We developed an integrated framework that employs a full Monte Carlo (MC) model for treatment-plan simulations of a passive double-scattering proton system.We have previously validated a virtual machine source model for full MC proton-dose calculations by comparing the percentage of depth-dose curves, spread-out Bragg peaks, and lateral profiles against measured commissioning data. This study further expanded our previous work by developing an integrate framework that facilitates its clinical use. Specifically, we have (1) constructed patient-specific applicator and compensator numerically from the plan data and incorporated them into the beamline, (2) created the patient anatomy from the computed tomography image and established the transformation between patient and machine coordinate systems, and (3) developed a graphical user interface to ease the whole process from importing the treatment plan in the Digital Imaging and Communications in Medicine format to parallelization of the MC calculations. End-to-end tests were performed to validate the functionality, and 3 clinical cases were used to demonstrate clinical utility of the framework.The end-to-end tests demonstrated that the framework functioned correctly for all tested functionality. Comparisons between the treatment planning system calculations and MC results in 3 clinical cases revealed large dose difference up to 17%, especially in the beam penumbra and near the end of beam range. The discrepancy likely originates from a variety of sources, such as the dose algorithms, modeling of the beamline, and the dose metric. The agreement for other regions was acceptable.An integrated framework was developed for full MC simulations of double-scattering proton therapy. It can be a valuable tool for dose verification and plan evaluation.
e12567 Background: Radiation-induced lymphopenia accompanied with radiation therapy is associated with inferior clinical outcomes in a wide variety of solid malignancies. This study aimed to examine the potential determines of radiation-induced lymphocyte decrease and radiation-induced lymphopenia in breast cancer patients who underwent radiotherapy. Methods: Patients with breast cancer treated who underwent radiotherapy were enrolled in University of Hong Kong-Shenzhen Hospital (our cohort). Circulating lymphocyte levels were evaluated within 7 days prior to and end of radiation therapy. Feature groups including clinical data, tumor characteristics, radiotherapy dosimetrics, treatment regiments were also collected. We applied machine learning algorithms (Extreme Gradient Boosting, XGboost) to predict the ratio of lymphocyte level after radiotherapy to baseline lymphocyte level and the event of lymphopenia and compared with Lasso regression approaches. Next, we used Shapley additive explanation (SHAP) to explore the directional contribution of each feature for lymphocyte decrease and lymphopenia. For the purpose of model validation and proof-of-concept validation, an independent cohort of patients enrolled in prospective trial was eligible (IP cohort). Results: A total of 589 patients were enrolled in our cohort and 203 patients in IP cohort. XGboost models which trained in our cohort with performances of a mean RMSE: 0.157 and R2: 53.9% for the ratio of lymphocyte levels; a mean accuracy: 0.757 and ROC-AUC: 0.733 for the lymphopenia events, separately. These models can predict the ratio of lymphocyte levels with a mean RMSE: 0.175 and R2: 47%; predict the lymphopenia events with a mean accuracy: 0.739 and ROC-AUC: 0.737 in the totally independent IP cohort. The feature group of dosimetrics had the largest predictive power with RMSE: 0.192, R2: 29.8%, accuracy: 0.678 and ROC-AUC: 0.667; followed by the group of baseline blood cells with predictive power as RMSE: 0.207, R2: 18.9%, accuracy: 0.669 and ROC-AUC: 0.645. Next, by SHAP value analysis, we investigated that integral dose of the total body, V5 dose, mean lung dose and V20 dose of ipsilateral lung/bilateral lungs were in consequence important promote factors for lymphocyte decrease and for the event of lymphopenia, while the features of baseline monocyte, mean heart dose and tumor size played a role of protection at some extend. Conclusions: In this study, we constructed robust XGboost models for predicting the lymphocyte decrease and the event of lymphopenia in breast cancer patients who underwent radiation therapy. We also applied SHAP analysis for revealing the directional contribution of features. These results are important either for the understanding the contributions of dosimetrics on immune response or for the refine of radiation dosimetrics before treatment in future clinical usages.
Abstract Purpose: Radiation pneumonitis is an important adverse event in patients with non–small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2. Experimental Design: Levels of 30 inflammatory cytokines and clinical information in patients with stages I–III NSCLC treated with radiotherapy were from our prospective studies. Statistical analysis was used to select predictive cytokine candidates and clinical covariates for adjustment. Machine learning algorithm was used to develop the generalized linear model for predicting risk RP2. Results: A total of 131 patients were eligible and 17 (13.0%) developed RP2. IL8 and CCL2 had significantly (Bonferroni) lower expression levels in patients with RP2 than without RP2. But none of the changes in cytokine levels during radiotherapy was significantly associated with RP2. The final predictive GLM model for RP2 was established, including IL8 and CCL2 at baseline level and two clinical variables. Nomogram was constructed based on the GLM model. The model's predicting ability was validated in the completely independent test set (AUC = 0.863, accuracy = 80.0%, sensitivity = 100%, specificity = 76.5%). Conclusions: By machine learning, this study has developed and validated a comprehensive model integrating inflammatory cytokines with clinical variables to predict RP2 before radiotherapy that provides an opportunity to guide clinicians.
