A proof-of-concept study of an in-situ partial-ring time-of-flight PET scanner for proton beam verification.

Development of a PET system capable of in-situ imaging requires a design that can accommodate the proton treatment beam nozzle. Among the several PET instrumentation approaches developed thus far, the dual-panel PET scanner is often used as it is simpler to develop and integrate within the proton therapy gantry. Partial-angle coverage of these systems can however lead to limited-angle artefacts in the reconstructed PET image. We have previously demonstrated via simulations that time-of-flight (TOF) reconstruction reduces the artifacts accompanying limited-angle data, and permits proton range measurement with 1-2 mm accuracy and precision. In this work we show measured results from a small proof-of-concept dual-panel PET system that uses TOF information to reconstruct PET data acquired after proton irradiation. The PET scanner comprises of two detector modules, each comprised of an array of 4×4×30 mm3 lanthanum bromide scintillator. Measurements are performed with an oxygen-rich gel-water, an adipose tissue equivalent material, and in vitro tissue phantoms. For each phantom measurement, 2 Gy dose was deposited using 54 - 100 MeV proton beams. For each phantom, a Monte Carlo simulation generating the expected distribution of PET isotope from the corresponding proton irradiation was also performed. Proton range was calculated by drawing multiple depth-profiles over a central region encompassing the proton dose deposition. For each profile, proton range was calculated using two techniques (a) 50% pick-off from the distal edge of the profile, and (b) comparing the measured and Monte Carlo profile to minimize the absolute sum of differences over the entire profile. A 10 min PET acquisition acquired with minimal delay post proton-irradiation is compared with a 10 min PET scan acquired after a 20 min delay. Measurements show that PET acquisition with minimal delay is necessary to collect 15O signal, and maximize 11C signal collection with a short PET acquisition. In comparison with the 50% pick-off technique, the shift technique is more robust and offers better precision in measuring the proton range for the different phantoms. Range measurements from PET images acquired with minimal delay, and the shift technique demonstrate the ability to achieve <1.5 mm accuracy and precision in estimating proton range.

Per-fraction positional and dosimetric performance of prone breast tangential radiotherapy on Halcyon™ linear accelerator assessed with daily rapid kilo-voltage cone beam computed tomography: a single-institution pilot study.

BACKGROUND: This study investigates daily breast geometry and delivered dose to prone-positioned patients undergoing tangential whole breast radiation therapy (WBRT) on an O-ring linear accelerator with 6X flattening filter free mode (6X-FFF), planned with electronic compensation (ECOMP) method. Most practices rely on skin marks or daily planar image matching for prone breast WBRT. This system provides low dose daily CBCT, which was used to study daily robustness of delivered dose parameters for prone-positioned WBRT. METHODS: Eight patients treated with 16-fraction prone-breast WBRT were retrospectively studied. Planning CTs were deformed to daily CBCT to generate daily synthetic CTs, on which delivered dose distributions were calculated. A total of 8 × 16 = 128 synthetic CTs were generated. Consensus ASTRO definition was used to contour Breast PTV Eval for each daily deformed CT. Breast PTV Eval coverage (V90%) and hotspot (V105% and Dmax) were monitored daily to compare prescription dose with daily delivered dose. Various predictors including patient weight, breast width diameter (BWD), and Dice similarity coefficient (DSC) were fit into an analysis of covariance model predicting V90% and V105% deviation from prescribed (ΔV90%, ΔV105%). Statistical significance is indicated with asterisks (* for p < 0.05; ** for p < 0.001). RESULTS: Daily delivered Breast PTV Eval V90% was moderately smaller than prescribed (median ΔV90% = - 0.1%*), while V105% was much larger (median ΔV105% = + 10.1%** or + 92.4 cc**). Patient's weight loss correlated with significantly increased ΔV105% (+ 4.6%/ - 1% weight, R2 = 0.4**) and moderately decreased ΔV90% (- 0.071%/ - 1% wt., R2 = 0.2**). Comprehensive ANCOVA models indicated three factors affect ΔV90% and ΔV105% the most: (1) BWD decrease (- 0.09%* and + 10%**/ - 1 cm respectively), (2) PTV Eval volume decrease (- 0.4%** and + 9%**/ - 100 cc), and for ΔV105% only, (3) the extent of breast deformation (+ 10%**/ - 0.01 DSC). Breast PTV Eval volume also decreased with time (- 2.21*cc/fx), possibly indicating seroma resolution and increase in V105% over time. CONCLUSIONS: Daily CBCT revealed key delivered dose parameters vary significantly for patients undergoing tangential prone breast WBRT planned with ECOMP using 6X-FFF. Patient weight, BWD, and breast shape deformation could be used to predict dosimetric variations from prescribed. Preliminary findings suggest an adaptive plan based on daily CBCT could reduce excessive dose to the breast.

A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.

