Liang, Xiaoying

AbstractBackgroundKilo‐voltage cone‐beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely‐used Feldkamp‐Davis‐Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary‐based methods, and prior information‐based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART.PurposeOne of the primary challenges in IR‐based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high‐resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels.MethodsThe HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp‐Davis‐Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process.ResultsWe evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms.ConclusionThe proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high‐definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.

Pencil beam scanning delivered with continuous scanning has several advantages over conventional discrete spot scanning. Such advantages include improved beam delivery efficiency and reduced beam delivery time. However, a move dose is delivered between consecutive spots with continuous scanning, and current treatment planning systems do not take this into account. Therefore, continuous scanning and discrete spot plans have an inherent dose discrepancy. Using the operating parameters of the state-of-the-art particle therapy system, we conducted a proof-of-concept study in which we systematically generated 28 plans for cubic targets with different combinations of plan parameters and simulated the dose discrepancies between continuous scanning and a planned one. A nomograph to guide the selection of plan parameters was developed to reduce the dose discrepancy. The effectiveness of the nomograph was evaluated with two clinical cases (one prostate and one liver). Plans with parameters guided by the nomograph decreased dose discrepancy than those used standard plan parameters. Specifically, the 2%/2 mm gamma passing rate increased from 96.3% to 100% for the prostate case and from 97.8% to 99.7% for the liver case. The CTV DVH root mean square error decreased from 2.2% to 0.2% for the prostate case and from 1.8% to 0.9% for the liver case. The decreased dose discrepancy may allow the relaxing of the delivery constraint for some cases, leading to greater benefits in continuous scanning. Further investigation is warranted.

AbstractBackgroundDiscrete spot scanning (DSS) is the commonly used method for proton pencil beam scanning (PBS). There is lack of data on the dose‐driven continuous scanning (DDCS).PurposeTo investigate delivery benefits and dosimetric implications of DDCS versus DSS for PBS systems.MethodsThe irradiation duty factor, beam delivery time (BDT), and dose deviation were simulated for eight treatment plans in prostate, head and neck, liver, and lung, with both conventional fractionation and hypofractionation schemes. DDCS results were compared with those of DSS.ResultsThe DDCS irradiation duty factor (range, 11%–41%) was appreciably improved compared to DSS delivery (range, 4%–14%), within which, hypofractionation schemes had greater improvement than conventional fractionation. With decreasing stop ratio constraints, the DDCS BDT reduction was greater, but dose deviation also increased. With stop ratio constraints of 2, 1, 0.5, and 0, DDCS BDT reduction reached to 6%, 10%, 12%, and 15%, respectively, and dose deviation reached to 0.6%, 1.7%, 3.0%, and 5.2% root mean square error in PTV DVH, respectively. The 3%/2‐mm gamma passing rate was greater than 99% with stop ratio constraints of 2 and 1, and greater than 95% with a stop ratio of 0.5. When the stop ratio constraint was removed, five of the eight treatment plans had a 3%/2‐mm gamma passing rate greater than 95%, and the other three plans had a 3%/2‐mm gamma passing rate between 90% and 95%.ConclusionsThe irradiation duty factor was considerably improved with DDCS. Smaller stop ratio constraints led to shorter BDTs, but with the cost of larger dose deviations. Our finding suggested that a stop ratio of 1 constraint seems to yield acceptable DDCS dose deviation.

Abstract Purpose: To determine the rate of rib fracture in a cohort of patients with breast cancer treated with proton therapy and enrolled on a prospective registry. In series investigating photon therapy for breast cancer, the rib fracture rate ranges from 0.3-13% Methods: From a prospective database, we identified patients treated with proton therapy for breast cancer at our institution between January 1, 2012 and December 31, 2020. Clinical and dosimetric data was extracted from the electronic medical record. The cumulative incidence method assessed rib fracture rate; the Fine-Gray test statistic assessed prognostic significance select variables. Results: 225 patients were identified, 223 women and 2 men. Median age at the time of radiation was 57.8 years (range, 25 – 87). 26% of patients were black, 69% white and 5% were other races or race not disclosed. 5% were Hispanic. 74% of patients had left-sided breast cancer, 5% bilateral, and 21% right-sided. DEXA scan was normal in 20%, showed osteopenia in 34%, osteoporosis in 6%, and not performed in 40%. 57% of patients received antiendocrine therapy with an aromatase inhibitor. For 16% of patients, the breast +/- internal mammary nodes (IMN) were treated, while 32% underwent proton therapy to treat the breast and comprehensive regional lymphatics. 1% of patients had chest wall +/- IMN treated, while 51% underwent proton therapy to treat the chest wall and comprehensive regional lymphatics. 41% of patients were treated with passive scatter proton therapy (n=92); 52% of these had mastectomy. 58% of patients received treatment with pencil beam scanning (PBS) proton therapy (n=131); 53% of these had mastectomy. A combination of passive scatter and PBS was used for 2 patients (1%). 85% of patients received a boost. Median follow-up was 3.1 years (range, 0.2 – 9.1). 97% of patients had > 12 months of follow-up. The 3 year cumulative incidence of in-field rib fracture was 3.7% (95%CI: 1.6%-7.1%). In total, 8 patients developed in-field rib fractures, one symptomatic and 7 identified incidentally on surveillance imaging, for a 0.4% rate of symptomatic rib fracture. Median time from completion of radiation to identification of rib fracture was 1.8 years (range, 0.4-7.4 years). Rib fractures occurred within 2.2 years from radiotherapy completion for seven of the eight patients who experienced this side effect. Three patients developed rib fractures outside of the radiation field, for a cumulative incidence of out-of-field rib fracture of 0.9% (95%CI: 0.2-3.0%). No variables were associated with rib fracture on univariate analysis. Of those with in-field rib fractures, 3 had low and 3 had normal bone density while 2 had not undergone testing; of those with out-of-field rib fractures, 2 had osteopenia and 1 did not have testing. Conclusions: Although a proton beam has higher biological dose deposition at end-of-range than the assumed constant RBE=1.1, the 3 year rate of any in-field rib fractures in our series remains low at 3.7%, with a 0.4% rate of symptomatic rib fracture. Contouring ribs as an OAR and establishing a dose constraint warrants further investigation as a means to maintaining a low rate of rib fracture with proton therapy. Citation Format: Julie Bradley, Xiaoying Liang, Raymond Mailhot Vega, Chunbo Liu, Eric Brooks, Teena Burchianti, Emma Viviers, Roi Dagan, Oluwadamilola Oladeru, Christopher Morris, Nancy Mendenhall. Incidence of rib fracture following treatment with proton therapy for breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-10-08.

