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PurposeThis study was conducted to compare clinical outcomes and treatment-related toxicities after stereotactic body radiation therapy (SBRT) with two different dose regimens for small hepatocellular carcinomas (HCC) ≤3 cm in size.Materials and MethodsWe retrospectively reviewed 44 patients with liver-confined HCC treated between 2009 and 2014 with SBRT. Total doses of 45 Gy (n = 10) or 60 Gy (n = 34) in 3 fractions were prescribed to the 95% isodose line covering 95% of the planning target volume. Rates of local control (LC), intrahepatic failure-free survival (IHFFS), distant metastasis-free survival (DMFS), and overall survival (OS) were calculated using the Kaplan-Meier method.ResultsMedian follow-up was 29 months (range, 8 to 64 months). Rates at 1 and 3 years were 97.7% and 95.0% for LC, 97.7% and 80.7% for OS, 76% and 40.5% for IHFFS, and 87.3% and 79.5% for DMFS. Five patients (11.4%) experienced degradation of albumin-bilirubin grade, 2 (4.5%) degradation of Child-Pugh score, and 4 (9.1%) grade 3 or greater laboratory abnormalities within 3 months after SBRT. No significant difference was seen in any oncological outcomes or treatment-related toxicities between the two dose regimens.conclusionsSBRT was highly effective for local control without severe toxicities in patients with HCC smaller than 3 cm. The regimen of a total dose of 45 Gy in 3 fractions was comparable to 60 Gy in efficacy and safety of SBRT for small HCC.

PurposeThe purpose of this report is to describe the proton therapy system at Samsung Medical Center (SMC-PTS) including the proton beam generator, irradiation system, patient positioning system, patient position verification system, respiratory gating system, and operating and safety control system, and review the current status of the SMC-PTS.Materials and MethodsThe SMC-PTS has a cyclotron (230 MeV) and two treatment rooms: one treatment room is equipped with a multi-purpose nozzle and the other treatment room is equipped with a dedicated pencil beam scanning nozzle. The proton beam generator including the cyclotron and the energy selection system can lower the energy of protons down to 70 MeV from the maximum 230 MeV.ResultsThe multi-purpose nozzle can deliver both wobbling proton beam and active scanning proton beam, and a multi-leaf collimator has been installed in the downstream of the nozzle. The dedicated scanning nozzle can deliver active scanning proton beam with a helium gas filled pipe minimizing unnecessary interactions with the air in the beam path. The equipment was provided by Sumitomo Heavy Industries Ltd., RayStation from RaySearch Laboratories AB is the selected treatment planning system, and data management will be handled by the MOSAIQ system from Elekta AB.ConclusionThe SMC-PTS located in Seoul, Korea, is scheduled to begin treating cancer patients in 2015.

PurposeThis study aimed to evaluate the initial outcomes of proton beam therapy (PBT) for hepatocellular carcinoma (HCC) in terms of tumor response and safety.Materials and MethodsHCC patients who were not indicated for standard curative local modalities and who were treated with PBT at Samsung Medical Center from January 2016 to February 2017 were enrolled. Toxicity was scored using the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Tumor response was evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST).ResultsA total of 101 HCC patients treated with PBT were included. Patients were treated with an equivalent dose of 62–92 GyE10. Liver function status was not significantly affected after PBT. Greater than 80% of patients had Child-Pugh class A and albumin-bilirubin (ALBI) grade 1 up to 3-months after PBT. Of 78 patients followed for three months after PBT, infield complete and partial responses were achieved in 54 (69.2%) and 14 (17.9%) patients, respectively.ConclusionPBT treatment of HCC patients showed a favorable infield complete response rate of 69.2% with acceptable acute toxicity. An additional follow-up study of these patients will be conducted.

