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Preclinical Feasibility and Evaluation of An IMRT Optimization Method That Directly Incorporates Personalized Radiosensitivity Models

D Polan1*, V Wu1, M Varsta2, D Owen1, M Epelman1, M Schipper1, M Matuszak1, (1) University of Michigan, Ann Arbor, MI, (2) Varian Medical Systems, Palo Alto, CA


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: A novel, automated intensity-modulated radiation therapy (IMRT) treatment planning workflow was developed to incorporate a personalized radiation therapy (RT) beamlet optimization step leveraging biomarkers and dose-based statistical models to estimate patient-specific toxicity and efficacy. To explore the potential benefit of this patient-specific optimization method, a preclinical investigation of the treatment planning workflow was performed to demonstrate feasibility and evaluate clinically-realistic lung RT plans generated with this process.

Methods: A prioritized optimization strategy was employed to maximize the utility metric of a plan subject to clinical dose constraints, followed by additional optimization steps to improve the clinical relevancy of the plan. The dose-dependent utility metric is defined by the probability of efficacy minus the sum of individually weighted toxicity probabilities. To evaluate this method, 5 non-small cell lung cancer (NSCLC) patients, previously treated on an IRB-approved clinical trial and representing a variety of patient geometries and patient-specific utility models, were re-planned using the new optimization method and compared to plans generated using traditional dose constraint optimization. For this NSCLC patient cohort, the patient specific utility metric incorporated models for local-regional progression-free survival, and grade 3+ cardiac events, pneumonitis, and esophagitis.

Results: In each of the five cases, the new IMRT treatment planning process successfully generated lung RT plans that directly incorporate patient-specific variability in radiation sensitivity while maintaining standard clinical constraints. Plans generated with this personalized optimization workflow resulted in an average 1.3% [range: 0.4% - 2.7%] improvement in plan utility when compared to standard dose optimization. Larger improvements were noted in cases with relatively large target volumes.

Conclusion: The successful implementation of this optimization method facilitates direct exploration and inclusion of patient-specific trade-offs within RT planning to maximize the probability of uncomplicated local control based on statistical models of control and toxicity with both dose and biomarkers as covariates.

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Funding Support, Disclosures, and Conflict of Interest: This work is funded by NIH P01CA059827 and supported in part by Varian Medical Systems Inc.


Optimization, Treatment Planning, Software


TH- External Beam- Photons: IMRT/VMAT dose optimization algorithms

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