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A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

Y Luo

Y Luo1*, D McShan1 , M Schipper1 , M Matuszak1 , F Kong2 , R Ten Haken1 , (1) University of Michigan, Ann Arbor, MI, (2) Georgia Regents University, Augusta, GA


SU-E-J-4 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: To develop a decision support tool to predict a patient’s potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect.

Methods: Important relationships between a patient’s basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity to different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient’s OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization.

Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient’s potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support.

Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient’s satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers.

Funding Support, Disclosures, and Conflict of Interest: P01-CA59827;R01CA142840

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