Cancer stands as a significant global mortality factor. Timely detection and proper treatment, such as intensity-modulated radiation therapy (IMRT), can hinder cancer progression. IMRT is crucial for targeting tumors while safeguarding healthy tissues. This study proposes a robust mathematical model addressing uncertainties in the dose delivered to the tumor as treatment sessions progress, using a polyhedral uncertainty set. The model employs linear constraints and a quadratic objective function derived from dose calculations. Variations of Newton-barrier and reduced gradient algorithms are utilized to solve the problem using real patient data, particularly from a prostate cancer case. Notably, the proposed method reduces penalties for healthy organs by approximately nine percent while maintaining an adequate dose delivered to the tumor.
IMRT treatment planning under dose uncertainty: A polyhedral and quadratic programming optimization model
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