Light Pareto robust optimization for IMRT treatment planning.

BACKGROUND: Robust optimization (RO) has been proposed to mitigate breathing motion uncertainty during treatment in intensity-modulated radiation therapy (IMRT) planning for breast or lung cancer. RO is a pessimistic approach that implicitly trades off average-case for worst-case treatment plan quality. Pareto robust optimization (PRO) provides a mechanism for improving nonworst-case plan outcomes, but often remains overly conservative in the average case. PURPOSE: The goal of this study is to characterize the trade-off between the optimality of robust IMRT plans in the worst case and the treatment quality in nonworst-case realizations of breathing motion. We provide a light Pareto robust optimization (LPRO) method for IMRT and test its clinical viability for improving the average-case plan quality while preserving robustness, in comparison to RO and PRO plans. METHODS: Five clinical left-sided breast cancer patients were included in the study, each with an associated 4D-CT dataset approximating their breathing cycle. Using simulation, 50 different breathing patterns were generated for each patient. A first-stage optimization was solved with the objective of cardiac sparing while ensuring robustness on the target dose under breathing uncertainty. Next, a second-stage objective of overdose minimization was considered to improve plan quality in a controlled LPRO framework. For the simulated breathing scenarios, the trade-off between loss of average cardiac sparing at worst-case and the overdose to the breast was quantified by calculating the accumulated dose for each plan in each breathing scenario. Finally, the RO, PRO, and LPRO plans were each evaluated using eight clinical dose-volume criteria on the target and organs at risk. RESULTS: The LPRO models allowed for significantly sharper dose falloffs in the expected dose instances, relative to both RO and PRO models. Plans began looking valid for delivery with average allowances of as little as +0.1 Gy additional dose to the heart, and most patients experienced diminishing returns beyond +0.2 Gy. CONCLUSIONS: Without sacrificing robustness, the LPRO approach produces viable plans with true total-target irradiation. Furthermore, the plans produced were able to reduce the nonworst-case downside typical of RO, without the characteristic overdosing or average-case pessimism seen in prior models.

Robust radiotherapy planning.

Motion and uncertainty in radiotherapy is traditionally handled via margins. The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose. However, the PTV concept has several limitations, especially in proton therapy. Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV. Initial work focused on random and systematic setup errors in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus. Over the past ten years, IMPT has emerged as a new application for robust planning methods. In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams - a problem that cannot generally be addressed by margins. Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use. This paper first summarizes the limitations of the PTV concept. Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.

Robust optimization methods for cardiac sparing in tangential breast IMRT.

PURPOSE: In left-sided tangential breast intensity modulated radiation therapy (IMRT), the heart may enter the radiation field and receive excessive radiation while the patient is breathing. The patient's breathing pattern is often irregular and unpredictable. We verify the clinical applicability of a heart-sparing robust optimization approach for breast IMRT. We compare robust optimized plans with clinical plans at free-breathing and clinical plans at deep inspiration breath-hold (DIBH) using active breathing control (ABC). METHODS: Eight patients were included in the study with each patient simulated using 4D-CT. The 4D-CT image acquisition generated ten breathing phase datasets. An average scan was constructed using all the phase datasets. Two of the eight patients were also imaged at breath-hold using ABC. The 4D-CT datasets were used to calculate the accumulated dose for robust optimized and clinical plans based on deformable registration. We generated a set of simulated breathing probability mass functions, which represent the fraction of time patients spend in different breathing phases. The robust optimization method was applied to each patient using a set of dose-influence matrices extracted from the 4D-CT data and a model of the breathing motion uncertainty. The goal of the optimization models was to minimize the dose to the heart while ensuring dose constraints on the target were achieved under breathing motion uncertainty. RESULTS: Robust optimized plans were improved or equivalent to the clinical plans in terms of heart sparing for all patients studied. The robust method reduced the accumulated heart dose (D10cc) by up to 801 cGy compared to the clinical method while also improving the coverage of the accumulated whole breast target volume. On average, the robust method reduced the heart dose (D10cc) by 364 cGy and improved the optBreast dose (D99%) by 477 cGy. In addition, the robust method had smaller deviations from the planned dose to the accumulated dose. The deviation of the accumulated dose from the planned dose for the optBreast (D99%) was 12 cGy for robust versus 445 cGy for clinical. The deviation for the heart (D10cc) was 41 cGy for robust and 320 cGy for clinical. CONCLUSIONS: The robust optimization approach can reduce heart dose compared to the clinical method at free-breathing and can potentially reduce the need for breath-hold techniques.