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A Machine Learning Framework for Predicting the Tumor Control Probability of Radiation Therapy Plans

E Lee1*, Y Cao2 , A Templeton3 , R Yao4 , J Chu5 , (1) Georgia Institute of Technology, Atlanta, GA, (2) Georgia Institute of Technology, Atlanta, GA, (3) Rush University Medical Center, Chicago, IL, (4) Columbus Regional Healthcare, Columbus, GA, (5) Rush University Medical Center, Oak Brook, IL


(Wednesday, 8/1/2018) 10:30 AM - 11:00 AM

Room: Exhibit Hall | Forum 7

Purpose: Functional imaging can identify the biology and characteristics of tumor cells. This facilitates personalized treatment planning to deliver escalated dose to these tumor pockets. In this study, we apply machine learning to uncover the relationship between tumor biology/characteristics and the tumor-control-probability(TCP) of an optimized treatment plan.

Methods: Three optimized-plans are compared: 1)Standard clinical plan, 2)2-stage TCP-driven PET-image-guided dose-escalated plan, and 3)Direct TCP-optimized PET-image-guided dose-escalated plan. The TCP formulation of these treatment models are derived from the Zaider and Minerbo models. A machine learning modeling framework is designed to uncover features of the tumor pockets and patient characteristics that can predict the TCP values. Five predictive models are used (“Neural Network�,“Regression SVM�,“Decision Tree�,“Gaussian Process Regression�,“Linear Regression�) and eight patient-specific features (“PTV volume�, “PET volume�,“PET volume percentage in PTV�,“Shape factor of PET�,“Average distance from PET to bladder center�,“Average distance from PET to rectum center�,“Shortest distance from PET center to PTV boundary�, and “PET center to PTV center distance�) are constructed for 40 cervical cancer cases.

Results: Gaussian process regression using 4 features (“PTV volume�, “PET volume percentage in PTV�, “shortest distance from PET center to PTV boundary� and “PET center to PTV center distance�) gives the best prediction. The mean square prediction error over all cases are 0.0007, 0.0025, 0.0000 respectively for the three treatment models. In particular, TCP prediction of direct TCP-optimized PET-image-guided dose-escalated plans always generates a consistently good accuracy, inferring that this treatment model is more robust and reliable and will result in a more reliable treatment outcome.

Conclusion: The model involves personalized tumor biology/physical/spatial characteristics. It allows quick evaluation of the (TCP-)quality of a treatment plan. The machine-learning modeling framework provides physicians a predictive platform to evaluate the potential TCP baseline of a patient. It also allows for efficient decision-making in selecting most effective (personalized) treatment plans.

Funding Support, Disclosures, and Conflict of Interest: This research is partially supported by a grant from the National Science Foundation.


Image-guided Therapy, Pattern Recognition, Classifier Design


TH- response assessment : Machine learning

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