Bi-level multi-criteria optimization for risk-informed radiotherapy

作者：Mara Schubert, Katrin Teichert, Z Liao, Thomas Bortfeld, Ali Ajdari · 发表于：Physics in Medicine and Biology · 年份：2026 · DOI：10.1088/1361-6560/ae8122 · 研究领域：Effects of Radiation Exposure、Advanced Radiotherapy Techniques、Lung Cancer Diagnosis and Treatment

Abstract Objective. In radiation therapy (RT) treatment planning, multi-criteria optimization (MCO) allows physicians to find the best plan efficiently. MCO is conventionally solved for a set of generic (population-wide) dosimetric criteria, ignoring patient-specific biological risk factors and compromising clinical outcomes in high-risk groups. We propose a one-shot method—risk-guided MCO—for integration of biological risk factors within conventional MCO, enabling interactive plan navigation between dosimetric and biological endpoints. Approach. A cohort of non small cell lung cancer patients receiving proton/photon RT was retrospectively analyzed. The clinical endpoint was the risk of symptomatic (grade 2+) radiation pneumonitis (RP), modeled using bootstrapped stepwise logistic regression (with interactions) accounting for baseline lung function, smoking history, and conventional dosimetric factors. We utilize an appropriately chosen order relation to fuse the conventional MCO sandwiching algorithm with bi-level optimization, restricting the (infinite) Pareto set to plans with substantial gain in the secondary risk objective for acceptable loss in primary (clinical) objectives. Thus, risk-guided MCO computes risk-optimized counterparts to clinical plans in a single run (rather than a sequential/lexicographic approach) within user-defined trade-offs. Performance was assessed in terms of clinical objectives and predicted RP risk. Main results. Across 19 patients, the risk-gu...