A workflow for screening patients' motion characteristics and optimizing beam angle selection was established for the pencil beam scanning proton therapy treatment of liver tumors. Abdominal compression was found to be useful at mitigation of moderate and large motion.

Abstract:Cancer is a disease of unregulated cell growth that is estimated to kill over 600,000 people in the United States in 2017 according to the National Institute of Health. While there are several therapies to treat cancer, tumor resistance to these therapies is a concern. Drug therapies have been developed that attack proliferating endothelial cells instead of the tumor in an attempt to create a therapy that is resistant to resistance in contrast to other forms of treatment such as chemotherapy and radiation therapy. In this study, a two-compartment model in terms of differential equations is presented in order to determine the optimal protocol for the delivery of anti-angiogenesis therapy. Optimal control theory is applied to the model with a range of anti-angiogenesis doses to determine optimal doses to minimize tumor volume at the end of a two week treatment and minimize drug toxicity to the patient. Applying a continuous optimal control protocol to our model of angiogenesis and tumor cell growth shows promising results for tumor control while minimizing the toxicity to the patients. By investigating a variety of doses, we determine that the optimal angiogenesis inhibitor dose is in the range of 10-20 mg/kg. In this clinically useful range of doses, good tumor control is achieved for a two week treatment period. This work shows that varying the toxicity of the treatment to the patient will change the optimal dosing scheme but tumor control can still be achieved.

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Purpose: To study if abdominal compression can reduce breathing motion and mitigate interplay effect in pencil beam scanning proton therapy (PBSPT) treatment of liver tumors in order to better spare healthy liver volumes compared with photon therapy. Methods: Ten patients, six having large tumors initially treated with IMRT and four having small tumors treated with SBRT, were replanned for PBSPT. ITV and beam‐specific PTVs based on 4D‐CT were used to ensure target coverage in PBSPT. The use of an abdominal compression belt and volumetric repainting was investigated to mitigate the interplay effect between breathing motion and PBSPT dynamic delivery. An in‐house Matlab script has been developed to simulate this interplay effect. The dose is computed on each phase individually by sorting all spots according to their simulated delivery timing. The final dose distribution is then obtained by accumulating all dose maps to a reference phase. Results: For equivalent target coverage PBSPT reduced average healthy liver dose by 9.5% of the prescription dose compared with IMRT/SBRT. Abdominal compression of 113.2±42.2 mmHg was effective for all 10 patients and reduced average motion by 2.25 mm. As a result, the average ITV volume decreased from 128.2% to 123.1% of CTV volume. Similarly, the average beam‐specific PTV volume decreased from 193.2% to 183.3%. For 8 of the 10 patients, the average motion was reduced below 5 mm, and up to 3 repainting were sufficient to mitigate interplay. For the other two patients with larger residual motion, 4–5 repainting were needed. Conclusion: We recommend evaluation of the 4DCT motion histogram following simulation and the interplay effect following treatment planning in order to personalize the use of compression and volumetric repainting for each patient. Abdominal compression enables safe and more effective PBS treatment of liver tumors by reduction of motion and interplay effect. Kevin Souris is supported by IBA and Televie Grant from F.R.S.‐FNRS. Liyong Lin is partially supported by Varian.

Purpose/Objective(s): To investigate whether CBCT and CT can be used simultaneously in radiomics analysis. To establish a batch correction method for radiomics in 2 similar image modalities. Materials/Methods: Four sites including rectum, bladder, femoral head, and lung were considered as regions of interest in this study. For each site, 10 treatment planning CT images were collected, and 10 CBCT images that came from the same site of the same patient were acquired at first radiation therapy fraction. A total of 253 radiomics features, which were selected by our test-retest study at rectum cancer CT (ICC>0.8), were calculated for both CBCT and CT images using a computer algorithm. Simple scaling (z-score) and nonlinear correction methods were applied to the CBCT radiomics features. The Pearson correlation coefficient was calculated to analyze the correlation between radiomics features of CBCT and CT images before and after correction. Cluster analysis of mixed data (for each site, 5 CT, and 5 CBCT data are randomly selected) was implemented to validate the feasibility to merge radiomics data from CBCT and CT. The consistency of clustering result and site grouping was verified by a chi-square test for different datasets, respectively.Results: For simple scaling, 234 of the 253 features have r>0.8 (P<0.05), among which 154 features have r>0.9 (P<0.05). For radiomics data after nonlinear correction, 240 of the 253 features have r>0.8 (P<0.05), among which 220 features have r>0.9 (P<0.05). Cluster analysis of mixed data shows that data of 4 sites was almost precisely separated for simple scaling (PZ5.41Â10 -7 , c 2 test) and nonlinear correction (PZ3.25Â10 -7 , c 2 test), which is similar to the cluster result of CT data (PZ7.19Â10 -8 , c 2 test).