3110 Background: A majority of patients with advanced cancer expire from uncontrolled disseminated disease due to lack of effective systemic treatment. Radiation (RT) serves a limited role as palliative therapy. Recently, a unique synergy between RT and immunotherapeutics (IT) has been noted. This study assesses currently published clinical data combining RT and IT (cRIT) to exam the effect of such combination treatment on patient survival and toxicity. Methods: Eligible studies were found through PubMed. Keywords included radiation, radiotherapy, metastasis, advanced cancer, immunotherapy, interleukin, interferon, dendritic cells, and NK cells. Criteria included clinical studies combining any RT and IT in patients with diagnosed advanced disease published since Jan 1, 1999, excluding studies that also combined chemotherapy. Patient survival and toxicity data was captured and analyzed. The primary endpoints included treatment response rates and median survivals. Results: Eleven studies were eligible with a total of 525 patients. Of those patients, 397 received cRIT; 110 and 41 patients received RT or IT alone, respectively. RT methods included conventional radiation, intensely modulated radiation therapy, and stereotactic radiotherapy. IT agents included interleukin 2, interferon α/β, thalidomide, and dendritic cells. Pooled data from seven studies (309 patients) reported response rates of complete response as 21.4% (95% CI 0-49.4%), partial response as 19.5% (95% CI 0-39.9%), stable disease as 19.7% (95% CI 1.7-37.6%), and progressive disease as 30.0% (95% CI 10.7-49.4%). The disease-free survival in cRIT patients was 18.9 (90% CI 1.8-35.9) months and overall survival was 21.7 (95% CI 6.0-37.3) months. Most common toxicities included skin reactions (49.5%), esophagitis (81.2%), anemia (21.8%), anorexia (21.2%), and liver dysfunction (19.1%). Pooled data does not show any significant increase in toxicity level with combined therapy. Conclusions: Based on this limited pooled analysis, cRIT appeared to be effective and safe in certain patients with advanced cancers. Randomized trials are needed to further assess the value of this new treatment modality and the best combination of RT and IT.
Purpose: The accuracy of second generation EBT film (EBT2) in commissioning of small fields for stereotactic radiosurgery (SRS) has yet to be demonstrated. We evaluate EBT2 films against a diode in a set of small circular fields collimated by cones. We investigate the challenges of EBT2 films for small field dosimetry and evaluate its accuracy as an absolute or relative dosimeter against diode measurements. Methods: Output factors (OFs), central‐axis percentage depth dose curves (PDDs) and cross‐beam profiles from a set of 9 Brainlab® SRS cones, with diameters ranging from 5 to 30 mm, were measured with a Scanditronix stereostatic diode(SFD) on a Varian Trilogy Linac. Measurements were repeated using EBT2 films in a Solid Water phantom. Blue‐color channel correction, as recommended by the vendor, was also investigated for reducing the effect of film inhomogeneity. Results: The OFs with EBT2 and diode agree within 1.1%, on average, for cone sizes 10 mm and above. SFD measures higher OFs than EBT2 for cones smaller than 10mm, likely due to the water nonequivalence of the silicon material in the diode detector which effectively reduces lateral electronic disequilibrium in small fields. Profiles and PDDs between EBT2 and diode match closely with each other. Film measurements were sometimes irregular due to non‐homogeneous film response. Blue‐color channel correction did not mitigate the film inhomogeneity. Conclusions: This study demonstrates that EBT2 films can be applied as absolute and relative dosimeters for measuring dose in small fields. Because EBT2 are more water‐equivalent than diode, it is likely that OFs measured by EBT2 in cones smaller than 10 mm are closer to truth. Inhomogeneity of the EBT2 films needs to be carefully evaluated before clinical use and the blue‐color channel correction is not recommended.
Purpose: Cone beam computed tomography (CBCT) provides wide scan coverage per rotation; however, its image quality is compromised due to large amounts of scatter. In this study, we performed detailed Monte Carlo (MC) simulations of a CBCT flat panel detector to characterize scatter for the purposes of scatter correction. Methods: An amorphous silicon (aSi) flat panel detector (Varian Medical Systems) of an On-Board Imager (Varian Medical Systems) was modeled using BEAMnrc/EGSnrc code system based on detailed geometric information provided by the manufacturer. Layers from the proximal Al cover to the light reflector encompassing the 10:1 anti-scatter grid were simulated using the block component module (BLOCK_CM) in BEAMnrc. Layers from the cesium iodide (CsI) detector to the proximal Pb electronics protection cover were modeled in DOSXYZnrc to create a voxelized representation of the detector layer. Various scatter properties were elicited from phase space files within the grid and detector layers. A two-dimensional (2-D) cone-beam image “in-air” (without the phantom) was acquired (125 kVp, 80 mAs). 2-D and 1-D pixel intensities were compared to the simulated projection to verify the accuracy of MC simulation of the entire detector system. Results: 2-D pixel intensities of the computed image agreed well to the measured image as the difference map showed values within +−10%. However, given the large number of histories required for detector simulation, the MC uncertainty was quite high, up to 10% in some regions. The central axis profile also showed good agreement between simulation and measurement within 3% on average, except in the regions where the statistical uncertainty was larger (1sigma=7.5%). Conclusions: A CBCT imager has been modeled in detail using the BEAMrnc/EGSnrc code system. Additional verification of the MC-based modeling is warranted to for the purposes of scatter characterization.