Abstract The Mayo Clinic Florida Integrated Oncology Building will be the home of the first spot-scanning only carbon/proton hybrid therapy system by Hitachi, Ltd. It will provide proton beams up to kinetic energies of 230 MeV and carbon beams up to 430 MeV n −1 for clinical deployment. To provide adequate radiation protection, the Geant4 (v10.6) Monte Carlo toolkit was utilized to quantify the ambient dose equivalent at a 10 mm depth ( H *(10)) for photons and neutrons. To perform accurate calculations of the ambient dose equivalent, three-dimensional computer-aided design files of the entire planned facility were imported into Geant4, as well as certain particle system components such as the bending magnets, fast Faraday cup, and gantry. Particle fluence was scored using 60 cm diameter spheres, which were strategically placed throughout areas of interests. Analytical calculations were performed as first-pass design checks. Major shielding slabs were optimized using Geant4 simulations iteratively, with more than 20 alternative designs evaluated within Geant4. The 430 MeV n −1 carbon beams played the most significant role in concrete thickness Requirements. The primary wall thickness for the carbon fixed beam room is 4 meters. The presence of the proton gantry structure in the simulation caused the ambient dose equivalent to increase by around 67% at the maze entrance, but a decrease in the high energy beam transport corridor. All shielding primary and secondary goals for clinical operations were met per state regulation and national guidelines.

Abstract Objective. To investigate synchrotron-based proton pencil beam scanning (PBS) beam delivery time (BDT) using novel continuous scanning mode. Approach. A BDT calculation model was developed for the Hitachi particle therapy system. The model was validated against the measured BDT of 36 representative clinical proton PBS plans with discrete spot scanning (DSS) in the current Hitachi proton therapy system. BDTs were calculated with the next generation using Mayo Clinic Florida system operating parameters for conventional DSS, and novel dose driven continuous scanning (DDCS). BDTs of DDCS with and without Break Spots were investigated. Main results. For DDCS without Break Spots, the use of Stop Ratio to control the transit dose largely reduced the beam intensity and consequently, severely prolonged the BDT. DDCS with Break Spots was able to maintain a sufficiently high beam intensity while controlling transit dose. In DDCS with Break Spots, tradeoffs were made between beam intensity and number of Break Spots. Therefore, BDT decreased with increased beam intensity but reached a plateau for beam intensity larger than 10 MU s −1 . Averaging over all clinical plans, BDT was reduced by 10% for DDCS with Break Spots compared to DSS. Significance. DDCS with Break Spots reduced BDT. DDCS has the potential to further reduce BDT under the ideal scenario which requests both stable beam intensity extraction and accurately modelling the transit dose. Further investigation is warranted.

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.

AbstractPurposeTo evaluate the relative biological effectiveness (RBE)‐weighted dose to the heart and to estimate RBE uncertainties when assuming a constant RBE of 1.1, for breast cancer patients receiving intensity‐modulated proton therapy (IMPT). Further, to study the impact of RBE uncertainties on the risk of an acute coronary event (ACE).Material and methodsWe analyzed 20 patients who received IMPT to either the left breast (n = 10) or left chest wall (n = 10) and regional lymph nodes. The Monte Carlo simulation engine, MCsquare, was used to simulate the dose‐averaged linear energy transfer (LETd) map. The RBE‐weighted dose to the heart and its substructures was calculated using three different RBE models. The risk of ACE was estimated per its linear relationship with mean heart dose (MHD) as established by Darby et al.ResultsThe median MHD increased from 1.33 GyRBE assuming an RBE of 1.1 to 1.64, 1.87, and 1.99 GyRBE when using the RBE‐weighted dose models. The median values (and ranges) of the excess absolute risk of ACE were 0.4% (0.1%–0.8%) when assuming an RBE of 1.1, and 0.6% (0.2%–1.0%), 0.6% (0.2%–1.1%), and 0.7% (0.2%–1.1%) with the RBE‐weighted models. For our patient cohort, the maximum excess absolute risk of ACE increased by 0.3% with the RBE‐weighted doses compared to the constant RBE of 1.1, reaching an excess absolute ACE risk of 1.1%. The interpatient LETd variation was small for the relevant high‐dose regions of the heart.ConclusionAll three RBE models predicted a higher biological dose compared to the clinical standard dose assuming a constant RBE of 1.1. An underestimation of the biological dose results in underestimation of the ACE risk. Analyzing the voxel‐by‐voxel biological dose and the LET map alongside clinical outcomes is warranted in the development of a more accurate normal‐tissue complication probability model.