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Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine
Amirarash Kashef1, Toktam Khatibi1, Azim Mehrvar2,3
1School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
Insights
Machine learning can predict the necessity of cranial radiotherapy (CRT) in pediatric Acute Lymphoblastic Leukemia (ALL) patients. This approach helps avoid unnecessary CRT, improving patient outcomes and reducing costs.
Area of Science:
- Oncology
- Pediatrics
- Medical Informatics
Background:
- Acute Lymphoblastic Leukemia (ALL) is a leading childhood cancer with a 90% survival rate due to improved treatments.
- Cranial radiotherapy (CRT) has been a standard treatment, but recent studies suggest it may not always be necessary and can sometimes be omitted without affecting outcomes.
Purpose of the Study:
- To develop a machine learning model for predicting the need for cranial radiotherapy (CRT) in pediatric Acute Lymphoblastic Leukemia (ALL) patients.
- To identify key factors influencing the decision to use CRT in treating childhood ALL.
- To support clinical decision-making and potentially reduce the application of CRT.
Main Methods:
- A stacked ensemble machine learning classifier was designed and trained on a dataset of 241 pediatric ALL patients (aged 0-17).
- The dataset, comprising 31 attributes, was collected from patient records at Mahak charity hospital.
- The model was evaluated using Area Under the Curve (AUC) and feature importance analysis.
Main Results:
- The stacked ensemble classifier achieved an AUC of 87.52%, demonstrating strong predictive performance.
- Disease relapse was identified as the most significant predictor for the necessity of CRT.
- The model provides a data-driven approach to personalize ALL treatment strategies.
Conclusions:
- The study suggests that the use of CRT in childhood ALL can be reduced, leading to more cost-effective and healthier treatment protocols.
- Machine learning models can aid clinicians in making informed decisions about CRT.
- Enhancing clinical databases and creating synthetic data are crucial for future research and improved patient care.
Background:
Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. Due to major improvements in the treatment protocols in the 50-years period, the survivability of this disease has witnessed dramatic rise until this date which is about 90 percent. There are many investigations tending to indicate the efficiency of cranial radiotherapy found out that without that, outcome of the patients did not change and even it improved at some cases.
Methods:
the main aim of this study is predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients using machine learning. Scope of this paper is intertwined with predicting the necessity of one of the treatment modalities that has been used for many years for this group of patients named Cranial Radiotherapy (CRT). For this purpose, a case study is considered at Mahak charity hospital. In this paper, our focus is on ALL patients aged 0 to 17 treated at Mahak hospital, one of the best centers for treatment of childhood malignancies in Iran. Dataset analyzed in this study is gathered by the research team from patient's paper-based files. Our dataset consists of 241 observations on patients with 31 attributes after the data cleaning process. Our designed machine learning model for predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients is a stacked ensemble classifier of independently strong models with a meta-learner to tune the weights and parameters of the base classifiers.
Results:
The stacked ensemble classifier show highly reasonable performance with AUC of 87.52%. Moreover, the attributes are ranked based on their predictive power and the most important variable for CRT necessity prediction is the disease relapse.
Conclusion:
In order to conclude, derived from previous studies regarding CRT it is not only cost-effective but also more healthy to eradicate the use of CRT for the treatment of childhood ALL. Furthermore, it is valuable to increase the clinical databases by creating more synthetic health databases not only for research purposes but also for physicians to keep track of their patient's status.
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