For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition). The accuracy measures are: (1) root mean square error (RMSE) and Pearson correlation coefficient (CC), (2) accuracy dependency on Ts and Pt, (3) respiratory pattern dependency, and (4) error for 30% and 70% of the respiration gating for a 5 mm tumor motion for latencies of 300, 500, and 700 ms. Under standard conditions, the Transformer model exhibits the highest accuracy with an RMSE and CC of 0.1554 and 0.9768, respectively. An increase in Ts improves accuracy, whereas an increase in Pt decreases accuracy. An evaluation of the regularity of the respiratory signals reveals that the lowest predictive accuracy is achieved with irregular amplitude patterns. For 30% and 70% of the phases, the average error of the three models is <1.4 mm for a latency of 500 ms and >2.0 mm for a latency of 700 ms. The prediction accuracy of the Transformer is superior to LSTM and Bi-LSTM. Thus, the three models have clinically applicable accuracies for a latency <500 ms for 10 mm of regular tumor motion. The clinical acceptability of the deep learning models depends on the inherent latency and the strategy for reducing the irregularity of respiration.

Purpose The purpose of this study was to investigate the feasibility of two‐dimensional (2D) dose distribution deconvolution using convolutional neural networks (CNNs) instead of an analytical approach for an in‐house scintillation detector that has a detector‐interface artifact in the penumbra region. Methods Datasets of 2D dose distributions were acquired from a medical linear accelerator of Novalis Tx. The datasets comprise two different sizes of square radiation fields and 13 clinical intensity‐modulated radiation treatment (IMRT) plans. These datasets were divided into two datasets (training and test) to train and validate the developed network, called PenumbraNet, which is a shallow linear CNN. The PenumbraNet was trained to transform the measured dose distribution [M(x, y)] to calculated distribution [D(x, y)] by the treatment planning system. After training of the PenumbraNet was completed, the performance was evaluated using test data, which were 10 × 10 cm2 open field and ten clinical IMRT cases. The corrected dose distribution [C(x, y)] was evaluated against D(x, y) with 2%/2 mm and 3%/3 mm criteria of the gamma index for each field. The M(x, y) and deconvolved dose distribution with the analytically obtained kernel using Wiener filtering [A(x, y)] were also evaluated for comparison. In addition, we compared the performance of the shallow depth of linear PenumbraNet with that of nonlinear PenumbraNet and a deep nonlinear PenumbraNet within the same training epoch. Results The mean gamma passing rates were 84.77% and 95.81% with 3%/3 mm gamma criteria for A(x, y) and C(x, y) of the PenumbraNet, respectively. The mean gamma pass rates of nonlinear PenumbraNet and the deep depth of nonlinear PenumbraNet were 96.62%, 93.42% with 3%/3 mm gamma criteria, respectively. Conclusions We demonstrated the feasibility of the PenumbraNets for 2D dose distribution deconvolution. The nonlinear PenumbraNet which has the best performance improved the gamma passing rate by 11.85% from the M(x, y) at 3%/3 mm gamma criteria.

In particle radiotherapy, range uncertainty is an important issue that needs to be overcome. Because high-dose conformality can be achieved using a particle beam, a small uncertainty can affect tumor control or cause normal-tissue complications. From this perspective, the treatment planning system (TPS) must be accurate. However, there is a well-known inaccuracy regarding dose computation in heterogeneous media. This means that verifying the uncertainty level is one of the prerequisites for TPS commissioning. We evaluated the range accuracy of the dose computation algorithm implemented in a commercial TPS, and Monte Carlo (MC) simulation against measurement using a CT calibration phantom. A treatment plan was produced for eight different materials plugged into a phantom, and two-dimensional doses were measured using a chamber array. The measurement setup and beam delivery were simulated by MC code. For an infinite solid water phantom, the gamma passing rate between the measurement and TPS was 97.7%, and that between the measurement and MC was 96.5%. However, gamma passing rates between the measurement and TPS were 49.4% for the lung and 67.8% for bone, and between the measurement and MC were 85.6% for the lung and 100.0% for bone tissue. For adipose, breast, brain, liver, and bone mineral, the gamma passing rates computed by TPS were 91.7%, 90.6%, 81.7%, 85.6%, and 85.6%, respectively. The gamma passing rates for MC for adipose, breast, brain, liver, and bone mineral were 100.0%, 97.2%, 95.0%, 98.9%, and 97.8%, respectively. In conclusion, the described procedure successfully evaluated the allowable range uncertainty for TPS commissioning. The TPS dose calculation is inefficient in heterogeneous media with large differences in density, such as lung or bone tissue. Therefore, the limitations of TPS in heterogeneous media should be understood and applied in clinical practice.

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