Enhancing precision in proton therapy: Utilizing machine learning for predicting Bragg curve peak location in cancer treatment

Tunc Asuroglu

Makale detayları

Enhancing precision in proton therapy: Utilizing machine learning for predicting Bragg curve peak location in cancer treatment

Yazarlar:

Tunc Asuroglu

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.140-161

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322642

pISSN:

1303-6009

Anahtar kelimeler:

Özet:

 In proton beam therapy, the Bragg peak is the point where protons lose energy the fastest. This point is crucial for dose control, preserving healthy tissues, minimizing lateral scattering, and the success of treatment planning. However, accurately predicting the location of the Bragg peak is challenging due to the complex interactions of protons with tissues. This study proposes a machine learning ML approach to predict the exact location of the Bragg peak from phantom tissue proton beam therapy experiments. A dataset comprising the eight most commonly used biomaterials, which mimic human tissue in proton therapy procedures, has been curated for this study. Various ML models are benchmarked to find the most successful approach. ML model parameters are further optimized using a metaheuristic approach to achieve the highest prediction capability. In addition, feature contributions of each feature in the dataset are analyzed using an explainable artificial intelligence XAI technique. According to experimental results, Random Forest RF model that is optimized with Genetic Algorithm GA achieved 0.742 Correlation Coefficient CC value, 0.069 Mean Absolute Error MAE and 0.145 Root Mean Square Error RMSE outperforming other ML models. The proposed approach can track and predict the movement of the proton beam in real-time during treatment, enhancing treatment safety and contributing to the more effective management of the treatment process. This study is the first to predict exact Bragg curve peak locations from proton beam therapy experiments using ML approaches. The optimized ML model can provide higher precision in identifying the needed beam dosage for targeted tumor and improving treatment outcomes.

 In proton beam therapy, the Bragg peak is the point where protons lose energy the fastest. This point is crucial for dose control, preserving healthy tissues, minimizing lateral scattering, and the success of treatment planning. However, accurately predicting the location of the Bragg peak is challenging due to the complex interactions of protons with tissues. This study proposes a machine learning ML approach to predict the exact location of the Bragg peak from phantom tissue proton beam therapy experiments. A dataset comprising the eight most commonly used biomaterials, which mimic human tissue in proton therapy procedures, has been curated for this study. Various ML models are benchmarked to find the most successful approach. ML model parameters are further optimized using a metaheuristic approach to achieve the highest prediction capability. In addition, feature contributions of each feature in the dataset are analyzed using an explainable artificial intelligence XAI technique. According to experimental results, Random Forest RF model that is optimized with Genetic Algorithm GA achieved 0.742 Correlation Coefficient CC value, 0.069 Mean Absolute Error MAE and 0.145 Root Mean Square Error RMSE outperforming other ML models. The proposed approach can track and predict the movement of the proton beam in real-time during treatment, enhancing treatment safety and contributing to the more effective management of the treatment process. This study is the first to predict exact Bragg curve peak locations from proton beam therapy experiments using ML approaches. The optimized ML model can provide higher precision in identifying the needed beam dosage for targeted tumor and improving treatment outcomes.

 In proton beam therapy, the Bragg peak is the point where protons lose energy the fastest. This point is crucial for dose control, preserving healthy tissues, minimizing lateral scattering, and the success of treatment planning. However, accurately predicting the location of the Bragg peak is challenging due to the complex interactions of protons with tissues. This study proposes a machine learning ML approach to predict the exact location of the Bragg peak from phantom tissue proton beam therapy experiments. A dataset comprising the eight most commonly used biomaterials, which mimic human tissue in proton therapy procedures, has been curated for this study. Various ML models are benchmarked to find the most successful approach. ML model parameters are further optimized using a metaheuristic approach to achieve the highest prediction capability. In addition, feature contributions of each feature in the dataset are analyzed using an explainable artificial intelligence XAI technique. According to experimental results, Random Forest RF model that is optimized with Genetic Algorithm GA achieved 0.742 Correlation Coefficient CC value, 0.069 Mean Absolute Error MAE and 0.145 Root Mean Square Error RMSE outperforming other ML models. The proposed approach can track and predict the movement of the proton beam in real-time during treatment, enhancing treatment safety and contributing to the more effective management of the treatment process. This study is the first to predict exact Bragg curve peak locations from proton beam therapy experiments using ML approaches. The optimized ML model can provide higher precision in identifying the needed beam dosage for targeted tumor and improving treatment outcomes.

 In proton beam therapy, the Bragg peak is the point where protons lose energy the fastest. This point is crucial for dose control, preserving healthy tissues, minimizing lateral scattering, and the success of treatment planning. However, accurately predicting the location of the Bragg peak is challenging due to the complex interactions of protons with tissues. This study proposes a machine learning ML approach to predict the exact location of the Bragg peak from phantom tissue proton beam therapy experiments. A dataset comprising the eight most commonly used biomaterials, which mimic human tissue in proton therapy procedures, has been curated for this study. Various ML models are benchmarked to find the most successful approach. ML model parameters are further optimized using a metaheuristic approach to achieve the highest prediction capability. In addition, feature contributions of each feature in the dataset are analyzed using an explainable artificial intelligence XAI technique. According to experimental results, Random Forest RF model that is optimized with Genetic Algorithm GA achieved 0.742 Correlation Coefficient CC value, 0.069 Mean Absolute Error MAE and 0.145 Root Mean Square Error RMSE outperforming other ML models. The proposed approach can track and predict the movement of the proton beam in real-time during treatment, enhancing treatment safety and contributing to the more effective management of the treatment process. This study is the first to predict exact Bragg curve peak locations from proton beam therapy experiments using ML approaches. The optimized ML model can provide higher precision in identifying the needed beam dosage for targeted tumor and improving treatment outcomes.

Makale detayları

Enhancing precision in proton therapy: Utilizing machine learning for predicting Bragg curve peak location in cancer treatment

Yazarlar:

Tunc Asuroglu

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.140-161

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322642

pISSN:

1303-6009

Machine learning

regression

proton therapy

Bragg curve

Sayı

Makale detayları

Enhancing precision in proton therapy: Utilizing machine learning for predicting Bragg curve peak location in cancer treatment

Yazarlar: Tunc Asuroglu

Yayıncı: Ankara University

Ana Başlıklar: Information Systems (Other)

Dergi: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.140-161

Belge Tipi: Makale

Yayın Tarihi: 11/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001322642

pISSN: 1303-6009

Machine learning

regression

proton therapy

Bragg curve

Sayı

Yazarlar:

Tunc Asuroglu

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.140-161

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322642

pISSN:

1303-6009