In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In current practice, patients with tumor motion may be required to receive two sets of CT scans - the initial free-breathing 4-dimensional CT (4DCT) scan for tumor motion estimation and a second CT scan under appropriate motion management such as breath-hold or abdominal compression. The aim of this study is to assess the feasibility of a predictive model for tumor motion estimation in three-dimensional space based on machine learning algorithms. The model was developed based on sixteen imaging features extracted from non-4D diagnostic CT images and eleven clinical features extracted from the Electronic Health Record (EHR) database of 150 patients to characterize the lung tumor motion. A super-learner model was trained to combine four base machine learning models including the Random Forest, Multi-Layer Perceptron, LightGBM and XGBoost, the hyper-parameters of which were also optimized to obtain the best performance. The outputs of the super-learner model consist of tumor motion predictions in the Superior-Inferior (SI), Anterior-Posterior (AP) and Left-Right (LR) directions, and were compared against tumor motions measured in the free-breathing 4DCT scans. The accuracy of predictions was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) through ten rounds of independent tests. The MAE and RMSE of predictions in the SI direction were 1.23 mm and 1.70 mm; the MAE and RMSE of predictions in the AP direction were 0.81 mm and 1.19 mm, and the MAE and RMSE of predictions in the LR direction were 0.70 mm and 0.95 mm. In addition, the relative feature importance analysis demonstrated that the imaging features are of great importance in the tumor motion prediction compared to the clinical features. Our findings indicate that a super-learner model can accurately predict tumor motion ranges as measured in the 4DCT, and could provide a machine learning framework to assist radiation oncologists in determining the active motion management strategy for patients with large tumor motion.

NCTN clinical trial standardization for radiotherapy through IROC and CIRO.

Evidence-based practice is the cornerstone of modern medicine. Randomized clinical trials across multiple institutions are the gold standard for modern evidence collection. National Cancer Trials Network (NCTN) instruments the clinical trials through the new infrastructure for improvements in cancer treatment. Radiation therapy is an integral component of cancer treatment and is involved in many of the NCTN clinical trials. Radiotherapy is experiencing exciting developments in new treatment modalities and multi-modality image guidance. One of NCTN network groups NRG Oncology brings together the research areas of the National Surgical Adjuvant Breast and Bowel Project (NSABP), the Radiation Therapy Oncology Group (RTOG), and the Gynecologic Oncology Group (GOG). The Imaging and Radiation Oncology Core (IROC) and Center for Innovation in Radiation Oncology(CIRO) of NRG Oncology complement each other's functions in development and implementation of the new radiotherapy and imaging technologies in clinical trials with standardization and other strategies for quality. The standardization process is the essential step to make the data collected for clinical trials of high quality, interoperable, and reusable.

Prompt Gamma Imaging for In Vivo Range Verification of Pencil Beam Scanning Proton Therapy.

PURPOSE: To report the first clinical results and value assessment of prompt gamma imaging for in vivo proton range verification in pencil beam scanning mode. METHODS AND MATERIALS: A stand-alone, trolley-mounted, prototype prompt gamma camera utilizing a knife-edge slit collimator design was used to record the prompt gamma signal emitted along the proton tracks during delivery of proton therapy for a brain cancer patient. The recorded prompt gamma depth detection profiles of individual pencil beam spots were compared with the expected profiles simulated from the treatment plan. RESULTS: In 6 treatment fractions recorded over 3 weeks, the mean (± standard deviation) range shifts aggregated over all spots in 9 energy layers were -0.8 ± 1.3 mm for the lateral field, 1.7 ± 0.7 mm for the right-superior-oblique field, and -0.4 ± 0.9 mm for the vertex field. CONCLUSIONS: This study demonstrates the feasibility and illustrates the distinctive benefits of prompt gamma imaging in pencil beam scanning treatment mode. Accuracy in range verification was found in this first clinical case to be better than the range uncertainty margin applied in the treatment plan. These first results lay the foundation for additional work toward tighter integration of the system for in vivo proton range verification and quantification of range uncertainties.

Effects of full-neck volumetric-modulated arc therapy vs split-field intensity-modulated head and neck radiation therapy on low neck targets and structures.

OBJECTIVE: While split-field intensity-modulated radiation therapy (SF-IMRT) decreases dose to low neck (LAN) structures such as the glottic larynx compared with full-neck intensity-modulated radiation therapy (IMRT), it is unknown whether SF-IMRT affords superior dose avoidance to organs than whole neck-field volumetric-modulated arc therapy (WF-VMAT). METHODS: 10 patients treated definitively with radiation for oropharyngeal, oral cavity or nasopharyngeal carcinoma were compared. Only patients ideally suited for SF-IMRT plans were included. The glottic larynx, supraglottic larynx, arytenoids, pharyngeal constrictors, oesophagus, brachial plexus and target volume coverage in the LAN were compared between WF-VMAT and SF-IMRT. RESULTS: Volumetric-modulated arc therapy (VMAT) yielded statistically significant decreases in maximum dose to the arytenoids and mean dose to the oesophagus. There was no difference in dose to the glottic larynx, supraglottic larynx, pharyngeal constrictors and brachial plexus. WF-VMAT led to improved coverage to 50/2 Gy fraction equivalent in LAN compared with SF-IMRT using an anteroposterior (AP) LAN field but no difference to the 60/2 Gy fraction equivalent between SF-IMRT and WF-VMAT using AP/posterior-anterior LAN boost. CONCLUSION: WF-VMAT affords equivalent glottic and supraglottic larynx dose and lower dose to the arytenoids and oesophagus. WF-VMAT better covers most LAN target structures. Given these findings as well as concerns with matchline cold spots or hotspots with SF-IMRT, patients requiring comprehensive elective nodal irradiation should typically be treated with WF-VMAT. ADVANCES IN KNOWLEDGE: SF-IMRT for larynx sparing has better dosimetric results to normal structures than whole-neck IMRT, but with increased matchline recurrence risk. We show dosimetric equivalence or superiority of WF-VMAT compared with SF-IMRT.