Background We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case scenario optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans’ ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from the internal target volume (ITV). The second employed the worst-case scenario optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests. Results Without respiratory motion considered, we affirmed that worst-case scenario optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, worst-case-scenario optimization still achieved more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% − D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)]. Conclusions Worst-case scenario optimization led to superior solutions for lung IMPT. Despite of the fact that worst-case-scenario optimization did not explicitly account for respiratory motion it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.

PurposeTo commission an open source Monte Carlo (MC) dose engine, “MCsquare” for a synchrotron‐based proton machine, integrate it into our in‐house C++‐based I/O user interface and our web‐based software platform, expand its functionalities, and improve calculation efficiency for intensity‐modulated proton therapy (IMPT).MethodsWe commissioned MCsquare using a double Gaussian beam model based on in‐air lateral profiles, integrated depth dose of 97 beam energies, and measurements of various spread‐out Bragg peaks (SOBPs). Then we integrated MCsquare into our C++‐based dose calculation code and web‐based second check platform “DOSeCHECK.” We validated the commissioned MCsquare based on 12 different patient geometries and compared the dose calculation with a well‐benchmarked GPU‐accelerated MC (gMC) dose engine. We further improved the MCsquare efficiency by employing the computed tomography (CT) resampling approach. We also expanded its functionality by adding a linear energy transfer (LET)‐related model‐dependent biological dose calculation.ResultsDifferences between MCsquare calculations and SOBP measurements were <2.5% (<1.5% for ~85% of measurements) in water. The dose distributions calculated using MCsquare agreed well with the results calculated using gMC in patient geometries. The average 3D gamma analysis (2%/2 mm) passing rates comparing MCsquare and gMC calculations in the 12 patient geometries were 98.0 ± 1.0%. The computation time to calculate one IMPT plan in patients’ geometries using an inexpensive CPU workstation (Intel Xeon E5‐2680 2.50 GHz) was 2.3 ± 1.8 min after the variable resolution technique was adopted. All calculations except for one craniospinal patient were finished within 3.5 min.ConclusionsMCsquare was successfully commissioned for a synchrotron‐based proton beam therapy delivery system and integrated into our web‐based second check platform. After adopting CT resampling and implementing LET model‐dependent biological dose calculation capabilities, MCsquare will be sufficiently efficient and powerful to achieve Monte Carlo‐based and LET‐guided robust optimization in IMPT, which will be done in the future studies.

We conclude that noise levels in cone-beam CTs that might reduce manual contouring accuracy do not reduce image registration and automatic contouring accuracy.

The purpose of this work is to investigate the effect of dose-calculation accuracy on head and neck (H&N) intensity modulated radiation therapy (IMRT) plans by determining the systematic dose-prediction and optimization-convergence errors (DPEs and OCEs), using a superposition/convolution (SC) algorithm. Ten patients with locally advanced H&N squamous cell carcinoma who were treated with simultaneous integrated boost IMRT were selected for this study. The targets consisted of gross target volume (GTV), clinical target volume (CTV), and nodal target volumes (CTV nodes). The critical structures included spinal cord, parotid glands, and brainstem. For all patients, three IMRT plans were created: A: an SC optimized plan (SCopt), B: an SCopt plan recalculated with Monte Carlo [MC(SCopt)], and C: an MC optimized plan (MCopt). For each structure, DPEs and OCEs were estimated as DPE(SC)=D(B)-D(A) and OCE(SC)=D(C)-D(B) where A, B, and C stand for the three different optimized plans as defined above. Deliverable optimization was used for all plans, that is, a leaf-sequencing step was incorporated into the optimization loop at each iteration. The range of DPE(SC) in the GTV D98 varied from -1.9% to -4.9%, while the OCE(SC) ranged from 0.9% to 7.0%. The DPE(SC) in the contralateral parotid D50 reached 8.2%, while the OCE(SC) in the contralateral parotid D50 varied from 0.91% to 6.99%. The DPE(SC) in cord D2 reached -3.0%, while the OCE(SC) reached to -7.0%. The magnitude of the DPE(SC) and OCE(SC) differences demonstrate the importance of using the most accurate available algorithm in the deliverable IMRT optimization process, especially for the estimation of normal structure doses.