Pencil-beam scanning (PBS) proton therapy (PT), particularly intensity modulated PT, represents the latest advanced PT technology for treating cancers, including thoracic malignancies. On the basis of virtual clinical studies, PBS-PT appears to have great potential in its ability to tightly tailor the dose to the target while sparing critical structures, thereby reducing treatment-related toxicities, particularly for tumors in areas with complicated anatomy. However, implementing PBS-PT for moving targets has several additional technical challenges compared with intensity modulated photon radiation therapy or passive scattering PT. Four-dimensional computed tomography-based motion management and robust optimization and evaluation are crucial for minimizing uncertainties associated with beam range and organ motion. Rigorous quality assurance is required to validate dose delivery both before and during the course of treatment. Active motion management (eg, breath hold), beam gating, rescanning, tracking, or adaptive planning may be needed for cases involving significant motion or changes in motion or anatomy over the course of treatment.

A proton therapy workflow based on CBCT provided clinical indicators similar to those using rCT for patients with lung cancer with considerable anatomic changes.

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This article describes the design, construction, and properties of an anthropomorphic thorax phantom with a moving surrogate tumor. This novel phantom permits detection of dose both inside and outside a moving tumor and within the substitute lung tissue material. A 3D printer generated the thorax shell composed of a chest wall, spinal column, and posterior regions of the phantom. Images of a computed tomography scan of the thorax from a patient with lung cancer provided the template for the 3D printing. The plastic phantom is segmented into two materials representing the muscle and bones, and its geometry closely matches a patient. A surrogate spherical plastic tumor controlled by a 3D linear stage simulates a lung tumor's trajectory during normal breathing. Sawdust emulates the lung tissue in terms of average and distribution in Hounsfield numbers. The sawdust also provides a forgiving medium that permits tumor motion and sandwiching of radiochromic film inside the mobile surrogate plastic tumor for dosimetry. A custom cork casing shields the film and tumor and eliminates film bending during extended scans. The phantom, lung tissue surrogate, and radiochromic film are exposed to a seven field plan based on an ECLIPSE plan for 6 MV photons from a Trilogy machine delivering 230 cGy to the isocenter. The dose collected in a sagittal plane is compared to the calculated plan. Gamma analysis finds 8.8% and 5.5% gamma failure rates for measurements of large amplitude trajectory and static measurements relative to the large amplitude plan, respectively. These particular gamma analysis results were achieved using parameters of 3% dose and 3 mm, for regions receiving doses >150 cGy. The plan assumes a stationary detection grid unlike the moving radiochromic film and tissues. This difference was experimentally observed and motivated calculated dose distributions that incorporated the phase of the tumor periodic motion. These calculations modestly improve agreement between the measured and intended doses.

Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-only treatment process is currently an active field of research since it could eliminate systematic MR-CT co-registration errors, reduce medical cost, avoid diagnostic radiation exposure, and simplify clinical workflow. The purpose of this work is to validate the application of a deep learning-based method for abdominal synthetic CT (sCT) generation by image evaluation and dosimetric assessment in a commercial proton pencil beam treatment planning system (TPS). This study proposes to integrate dense block into a 3D cycle-consistent generative adversarial networks (cycle GAN) framework in an effort to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 21 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT image quality by leave-one-out cross validation. The CT image quality, dosimetric accuracy and the distal range fidelity were rigorously checked, using side-by-side comparison against the corresponding original CT images. The average mean absolute error (MAE) was 72.87±18.16 HU. The relative differences of the statistics of the PTV dose volume histogram (DVH) metrics between sCT and CT were generally less than 1%. Mean 3D gamma analysis passing rate of 1mm/1%, 2mm/2%, 3mm/3% criteria with 10% dose threshold were 90.76±5.94%, 96.98±2.93% and 99.37±0.99%, respectively. The median, mean and standard deviation of absolute maximum range differences were 0.170 cm, 0.186 cm and 0.155 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for liver proton radiotherapy.