanning proton pencil beam (Varian Medical, Palo Alto, CA) system was conducted at the South Florida Proton Therapy Institute (SFPTI). The beam dosimetry and characterizations were the vital section used to verify the consistency of the treatment planning system (TPS) outputs. The integrated depth dose curves were acquired with AP CAX in water phantom utilizing a large PTW Bragg peak chamber; the dose output factors...
Show moreA clinical commissioning of the first 360 rotational compact Varian ProBeam scanning proton pencil beam (Varian Medical, Palo Alto, CA) system was conducted at the South Florida Proton Therapy Institute (SFPTI). The beam dosimetry and characterizations were the vital section used to verify the consistency of the treatment planning system (TPS) outputs. The integrated depth dose curves were acquired with AP CAX in water phantom utilizing a large PTW Bragg peak chamber; the dose output factors measurements were performed by using IBA PCC05 chamber at 1.5 cm water depth applying a single layer 10×10 cm2 beams and 1.1 RBE offset as recommended in TRS 398 report. Widths of the Bragg peaks ranges (Rb80-Ra80) were from 4.07 cm to 30.51 cm for the energy range 70 MeV to 220 MeV. Beam optics such as spot sizes and spot profiles were acquired in-air by using Logos scintillators with a CCD camera and the result data were from 2.33 mm to for 77 MeV to 9.70 mm for 220 MeV. In different field sizes, a comparison between the dose measured using PTW Semiflex and the AcurosPT estimated dose were performed to study the halo effect. All the measured dosimetric parameters showed that the design specifications were well achieved, and the results are suitable for being used as a part of the clinical commissioning and quality assurance program for treating patients.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013691
Subject Headings: Proton Therapy, Dosimetry
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.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014064
Subject Headings: Lungs--Cancer--Radiotherapy, Radiobiology, Neural networks (Computer science)
Format: Document (PDF)

Title: A MONTE CARLO STUDY OF THE NEUTRON AMBIENT DOSE EQUIVALENT FROM A PROTON PENCIL BEAM MEDICAL THERAPY UNIT.
Creator: Llanes, Alejandro Rene Lopez, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description: Proton Therapy, an effective cancer treatment, poses unintended consequences for patients and personnel due to secondary neutron production. This study investigates neutron attenuation in shielding materials like concrete using Monte Carlo (MC) simulations to optimize shielding requirements. Experimental limitations, such as detector sensitivity, energy range response, and spatial resolution, lead to inaccurate evaluations. MC simulations address that by modeling radiation transport and...
Show moreProton Therapy, an effective cancer treatment, poses unintended consequences for patients and personnel due to secondary neutron production. This study investigates neutron attenuation in shielding materials like concrete using Monte Carlo (MC) simulations to optimize shielding requirements. Experimental limitations, such as detector sensitivity, energy range response, and spatial resolution, lead to inaccurate evaluations. MC simulations address that by modeling radiation transport and neutron interactions with shielding materials. The TOPAS-MC code simulated secondary neutrons generated by a 226.5 MeV energy proton beam on a 30 cm diameter tissue-equivalent target. The target was placed in a 200 cm spherical concrete shell with a 100 cm inner radius and 2.3 g/cm3 density. Energy deposition and particle fluence were scored in 20 radial points across 18 angular positions, and the mean value per particle was estimated. Neutron fluence to ambient dose equivalent conversion coefficients from ICRU Report No. 95 were used to calculate the total dose equivalent values, which were scaled based on distance and concrete shield thickness.
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Date Issued: 2023
PURL: http://purl.flvc.org/fau/fd/FA00014213
Subject Headings: Proton Therapy, Monte Carlo simulation, Neutrons
Format: Document (PDF)

Proton Therapy (6)	+ -
Neural networks (Computer science) (3)	+ -
Radiation dosimetry (2)	+ -
Radiotherapy (2)	+ -
Algorithms (1)	+ -

Artificial neural networks (1)	+ -
Breast--Cancer (1)	+ -
Cancer--Risk assessment (1)	+ -
Cancer--Risk factors (1)	+ -
Dosimetry (1)	+ -
Gamma rays (1)	+ -
Liver--Cancer (1)	+ -
Lungs--Cancer (1)	+ -
Lungs--Cancer--Radiotherapy (1)	+ -
Machine learning (1)	+ -
Monte Carlo method--Simulation (1)	+ -
Monte Carlo simulation (1)	+ -
Monte-Carlo-Simulation (1)	+ -
Neutrons (1)	+ -
Prostate--Cancer (1)	+ -
Proton beams (1)	+ -
Quality assurance (1)	+ -
Radiobiology (1)	+ -
Radiotherapy Dosage (1)	+ -
Stereotaxic Techniques (1)	+ -
Tomography, X-Ray Computed (1)	+ -

Charles E. Schmidt College of Science (11)	+ -
Department of Physics (11)	+ -
Florida Atlantic University (11)	+ -
Muhammad, Wazir (11)	+ -
Shang, Charles (5)	+ -

Ataei, Afrouz (1)	+ -
Beqiri, Atdhe (1)	+ -
Fathallah, Shreen Mohamed (1)	+ -
Galanakou, Panagiota (1)	+ -
Kassel, Maxwell (1)	+ -
Leventouri, Theodora (1)	+ -
Llanes, Alejandro Rene Lopez (1)	+ -
Mostafanazhad, Shahabeddin Aslmarand (1)	+ -
Pudasaini, Mukunda Prasad (1)	+ -
Rikhtehgaran, Samaneh (1)	+ -
Stevens, Ryan (1)	+ -
String, Shawn (1)	+ -

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